Tidy meteoland

Victor Granda

library(meteoland)
library(stars)
library(dplyr)

A new way of working with meteoland

With the retirement of rgdal, rgeos and maptools R packages, a complete update of meteoland was necessary to remove the hard dependency meteoland has with sp and raster R packages. Starting with version 2.0.0 of meteoland, any hard dependency on retired packages as well as sp and raster has been removed, and now sf and stars packages are internally used for working with simple features and raster data.

By June 2023, sp and raster packages will be completely removed from the dependency list.

As fundamental changes were required (meteoland meteorology classes were based on sp ones), a decision to improve and make meteorological interpolation process simpler was taken. In this vignette, we provide insights on the new ways of working with meteoland.

If you are interested in the equivalences between older and newer functions of meteoland, please see the appendix at the end of the vignette.

Example datasets

meteoland now ships with new example objects, based on the sf and stars packages. The following table describes the new data examples:

Name Description
meteoland_interpolator_example A meteoland interpolator object, with daily meteorological data in Catalonia (Spain) for April 2022.
meteoland_meteo_example Data from meteorological stations in Catalonia (Spain) for April 2022.
meteoland_meteo_no_topo_example Data from meteorological stations in Catalonia (Spain) for April 2022, without topographical information.
meteoland_topo_example Topographical information for meteorological stations in Catalonia (Spain).
points_to_interpolate_example Topographical information for 15 plots located in Catalonia (Spain).
raster_to_interpolate_example Topographical information for a 0.01 degree grid (10x10 cells) raster located in central Catalonia (Spain)

The new interpolation process

The interpolation of meteorological information requires two kinds of information:

  1. The topographical information of the target locations to interpolate. This includes elevation, aspect and slope. Elevation is the only mandatory topography variable, but interpolation results improve when also aspect and slope are provided in mountain areas.

  2. The reference meteorological information that we will use to build the interpolator object. This information can come from meteorological stations in the area we are interested on or, alternatively, can be extracted from available rasters with meteorological variables.

Topographical information

In order to interpolate, we need our locations in a format that meteoland can understand. For point locations, this is a sf object including the elevation (in m.a.s.l.), slope (in degrees) and aspect (in degrees) variables, such as:

points_to_interpolate_example
#> Simple feature collection with 15 features and 4 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 × 5
#>    plot_id elevation slope aspect             geometry
#>    <chr>       <dbl> <dbl>  <dbl>          <POINT [°]>
#>  1 P_05284      889. 25.2   313.   (2.320167 42.24139)
#>  2 P_06572      680. 18.0    79.9  (2.552233 42.02596)
#>  3 P_07201      141.  4.17   52.9  (2.721874 41.88258)
#>  4 P_07512      254. 14.3   261.     (2.98657 41.9006)
#>  5 P_08207     1860. 36.4   293.   (2.209903 42.33968)
#>  6 P_08299      183.  4.12   92.9  (2.817143 42.24325)
#>  7 P_09341      819  23.4   128.   (1.126766 42.42612)
#>  8 P_10272      860  34.8   347.   (1.398528 42.26791)
#>  9 P_10861      706  22.4    22.6 (0.9314126 42.04226)
#> 10 P_11651      585  22.0   199.   (0.7578958 41.8612)
#> 11 P_12150      674. 30.3   154.   (1.481719 41.81838)
#> 12 P_12227      752.  6.04   27.7    (1.283161 41.591)
#> 13 P_12417      702  11.6    63.1 (0.8727224 41.35875)
#> 14 P_13007      972.  4.21  338.    (1.120383 42.6336)
#> 15 P_14029      556. 14.1    41.4  (1.480716 41.31541)

For spatially-continuous data (i.e. a raster), we need a stars object including elevation (in m.a.s.l.), slope (in degrees) and aspect (in degrees) as attributes, such as:

raster_to_interpolate_example
#> stars object with 2 dimensions and 3 attributes
#> attribute(s):
#>                  Min.    1st Qu.    Median      Mean   3rd Qu.     Max.
#> elevation  240.000000 370.000000 447.00000 460.32231 525.00000 786.0000
#> slope        1.432096   5.720433  11.34812  13.07343  19.75851  31.0719
#> aspect       5.194427  74.744881 174.36932 181.67923 291.03751 360.0000
#> dimension(s):
#>   from  to  offset    delta refsys x/y
#> x  155 165 0.03648  0.01058 WGS 84 [x]
#> y  110 120   42.92 -0.01058 WGS 84 [y]

Both, sf and stars R packages have the necessary functions to read most spatial formats, the only thing to consider is ensuring that the topographical variables are included, have the proper units and mandatory names (elevation, aspect, slope).

