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[Experimental]

Summarises the interpolated meteorology in one or more locations by the desired temporal scale

Usage

summarise_interpolated_data(
  interpolated_data,
  fun = "mean",
  frequency = NULL,
  vars_to_summary = c("MeanTemperature", "MinTemperature", "MaxTemperature",
    "Precipitation", "MeanRelativeHumidity", "MinRelativeHumidity",
    "MaxRelativeHumidity", "Radiation", "WindSpeed", "WindDirection", "PET"),
  dates_to_summary = NULL,
  months_to_summary = 1:12,
  verbose = getOption("meteoland_verbosity", TRUE),
  ...
)

Arguments

interpolated_data

An interpolated data object as returned by interpolate_data.

fun

The function to use for summarising the data.

frequency

A string indicating the interval specification (allowed ones are "week", "month", "quarter" and "year"). If NULL (default), aggregation is done in one interval for all the dates present.

vars_to_summary

A character vector with one or more variable names to summarise. By default, all interpolated variables are summarised.

dates_to_summary

A Date object to define the dates to be summarised. If NULL (default), all dates in the interpolated data are processed.

months_to_summary

A numeric vector with the month numbers to subset the data before summarising. (e.g. c(7,8) for July and August). This parameter allows studying particular seasons, when combined with frequency. For example frequency = "years" and months = 6:8 leads to summarizing summer months of each year.

verbose

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)

...

Arguments needed for fun

Value

For a nested interpolated data, the same sf object with a new column with the temporal summaries. For an unnested interpolated data, a data.frame with the summarised meteo variables. For an interpolated raster (stars object), the same raster with the temporal dimension aggregated as desired.

Details

If interpolated_data is a nested interpolated data sf object, as returned by interpolate_data, temporal summary is done for each location present in the interpolated data. If interpolated_data is an unnested interpolated data sf object, temporal summary is done for all locations together. If interpolated_data is a single location data.frame containing the dates and the interpolated variables, temporal summary is done for that location. If interpolated_data is a stars object as returned by interpolate_data, temporal summary is done for all the raster.

Author

Víctor Granda García, CREAF

Examples

# \donttest{
# points interpolation aggregation
points_to_interpolate_example |>
  interpolate_data(meteoland_interpolator_example, verbose = FALSE) |>
  summarise_interpolated_data()
#> Simple feature collection with 15 features and 6 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 × 7
#>    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]>
#> # ℹ 1 more variable: all_mean <list>

# raster interpolation aggregation
raster_to_interpolate_example |>
  interpolate_data(meteoland_interpolator_example, verbose = FALSE) |>
  summarise_interpolated_data()
#> stars object with 3 dimensions and 11 attributes
#> attribute(s):
#>                             Min.   1st Qu.    Median      Mean   3rd Qu.
#> MeanTemperature       11.5704340 13.085384 13.580366 13.471788 14.000217
#> MinTemperature         5.4241252  5.723106  5.834485  5.860040  6.015028
#> MaxTemperature        15.4618660 17.809716 18.593723 18.420680 19.205037
#> Precipitation          1.0384680  1.131101  1.199751  1.217155  1.293790
#> MeanRelativeHumidity  60.7962965 63.361941 65.087755 65.520741 67.043155
#> MinRelativeHumidity   43.3452715 46.019400 48.149531 48.591400 50.195214
#> MaxRelativeHumidity   96.2942503 96.710474 96.899356 96.878881 97.036136
#> Radiation             13.9846077 18.365970 20.168242 19.595816 21.198293
#> WindSpeed              0.9485546  1.032881  1.262741  1.385929  1.680294
#> WindDirection                 NA        NA        NA       NaN        NA
#> PET                    1.9918440  2.851873  3.153198  3.087621  3.402888
#>                            Max. NA's
#> MeanTemperature       14.659490    0
#> MinTemperature         6.280687    0
#> MaxTemperature        20.321977    0
#> Precipitation          1.445986    0
#> MeanRelativeHumidity  73.956489    0
#> MinRelativeHumidity   58.061458    0
#> MaxRelativeHumidity   97.482022    0
#> Radiation             22.812720    0
#> WindSpeed              2.382469    1
#> WindDirection                NA  121
#> PET                    4.214379    0
#> dimension(s):
#>      from to offset    delta  refsys point          values x/y
#> time    1  1     NA       NA POSIXct    NA 2022-04-01 CEST    
#> x       1 11  1.671  0.01058  WGS 84 FALSE            NULL [x]
#> y       1 11  41.76 -0.01058  WGS 84 FALSE            NULL [y]
# }