interpolate_data.RdInterpolate spatial data to obtain downscaled meteorologic variables
interpolate_data(
spatial_data,
interpolator,
dates = NULL,
variables = NULL,
ignore_convex_hull_check = FALSE,
verbose = getOption("meteoland_verbosity", TRUE)
)An sf or stars raster object to interpolate
A meteoland interpolator object, as created by
create_meteo_interpolator
vector with dates to interpolate (must be within the interpolator date range). Default to NULL (all dates present in the interpolator object)
vector with variable names to be interpolated. NULL (default), will interpolate all variables. Accepted names are "Temperature", "Precipitation", "RelativeHumidity", "Radiation" and "Wind"
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 ignore_convex_hull_check = TRUE means
that a warning is raised but interpolation is performed, which can be useful to users interpolating
on a few points close but outside of the convex hull.
Logical indicating if the function must show messages and info.
Default value checks "meteoland_verbosity" option and if not set, defaults
to TRUE. It can be turned off for the function with FALSE, or session wide with
options(meteoland_verbosity = FALSE)
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)
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.
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.
# \donttest{
# 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)
#> ℹ Starting interpolation...
#> ℹ Temperature interpolation is needed also...
#> • Interpolating temperature...
#> ℹ Precipitation interpolation is needed also...
#> • Interpolating precipitation...
#> ℹ Relative humidity interpolation is needed also...
#> • Interpolating relative humidity...
#> ℹ Radiation calculation is needed also...
#> • Calculating radiation...
#> ℹ Wind interpolation is needed also...
#> • Interpolating wind...
#> • Calculating PET...
#> ✔ Interpolation done...
# check result
# same class as input data
class(res)
#> [1] "sf" "tbl_df" "tbl" "data.frame"
# data
res
#> Simple feature collection with 15 features and 5 fields
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: 0.7578958 ymin: 41.31541 xmax: 2.98657 ymax: 42.6336
#> Geodetic CRS: WGS 84
#> # A tibble: 15 × 6
#> plot_id elevation slope aspect geometry interpolated_data
#> <chr> <dbl> <dbl> <dbl> <POINT [°]> <list>
#> 1 P_05284 889. 25.2 313. (2.320167 42.24139) <tibble [30 × 13]>
#> 2 P_06572 680. 18.0 79.9 (2.552233 42.02596) <tibble [30 × 13]>
#> 3 P_07201 141. 4.17 52.9 (2.721874 41.88258) <tibble [30 × 13]>
#> 4 P_07512 254. 14.3 261. (2.98657 41.9006) <tibble [30 × 13]>
#> 5 P_08207 1860. 36.4 293. (2.209903 42.33968) <tibble [30 × 13]>
#> 6 P_08299 183. 4.12 92.9 (2.817143 42.24325) <tibble [30 × 13]>
#> 7 P_09341 819 23.4 128. (1.126766 42.42612) <tibble [30 × 13]>
#> 8 P_10272 860 34.8 347. (1.398528 42.26791) <tibble [30 × 13]>
#> 9 P_10861 706 22.4 22.6 (0.9314126 42.04226) <tibble [30 × 13]>
#> 10 P_11651 585 22.0 199. (0.7578958 41.8612) <tibble [30 × 13]>
#> 11 P_12150 674. 30.3 154. (1.481719 41.81838) <tibble [30 × 13]>
#> 12 P_12227 752. 6.04 27.7 (1.283161 41.591) <tibble [30 × 13]>
#> 13 P_12417 702 11.6 63.1 (0.8727224 41.35875) <tibble [30 × 13]>
#> 14 P_13007 972. 4.21 338. (1.120383 42.6336) <tibble [30 × 13]>
#> 15 P_14029 556. 14.1 41.4 (1.480716 41.31541) <tibble [30 × 13]>
# results for the first location
res[["interpolated_data"]][1]
#> [[1]]
#> # A tibble: 30 × 13
#> dates DOY MeanTemperature MinTemperature MaxTemperature
#> <dttm> <dbl> <dbl> <dbl> <dbl>
#> 1 2022-04-01 00:00:00 91 3.24 -2.53 6.99
#> 2 2022-04-02 00:00:00 92 2.99 -4.20 7.67
#> 3 2022-04-03 00:00:00 93 2.39 -5.61 7.59
#> 4 2022-04-04 00:00:00 94 4.02 -3.93 9.20
#> 5 2022-04-05 00:00:00 95 7.38 -3.29 14.3
#> 6 2022-04-06 00:00:00 96 8.78 1.31 13.6
#> 7 2022-04-07 00:00:00 97 12.1 2.44 18.4
#> 8 2022-04-08 00:00:00 98 12.9 3.32 19.1
#> 9 2022-04-09 00:00:00 99 10.0 3.83 14.1
#> 10 2022-04-10 00:00:00 100 11.4 4.85 15.6
#> # ℹ 20 more rows
#> # ℹ 8 more variables: Precipitation <dbl>, MeanRelativeHumidity <dbl>,
#> # MinRelativeHumidity <dbl>, MaxRelativeHumidity <dbl>, Radiation <dbl>,
#> # WindSpeed <dbl>, WindDirection <dbl>, PET <dbl>
#>
# unnest results
tidyr::unnest(res, cols = "interpolated_data")
#> Simple feature collection with 450 features and 17 fields
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: 0.7578958 ymin: 41.31541 xmax: 2.98657 ymax: 42.6336
#> Geodetic CRS: WGS 84
#> # A tibble: 450 × 18
#> plot_id elevation slope aspect geometry dates
#> <chr> <dbl> <dbl> <dbl> <POINT [°]> <dttm>
#> 1 P_05284 889. 25.2 313. (2.320167 42.24139) 2022-04-01 00:00:00
#> 2 P_05284 889. 25.2 313. (2.320167 42.24139) 2022-04-02 00:00:00
#> 3 P_05284 889. 25.2 313. (2.320167 42.24139) 2022-04-03 00:00:00
#> 4 P_05284 889. 25.2 313. (2.320167 42.24139) 2022-04-04 00:00:00
#> 5 P_05284 889. 25.2 313. (2.320167 42.24139) 2022-04-05 00:00:00
#> 6 P_05284 889. 25.2 313. (2.320167 42.24139) 2022-04-06 00:00:00
#> 7 P_05284 889. 25.2 313. (2.320167 42.24139) 2022-04-07 00:00:00
#> 8 P_05284 889. 25.2 313. (2.320167 42.24139) 2022-04-08 00:00:00
#> 9 P_05284 889. 25.2 313. (2.320167 42.24139) 2022-04-09 00:00:00
#> 10 P_05284 889. 25.2 313. (2.320167 42.24139) 2022-04-10 00:00:00
#> # ℹ 440 more rows
#> # ℹ 12 more variables: DOY <dbl>, MeanTemperature <dbl>, MinTemperature <dbl>,
#> # MaxTemperature <dbl>, Precipitation <dbl>, MeanRelativeHumidity <dbl>,
#> # MinRelativeHumidity <dbl>, MaxRelativeHumidity <dbl>, Radiation <dbl>,
#> # WindSpeed <dbl>, WindDirection <dbl>, PET <dbl>
# }