Meteorological information

For interpolating the meteorological variables in our locations (see above), we need a reference meteorological data. This is a sf object with the reference locations, and daily values of the weather variables needed to perform the interpolation, for example:

meteoland_meteo_example
#> Simple feature collection with 5652 features and 18 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 0.30565 ymin: 40.55786 xmax: 3.18165 ymax: 42.77011
#> Geodetic CRS:  WGS 84
#> # A tibble: 5,652 × 19
#>    dates               service stationID station_name station_province elevation
#>  * <dttm>              <chr>   <chr>     <chr>        <chr>                <dbl>
#>  1 2022-04-01 00:00:00 meteoc… C6        Castellnou … Lleida                264 
#>  2 2022-04-01 00:00:00 meteoc… C7        Tàrrega      Lleida                427 
#>  3 2022-04-01 00:00:00 meteoc… C8        Cervera      Lleida                554 
#>  4 2022-04-01 00:00:00 meteoc… C9        Mas de Barb… Tarragona             240 
#>  5 2022-04-01 00:00:00 meteoc… CC        Orís         Barcelona             626 
#>  6 2022-04-01 00:00:00 meteoc… CD        la Seu d'Ur… Lleida                849 
#>  7 2022-04-01 00:00:00 meteoc… CE        els Hostale… Barcelona             316 
#>  8 2022-04-01 00:00:00 meteoc… CG        Molló - Fab… Girona               1405 
#>  9 2022-04-01 00:00:00 meteoc… CI        Sant Pau de… Girona                852 
#> 10 2022-04-01 00:00:00 meteoc… CJ        Organyà      Lleida                566.
#> # ℹ 5,642 more rows
#> # ℹ 13 more variables: MeanTemperature <dbl>, MinTemperature <dbl>,
#> #   MaxTemperature <dbl>, MeanRelativeHumidity <dbl>,
#> #   MinRelativeHumidity <dbl>, MaxRelativeHumidity <dbl>, Precipitation <dbl>,
#> #   WindDirection <dbl>, WindSpeed <dbl>, Radiation <dbl>, geom <POINT [°]>,
#> #   aspect <dbl>, slope <dbl>

meteoland expects variable names to be as indicated in the example:

names(meteoland_meteo_example)
#>  [1] "dates"                "service"              "stationID"           
#>  [4] "station_name"         "station_province"     "elevation"           
#>  [7] "MeanTemperature"      "MinTemperature"       "MaxTemperature"      
#> [10] "MeanRelativeHumidity" "MinRelativeHumidity"  "MaxRelativeHumidity" 
#> [13] "Precipitation"        "WindDirection"        "WindSpeed"           
#> [16] "Radiation"            "geom"                 "aspect"              
#> [19] "slope"

The only mandatory variables are MinTemperature and MaxTemperature. Other variables (Precipitation, WindSpeed…), when present, allow for a more complete weather interpolation.

For more information on preparing meteorological data for meteoland, see vignette("reshaping-meteo", package = "meteoland")

Quick interpolation (if everything is ok)

With the necessary data in the correct format we can perform the interpolation right away:

# creating the interpolator object
interpolator <- with_meteo(meteoland_meteo_example) |>
  create_meteo_interpolator()
#> ℹ Checking meteorology object...
#> ✔ meteorology object ok
#> ℹ Creating interpolator...
#> Warning: No interpolation parameters provided, using defaults
#> ℹ Set the `params` argument to modify parameter default values
#> • Calculating smoothed variables...
#> • Updating intial_Rp parameter with the actual stations mean distance...
#> ✔ Interpolator created.

# performing the interpolation
points_interpolated <- points_to_interpolate_example |>
  interpolate_data(interpolator)
#> 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...
points_interpolated
#> 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]>

Let’s see this step by step.

meteo_without_temp <- meteoland_meteo_example
meteo_without_temp[["MinTemperature"]] <- NULL
meteo_without_temp[["MaxTemperature"]] <- NULL
with_meteo(meteo_without_temp)
#> ℹ Checking meteorology object...
#> Error: Names found in meteo don't comply with the required names:
#> meteo should have the following meteorology variables:
#>   - MinTemperature ***
#>   - MaxTemperature ***
#>   - Precipitation
#>   - RelativeHumidity
#>   - Radiation
#>   - WindSpeed
#>   - WindDirection
#> 
#>  ***: mandatory variables
# parameters
get_interpolation_params(interpolator)
#> $initial_Rp
#> [1] 1.136444
#> 
#> $iterations
#> [1] 3
#> 
#> $alpha_MinTemperature
#> [1] 3
#> 
#> $alpha_MaxTemperature
#> [1] 3
#> 
#> $alpha_DewTemperature
#> [1] 3
#> 
#> $alpha_PrecipitationEvent
#> [1] 5
#> 
#> $alpha_PrecipitationAmount
#> [1] 5
#> 
#> $alpha_Wind
#> [1] 3
#> 
#> $N_MinTemperature
#> [1] 30
#> 
#> $N_MaxTemperature
#> [1] 30
#> 
#> $N_DewTemperature
#> [1] 30
#> 
#> $N_PrecipitationEvent
#> [1] 5
#> 
#> $N_PrecipitationAmount
#> [1] 20
#> 
#> $N_Wind
#> [1] 2
#> 
#> $St_Precipitation
#> [1] 5
#> 
#> $St_TemperatureRange
#> [1] 15
#> 
#> $pop_crit
#> [1] 0.5
#> 
#> $f_max
#> [1] 0.6
#> 
#> $wind_height
#> [1] 10
#> 
#> $wind_roughness_height
#> [1] 0.001
#> 
#> $penman_albedo
#> [1] 0.25
#> 
#> $penman_windfun
#> [1] "1956"
#> 
#> $debug
#> [1] FALSE
# interpolated meteo for the first location
points_interpolated[["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>

We can “unnest” the results to get the data in a long format (each combination of location and date in a different row):

tidyr::unnest(points_interpolated, 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>

Interpolator object

In older versions of meteoland, the interpolator object inherited the MeteorologyInterpolationData class (based on sp classes). Starting on meteoland v2.0.0, MeteorologyInterpolationData class is deprecated, and the interpolator object created by create_meteo_interpolator() inherits directly from stars class.

class(interpolator)
#> [1] "stars"

This object is a data cube, with reference weather locations and dates as dimensions, and meteorological and topographical variables as attributes.

interpolator
#> stars object with 2 dimensions and 13 attributes
#> attribute(s):
#>                                 Min.    1st Qu.    Median       Mean   3rd Qu.
#> Temperature               -14.200000   8.800000  12.60000  11.324991  14.80000
#> MinTemperature            -15.900000   3.300000   6.90000   5.883189   9.40000
#> MaxTemperature            -13.000000  13.900000  18.30000  17.364292  21.80000
#> RelativeHumidity           18.000000  57.000000  67.00000  67.720000  78.00000
#> Precipitation               0.000000   0.000000   0.00000   1.925434   0.10000
#> Radiation                   7.707484  17.717235  22.04597  20.762628  23.61352
#> WindDirection               0.000000 116.000000 217.00000 196.861908 275.00000
#> WindSpeed                   0.200000   0.900000   1.30000   1.603907   2.00000
#> elevation                   0.000000 147.000000 317.00000 515.629630 668.00000
#> aspect                      0.000000   0.000000   0.00000   0.000000   0.00000
#> slope                       0.000000   0.000000   0.00000   0.000000   0.00000
#> SmoothedPrecipitation       0.100000   1.862500   5.55000   6.197496   9.07500
#> SmoothedTemperatureRange    4.695000   9.863542  11.66307  11.400932  13.26868
#>                                 Max. NA's
#> Temperature                 23.40000  148
#> MinTemperature              20.10000  138
#> MaxTemperature              29.90000  139
#> RelativeHumidity           100.00000  145
#> Precipitation              160.90000   79
#> Radiation                   28.10878  139
#> WindDirection              359.00000 4171
#> WindSpeed                    6.90000 4160
#> elevation                 2535.00000    0
#> aspect                       0.00000    0
#> slope                        0.00000    0
#> SmoothedPrecipitation       65.10000  818
#> SmoothedTemperatureRange    17.72778  124
#> dimension(s):
#>         from  to         offset  delta  refsys point
#> date       1  30 2022-04-01 UTC 1 days POSIXct FALSE
#> station    1 189             NA     NA  WGS 84  TRUE
#>                                                       values
#> date                                                    NULL
#> station POINT (0.95172 41.6566),...,POINT (1.89716 42.32211)

The object also contains the interpolation parameters as an attribute, that can be accessed with get_interpolation_params().

get_interpolation_params(interpolator)
#> $initial_Rp
#> [1] 1.136444
#> 
#> $iterations
#> [1] 3
#> 
#> $alpha_MinTemperature
#> [1] 3
#> 
#> $alpha_MaxTemperature
#> [1] 3
#> 
#> $alpha_DewTemperature
#> [1] 3
#> 
#> $alpha_PrecipitationEvent
#> [1] 5
#> 
#> $alpha_PrecipitationAmount
#> [1] 5
#> 
#> $alpha_Wind
#> [1] 3
#> 
#> $N_MinTemperature
#> [1] 30
#> 
#> $N_MaxTemperature
#> [1] 30
#> 
#> $N_DewTemperature
#> [1] 30
#> 
#> $N_PrecipitationEvent
#> [1] 5
#> 
#> $N_PrecipitationAmount
#> [1] 20
#> 
#> $N_Wind
#> [1] 2
#> 
#> $St_Precipitation
#> [1] 5
#> 
#> $St_TemperatureRange
#> [1] 15
#> 
#> $pop_crit
#> [1] 0.5
#> 
#> $f_max
#> [1] 0.6
#> 
#> $wind_height
#> [1] 10
#> 
#> $wind_roughness_height
#> [1] 0.001
#> 
#> $penman_albedo
#> [1] 0.25
#> 
#> $penman_windfun
#> [1] "1956"
#> 
#> $debug
#> [1] FALSE

Interpolation parameters can also be changed with set_interpolation_params().

# wind_height parameter
get_interpolation_params(interpolator)$wind_height
#> [1] 10

# set a new wind_height parameter and check
interpolator <- set_interpolation_params(interpolator, params = list(wind_height = 5))
#> ℹ Some interpolation parameters are missing, using default values for those
get_interpolation_params(interpolator)$wind_height
#> [1] 5

Writing and reading interpolator objects

Interpolator objects can be reused for different interpolations exercises within the area covered by the interpolator. To allow interpolator objects to be shared between sessions, meteoland offers functions to write and read these objects. The interpolator is saved in NetCDF-CF format (https://cfconventions.org/cf-conventions/cf-conventions.html) and can be also opened with any GIS software that supports NetCDF-CF.

temporal_folder <- tempdir()
write_interpolator(interpolator, file.path(temporal_folder, "interpolator.nc"))
#> ℹ Creating nc file following the NetCDF-CF conventions <https://cfconventions.org/cf-conventions/cf-conventions.html>
#> ℹ Adding spatial info to nc file
#> ✔ Done
# file should exists now
file.exists(file.path(temporal_folder, "interpolator.nc"))
#> [1] TRUE

To load the interpolator in your session again, you can use the read_interpolator() function.

file_interpolator <- read_interpolator(file.path(temporal_folder, "interpolator.nc"))
# the read interpolator should be identical to the one we have already
identical(file_interpolator, interpolator)
#> [1] TRUE

Interpolator calibration

Interpolation parameters can be calibrated for individual variables before performing the interpolation process. In fact, it’s recommended to calibrate the interpolator object before using it, as the default interpolation parameters can be not adequate for the studied area. meteoland offers a calibration process with the interpolator_calibration() function.

**Important!** Calibration process for one variable can take a long time to finish,
as it performs a *leave-one-out* interpolation for all stations present in the
interpolator and all combinations of N and alpha sequences provided. In this example
we reduce the N and alpha test values for the process to be faster, but is recommended
to explore a wider range of these values.
# min temperature N and alpha before calibration
get_interpolation_params(interpolator)$N_MinTemperature
#> [1] 30
get_interpolation_params(interpolator)$alpha_MinTemperature
#> [1] 3

# calibration
interpolator <- interpolator_calibration(
  interpolator,
  variable = "MinTemperature",
  N_seq = c(5, 20),
  alpha_seq = c(1, 10),
  update_interpolation_params = TRUE
)
#> ℹ Total number of stations: 189
#> ℹ Number of stations with available data: 185
#> ℹ Number of stations used for MAE calculation: 185
#> ℹ Number of parameters combinations to test: 4
#> ℹ Starting evaluation of parameter combinations for "MinTemperature"...
#> • Evaluating N: 5, alpha: 1...
#> • Evaluating N: 5, alpha: 10...
#> • Evaluating N: 20, alpha: 1...
#> • Evaluating N: 20, alpha: 10...
#> ✔ Calibration done: Minimum MAE: 1.18956628899488; N: 20; alpha: 10

# parameters after calibration
get_interpolation_params(interpolator)$N_MinTemperature
#> [1] 20
get_interpolation_params(interpolator)$alpha_MinTemperature
#> [1] 10

One advantage of the new data flows in meteoland is that we can pipe the creation and the calibration of the interpolator, as well as the writing:

interpolator <- with_meteo(meteoland_meteo_example) |>
  create_meteo_interpolator() |>
  interpolator_calibration(
    variable = "MinTemperature",
    N_seq = c(5, 20),
    alpha_seq = c(1, 10),
    update_interpolation_params = TRUE
  ) |>
  interpolator_calibration(
    variable = "MaxTemperature",
    N_seq = c(5, 20),
    alpha_seq = c(1, 10),
    update_interpolation_params = TRUE
  ) |>
  interpolator_calibration(
    variable = "DewTemperature",
    N_seq = c(5, 20),
    alpha_seq = c(1, 10),
    update_interpolation_params = TRUE
  ) |>
  write_interpolator(
    filename = file.path(temporal_folder, "interpolator.nc"),
    .overwrite = TRUE
  )
#> ℹ Checking meteorology object...
#> ✔ meteorology object ok
#> ℹ Creating interpolator...
#> Warning: No interpolation parameters provided, using defaults
#> ℹ Set the `params` argument to modify parameter default values
#> • Calculating smoothed variables...
#> • Updating intial_Rp parameter with the actual stations mean distance...
#> ✔ Interpolator created.
#> ℹ Total number of stations: 189
#> ℹ Number of stations with available data: 185
#> ℹ Number of stations used for MAE calculation: 185
#> ℹ Number of parameters combinations to test: 4
#> ℹ Starting evaluation of parameter combinations for "MinTemperature"...
#> • Evaluating N: 5, alpha: 1...
#> • Evaluating N: 5, alpha: 10...
#> • Evaluating N: 20, alpha: 1...
#> • Evaluating N: 20, alpha: 10...
#> ✔ Calibration done: Minimum MAE: 1.18956628899488; N: 20; alpha: 10
#> ℹ Total number of stations: 189
#> ℹ Number of stations with available data: 185
#> ℹ Number of stations used for MAE calculation: 185
#> ℹ Number of parameters combinations to test: 4
#> ℹ Starting evaluation of parameter combinations for "MaxTemperature"...
#> • Evaluating N: 5, alpha: 1...
#> • Evaluating N: 5, alpha: 10...
#> • Evaluating N: 20, alpha: 1...
#> • Evaluating N: 20, alpha: 10...
#> ✔ Calibration done: Minimum MAE: 0.849019890784243; N: 20; alpha: 10
#> ℹ Total number of stations: 189
#> ℹ Number of stations with available data: 185
#> ℹ Number of stations used for MAE calculation: 185
#> ℹ Number of parameters combinations to test: 4
#> ℹ Starting evaluation of parameter combinations for "DewTemperature"...
#> • Evaluating N: 5, alpha: 1...
#> • Evaluating N: 5, alpha: 10...
#> • Evaluating N: 20, alpha: 1...
#> • Evaluating N: 20, alpha: 10...
#> ✔ Calibration done: Minimum MAE: 1.37971537117584; N: 20; alpha: 10
#> ℹ Creating nc file following the NetCDF-CF conventions <https://cfconventions.org/cf-conventions/cf-conventions.html>
#> ℹ Adding spatial info to nc file
#> ✔ Done

This way we can create and calibrate the interpolator once, and using it in future sessions, avoiding the time consuming step of calibrating every time.

Interpolation validation

The interpolation process can be cross validated. meteoland offers the possibility with the interpolation_cross_validation() function. This function takes an interpolator object and calculates different error measures.

cross_validation <- interpolation_cross_validation(interpolator, verbose = FALSE)
cross_validation$errors
#> # A tibble: 5,670 × 21
#>    dates               station stationID MinTemperature_error
#>    <dttm>                <int> <chr>                    <dbl>
#>  1 2022-04-01 00:00:00       1 C6                      0.125 
#>  2 2022-04-02 00:00:00       1 C6                      0.0598
#>  3 2022-04-03 00:00:00       1 C6                      0.145 
#>  4 2022-04-04 00:00:00       1 C6                     -0.306 
#>  5 2022-04-05 00:00:00       1 C6                     -0.0977
#>  6 2022-04-06 00:00:00       1 C6                     -0.248 
#>  7 2022-04-07 00:00:00       1 C6                     -0.457 
#>  8 2022-04-08 00:00:00       1 C6                      1.24  
#>  9 2022-04-09 00:00:00       1 C6                     -0.546 
#> 10 2022-04-10 00:00:00       1 C6                      0.601 
#> # ℹ 5,660 more rows
#> # ℹ 17 more variables: MaxTemperature_error <dbl>,
#> #   RangeTemperature_error <dbl>, RelativeHumidity_error <dbl>,
#> #   Radiation_error <dbl>, Precipitation_error <dbl>,
#> #   MinTemperature_predicted <dbl>, MaxTemperature_predicted <dbl>,
#> #   RangeTemperature_predicted <dbl>, RelativeHumidity_predicted <dbl>,
#> #   Radiation_predicted <dbl>, Precipitation_predicted <dbl>, …
cross_validation$station_stats
#> # A tibble: 189 × 20
#>    station stationID MinTemperature_station_bias MaxTemperature_station_bias
#>      <int> <chr>                           <dbl>                       <dbl>
#>  1       1 C6                            -0.109                     -0.606  
#>  2       2 C7                            -1.03                      -0.582  
#>  3       3 C8                             0.0180                     0.0356 
#>  4       4 C9                            -0.316                      0.657  
#>  5       5 CC                            -0.0373                    -0.483  
#>  6       6 CD                             0.508                     -1.03   
#>  7       7 CE                            -1.62                      -0.779  
#>  8       8 CG                            -1.51                      -0.379  
#>  9       9 CI                             0.888                      0.509  
#> 10      10 CJ                             0.572                      0.00525
#> # ℹ 179 more rows
#> # ℹ 16 more variables: RangeTemperature_station_bias <dbl>,
#> #   RelativeHumidity_station_bias <dbl>, Radiation_station_bias <dbl>,
#> #   MinTemperature_station_mae <dbl>, MaxTemperature_station_mae <dbl>,
#> #   RangeTemperature_station_mae <dbl>, RelativeHumidity_station_mae <dbl>,
#> #   Radiation_station_mae <dbl>, TotalPrecipitation_station_observed <dbl>,
#> #   TotalPrecipitation_station_predicted <dbl>, …
cross_validation$dates_stats
#> # A tibble: 30 × 19
#>    dates               MinTemperature_date_bias MaxTemperature_date_bias
#>    <dttm>                                 <dbl>                    <dbl>
#>  1 2022-04-01 00:00:00                   0.0125                   0.0405
#>  2 2022-04-02 00:00:00                   0.0329                   0.0972
#>  3 2022-04-03 00:00:00                  -0.116                    0.0325
#>  4 2022-04-04 00:00:00                  -0.142                    0.0400
#>  5 2022-04-05 00:00:00                  -0.185                    0.0786
#>  6 2022-04-06 00:00:00                  -0.0303                   0.0688
#>  7 2022-04-07 00:00:00                  -0.156                    0.107 
#>  8 2022-04-08 00:00:00                  -0.0582                   0.0600
#>  9 2022-04-09 00:00:00                   0.0801                   0.0474
#> 10 2022-04-10 00:00:00                  -0.101                    0.0761
#> # ℹ 20 more rows
#> # ℹ 16 more variables: RangeTemperature_date_bias <dbl>,
#> #   RelativeHumidity_date_bias <dbl>, Radiation_date_bias <dbl>,
#> #   MinTemperature_date_mae <dbl>, MaxTemperature_date_mae <dbl>,
#> #   RangeTemperature_date_mae <dbl>, RelativeHumidity_date_mae <dbl>,
#> #   Radiation_date_mae <dbl>, TotalPrecipitation_date_observed <dbl>,
#> #   TotalPrecipitation_date_predicted <dbl>, …
cross_validation$r2
#> $MinTemperature
#> [1] 0.9388959
#> 
#> $MaxTemperature
#> [1] 0.9788855
#> 
#> $RangeTemperature
#> [1] 0.8835377
#> 
#> $RelativeHumidity
#> [1] 0.8096727
#> 
#> $Radiation
#> [1] 0.8911309

Interpolation utils

meteoland also offers some utilities to work with the interpolated data.

Temporal summary of interpolated data

meteoland works at the daily scale. But sometimes the data needs to be aggregated into bigger temporal scales (monthly, quarterly, yearly…). This can be done with the summarise_interpolated_data() function. This function takes the result of interpolate_data and creates summaries in the desired frequency.
The function returns the same interpolated data given as input, but with the weekly summary in an additional column.

summarise_interpolated_data(
  points_interpolated,
  fun = "mean",
  frequency = "week"
)
#> 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: weekly_mean <list>

Calculating rainfall erosivity

meteoland also offers the possibility of calculating the rainfall erosivity value, with the precipitation_rainfall_erosivity() function. This can be used for individual locations:

precipitation_rainfall_erosivity(
  points_interpolated$interpolated_data[[1]],
  longitude = sf::st_coordinates(points_interpolated$geometry[[1]])[,1],
  scale = 'month'
)
#>        4 
#> 48.02896

But also for all locations in the results obtained from the call to interpolate_data():

points_interpolated |>
  mutate(erosivity = precipitation_rainfall_erosivity(
    interpolated_data,
    longitude = sf::st_coordinates(geometry)[,1],
    scale = 'month'
  ))
#> 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: erosivity <list>

Piping all together

meteoland new data flows also allows for piping all processes:

points_interpolated <- points_to_interpolate_example |>
  interpolate_data(interpolator) |>
  summarise_interpolated_data(
    fun = "mean",
    frequency = "week"
  ) |>
  summarise_interpolated_data(
    fun = "max",
    frequency = "month"
  ) |>
  mutate(
    monthly_erosivity = precipitation_rainfall_erosivity(
      interpolated_data,
      longitude = sf::st_coordinates(geometry)[,1],
      scale = 'month'
    )
  )
#> 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...

points_interpolated
#> Simple feature collection with 15 features and 8 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 × 9
#>    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 3 more variables: weekly_mean <list>, monthly_max <list>,
#> #   monthly_erosivity <list>

Interpolation on raster data

We can use the interpolate_data() function in a raster type of data. All we need in this case is the topography information in a stars object, with elevation, aspect and slope variables as raster attributes:

raster_to_interpolate_example
#> stars object with 2 dimensions and 3 attributes
#> attribute(s):
#>                  Min.    1st Qu.    Median      Mean   3rd Qu.     Max.
#> elevation  240.000000 370.000000 447.00000 460.32231 525.00000 786.0000
#> slope        1.432096   5.720433  11.34812  13.07343  19.75851  31.0719
#> aspect       5.194427  74.744881 174.36932 181.67923 291.03751 360.0000
#> dimension(s):
#>   from  to  offset    delta refsys x/y
#> x  155 165 0.03648  0.01058 WGS 84 [x]
#> y  110 120   42.92 -0.01058 WGS 84 [y]

In this case, the raster is a 0.01 degree grid (10x10 cells) in central Catalonia. As the raster is inside the area covered by the interpolator object we created before, we will use it.

raster_interpolated <- raster_to_interpolate_example |>
  interpolate_data(interpolator)
#> 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...
#> ℹ Binding together interpolation results
#> ✔ Interpolation process finished

raster_interpolated
#> stars object with 3 dimensions and 14 attributes
#> attribute(s):
#>                               Min.     1st Qu.     Median       Mean
#> MeanTemperature         3.28710017  11.4127317  13.905175  13.419200
#> MinTemperature         -4.21944950   3.9207208   6.575382   5.306937
#> MaxTemperature          6.96679206  14.9159502  19.225056  18.693510
#> Precipitation           0.00000000   0.0000000   0.000000   1.217155
#> MeanRelativeHumidity   32.89385498  58.0844689  64.649229  65.957527
#> MinRelativeHumidity    25.19625829  38.4317461  44.433889  47.943580
#> MaxRelativeHumidity    49.75939189 100.0000000 100.000000  97.027820
#> Radiation               7.38449673  16.1504318  20.815039  19.742938
#> WindSpeed               0.02164994   0.9119314   1.252240   1.385929
#> WindDirection           0.18621266  71.4293342 198.955399 181.615512
#> PET                     1.09428184   2.4611166   3.196850   3.110504
#> elevation             240.00000000 370.0000000 447.000000 460.322314
#> slope                   1.43209624   5.7204332  11.348120  13.073426
#> aspect                  5.19442749  74.7448807 174.369324 181.679232
#>                           3rd Qu.       Max. NA's
#> MeanTemperature        16.0215966  20.225457    0
#> MinTemperature          7.6128811  11.721902    0
#> MaxTemperature         22.3273620  29.104431    0
#> Precipitation           0.2922964  21.000409    0
#> MeanRelativeHumidity   75.6627580 100.000000    0
#> MinRelativeHumidity    55.1586632  91.663163    0
#> MaxRelativeHumidity   100.0000000 100.000000    0
#> Radiation              23.8148915  28.126824    0
#> WindSpeed               1.7444530   5.811866   30
#> WindDirection         264.0404014 359.908196 1350
#> PET                     3.7822172   5.598250    0
#> elevation             525.0000000 786.000000    0
#> slope                  19.7585106  31.071896    0
#> aspect                291.0375061 360.000000    0
#> dimension(s):
#>      from to         offset    delta  refsys point x/y
#> x       1 11          1.671  0.01058  WGS 84 FALSE [x]
#> y       1 11          41.76 -0.01058  WGS 84 FALSE [y]
#> date    1 30 2022-04-01 UTC   1 days POSIXct FALSE

As we can see, the returned object is the same stars raster provided to the function, with the interpolated meteorological variables as new attributes. Dimensions now include the date, besides the input’s latitude and longitude.

Temporal aggregation in raster

Like with the point location example, interpolated data in raster format can also be aggregated temporally.

summarise_interpolated_data(
  raster_interpolated,
  fun = "mean",
  frequency = "week"
)
#> stars object with 3 dimensions and 11 attributes
#> attribute(s):
#>                              Min.      1st Qu.     Median       Mean
#> MeanTemperature         3.8085157  11.46543487  13.276586  12.702695
#> MinTemperature         -2.1490631   2.77153554   6.299540   4.640385
#> MaxTemperature          7.6274425  16.18746065  18.457836  17.944528
#> Precipitation           0.0000000   0.04039901   0.372891   1.068806
#> MeanRelativeHumidity   42.9062182  60.53136706  64.595298  63.904152
#> MinRelativeHumidity    29.2592761  40.80836041  45.249846  46.333339
#> MaxRelativeHumidity    75.1187263  96.67327485  99.376895  95.009647
#> Radiation              11.7245475  17.90842114  19.929736  19.765548
#> WindSpeed               0.6861051   0.87245304   1.343451   1.515442
#> WindDirection         122.7681189 152.95041467 168.597605 184.990642
#> PET                     1.6176152   2.74501755   3.084015   3.091572
#>                           3rd Qu.       Max. NA's
#> MeanTemperature        15.8981470  17.107689    0
#> MinTemperature          7.0401778   8.721007    0
#> MaxTemperature         21.3502767  24.825805    0
#> Precipitation           0.9568111   4.916280    0
#> MeanRelativeHumidity   69.2639299  88.634437    0
#> MinRelativeHumidity    52.5994550  72.780577    0
#> MaxRelativeHumidity   100.0000000 100.000000    0
#> Radiation              21.8800875  25.287348    0
#> WindSpeed               1.8527502   4.348124    5
#> WindDirection         234.5482210 246.486142  365
#> PET                     3.4728443   5.069591    0
#> dimension(s):
#>      from to          offset    delta  refsys point x/y
#> time    1  5 2022-03-28 CEST   7 days POSIXct    NA    
#> x       1 11           1.671  0.01058  WGS 84 FALSE [x]
#> y       1 11           41.76 -0.01058  WGS 84 FALSE [y]

In this case the result is the aggregated variables as attributes, and the time dimension is aggregated in the desired frequency.

Piping raster interpolation

As with points, raster interpolation and utilities can also be piped. This way, if we are only interested in the mean monthly temperature in our study area, we can do:

monthly_mean_temperature <- raster_to_interpolate_example |>
  interpolate_data(interpolator, variables = "Temperature") |>
  summarise_interpolated_data(
    fun = "max",
    frequency = "month",
    variable = "MeanTemperature"
  )
#> ℹ Starting interpolation...
#> • Interpolating temperature...
#> ✔ Interpolation done...
#> ℹ Binding together interpolation results
#> ✔ Interpolation process finished

plot(monthly_mean_temperature)

Appendix

Equivalence table

Function (< 2.0.0) Equivalence (>= 2.0.0) deprecated
averagearea No equivalence, aggregating by area can be done with the sf package TRUE
correctionpoint, correctionpoints, correctionpoints.errors, correction_series, defaultCorrectionParams No equivalence, better bias correction methods are provided by other packages (see package MBC for example) TRUE
defaultInterpolationParams No change FALSE
download_* functions No equivalence, weather download functions are now provided by meteospain package TRUE
extractdates, extractgridindex, extractgridpoints, extractNetCDF, extractvars No equivalence, not needed as the meteo objects are now sf objects TRUE
humidity_* conversion tools No change FALSE
interpolation.calibration interpolator_calibration TRUE
interpolation.calibration.fmax interpolator_calibration TRUE
interpolation.coverage No equivalence TRUE
interpolation.cv interpolation_cross_validation TRUE
interpolationgrid, interpolationpixels, interpolationpoints interpolate_data TRUE
mergegrid, mergepoints No equivalence, meteorological objects are now sf objects and can be merged, joined or filtered as any data.frame TRUE
meteocomplete complete_meteo TRUE
meteoplot No equivalence, meteo objects are now sf objects and can be plotted as any other data.frame TRUE
Meteorology_*_Data No equivalence, spatial classes based on sp are now deprecated in meteoland TRUE
penman, penmanmonteith No changes FALSE
plot.interpolation.cv No equivalence TRUE
precipitation_concentration No equivalence, precipitation_concentration utility is deprecated and will be removed in future versions TRUE
precipitation_rainfallErosivity precipitation_rainfall_erosivity TRUE
radiation_* utility functions No change FALSE
readmeteorology* No equivalence, spatial classes based on sp are now deprecated in meteoland TRUE
readNetCDF* No equivalence, NetCDF files can be managed with more recent and up to date R packages (ncmeta, stars…) TRUE
readWindNinjaWindFields No equivalence TRUE
reshapemeteospain meteospain2meteoland TRUE
reshapeworldmet worldmet2meteoland TRUE
reshapeweathercan No equivalence, weathercan package was removed from CRAN and the functions are deprecated TRUE
Spatial**Meteorology, Spatial**Topography No equivalence, spatial classes based on sp are now deprecated in meteoland TRUE
summary* summarise_interpolate_data, summarise_interpolator TRUE
utils_* No change FALSE
weathergeneration, defaultGenerationParams No equivalence, current weather generation methods are currently deprecated because they operate with classes that are deprecated themselves, but for future versions, we plan to keep the functionality in new functions. TRUE
writemeteorology* No equivalence, spatial classes based on sp are now deprecated in meteoland TRUE