Reshaping meteorological data for meteoland

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Victor Granda

Meteorological data format

For the interpolation of meteorological variables on our target locations, we need meteorological data for a reference set of locations. In meteoland this is a sf object with the spatial coordinates of our reference locations (usually meteorological stations) and daily values of the meteorological variables needed to perform the interpolation, i.e.:

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 names to be as 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 interpolation.

Converting meteorological data to `meteoland` format

Meteorological data can come in many formats and with different names for the same variables. As we saw above, we need to convert it to a meteoland compliant format (sf object with correct names).

Converting from data frames

If we have our meteorological data in a data.frame (i.e. we obtained it from our own weather stations, or download it from other source in this format) we can just simply transform it to the desired format:

unformatted_meteo
#> # A tibble: 15 × 7
#>    date       station latitude longitude min_temp max_temp    rh
#>    <date>     <chr>      <dbl>     <dbl>    <dbl>    <dbl> <dbl>
#>  1 2022-12-01 a           41.4     -0.33     4.25    16.2   25.6
#>  2 2022-12-02 a           41.4     -0.33    14.7     26.1   20  
#>  3 2022-12-03 a           41.4     -0.33    12.9     22.4   47.5
#>  4 2022-12-04 a           41.4     -0.33     2.28    18.8   59.3
#>  5 2022-12-05 a           41.4     -0.33    19.5     29.3   89.9
#>  6 2022-12-01 b           40.1      0.12     6.66    19.1   40.0
#>  7 2022-12-02 b           40.1      0.12    22.7     26.9   70.3
#>  8 2022-12-03 b           40.1      0.12    14.3     26.6   71.2
#>  9 2022-12-04 b           40.1      0.12    13.0     17.5   73.9
#> 10 2022-12-05 b           40.1      0.12     7.76    20.1   72.6
#> 11 2022-12-01 c           42        1.12     4.30    15.0   34.4
#> 12 2022-12-02 c           42        1.12     6.10    17.1  100  
#> 13 2022-12-03 c           42        1.12     9.20    24.4   23.7
#> 14 2022-12-04 c           42        1.12     2.43     8.75  90.8
#> 15 2022-12-05 c           42        1.12     6.01     8.11  69.1

ready_meteo <- unformatted_meteo |>
  # convert names to correct ones
  dplyr::mutate(
    MinTemperature = min_temp,
    MaxTemperature = max_temp,
    MeanRelativeHumidity = rh
  ) |>
  # transform to sf (WGS84)
  sf::st_as_sf(
    coords = c("longitude", "latitude"),
    crs = sf::st_crs(4326)
  )

ready_meteo
#> Simple feature collection with 15 features and 8 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: -0.33 ymin: 40.11 xmax: 1.12 ymax: 42
#> Geodetic CRS:  WGS 84
#> # A tibble: 15 × 9
#>    date       station min_temp max_temp    rh MinTemperature MaxTemperature
#>  * <date>     <chr>      <dbl>    <dbl> <dbl>          <dbl>          <dbl>
#>  1 2022-12-01 a           4.25    16.2   25.6           4.25          16.2 
#>  2 2022-12-02 a          14.7     26.1   20            14.7           26.1 
#>  3 2022-12-03 a          12.9     22.4   47.5          12.9           22.4 
#>  4 2022-12-04 a           2.28    18.8   59.3           2.28          18.8 
#>  5 2022-12-05 a          19.5     29.3   89.9          19.5           29.3 
#>  6 2022-12-01 b           6.66    19.1   40.0           6.66          19.1 
#>  7 2022-12-02 b          22.7     26.9   70.3          22.7           26.9 
#>  8 2022-12-03 b          14.3     26.6   71.2          14.3           26.6 
#>  9 2022-12-04 b          13.0     17.5   73.9          13.0           17.5 
#> 10 2022-12-05 b           7.76    20.1   72.6           7.76          20.1 
#> 11 2022-12-01 c           4.30    15.0   34.4           4.30          15.0 
#> 12 2022-12-02 c           6.10    17.1  100             6.10          17.1 
#> 13 2022-12-03 c           9.20    24.4   23.7           9.20          24.4 
#> 14 2022-12-04 c           2.43     8.75  90.8           2.43           8.75
#> 15 2022-12-05 c           6.01     8.11  69.1           6.01           8.11
#> # ℹ 2 more variables: MeanRelativeHumidity <dbl>, geometry <POINT [°]>

And voilà, we have our meteo data in the correct format

Meteorological data from other R packages

meteoland offers transformation functions for meteorological data downloaded from the meteospain and worldmet R packages.

`meteospain` data

For data coming from meteospain package we have the meteospain2meteoland function that transforming the data for us:

library(meteospain)
get_meteo_from(
  "meteogalicia",
  meteogalicia_options('daily', as.Date("2022-12-01"), as.Date("2022-12-05"))
) |>
  meteospain2meteoland()

`worldmet` data

For data coming from worldmet package we have the worldmet2meteoland function that do the reshaping for us:

library(worldmet)
worldmet::importNOAA("081120-99999", year = 2022) |>
  worldmet2meteoland()

Meteorological data from raster sources

As we have seen, we need the meteorological reference data in a sf object (points). To be able to create an interpolator from a raster, we need to transform the cell values to points (i.e. using the cell center coordinates) for each variable and day and use it to create the interpolator.

So if we have a multi-layered raster with several dates of meteorological data:

> Remember that the raster, besides the meteo data, needs to contain also the topographic
(elevation, aspect and slope) data for the interpolator to work.

raster_meteo_reference
#> stars object with 3 dimensions and 14 attributes
#> attribute(s):
#>                               Min.     1st Qu.     Median       Mean
#> MeanTemperature         3.47453413  11.4535839  13.940206  13.471788
#> MinTemperature         -3.27100714   4.3824425   6.969667   5.860040
#> MaxTemperature          7.16265820  14.7798101  18.901768  18.420680
#> Precipitation           0.00000000   0.0000000   0.000000   1.217155
#> MeanRelativeHumidity   35.08268335  56.7720829  64.269391  65.520741
#> MinRelativeHumidity    26.85783275  38.7257795  45.058448  48.591400
#> MaxRelativeHumidity    53.84205927 100.0000000 100.000000  96.878881
#> Radiation               7.54372475  15.9837124  20.690395  19.595816
#> WindSpeed               0.02164994   0.9119314   1.252240   1.385929
#> WindDirection           0.18621266  71.4293342 198.955399 181.615512
#> PET                     1.09027039   2.4105288   3.176816   3.087621
#> 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        16.1442989  20.781408    0
#> MinTemperature          8.2924049  11.071295    0
#> MaxTemperature         22.0316730  28.801911    0
#> Precipitation           0.2922964  21.000409    0
#> MeanRelativeHumidity   75.5124486 100.000000    0
#> MinRelativeHumidity    55.7307897  90.243329    0
#> MaxRelativeHumidity   100.0000000 100.000000    0
#> Radiation              23.7299248  27.982274    0
#> WindSpeed               1.7444530   5.811866   30
#> WindDirection         264.0404014 359.908196 1350
#> PET                     3.7736818   5.625855    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

we need to convert it to points:

points_meteo_reference <- names(raster_meteo_reference) |>
  # for each variable
  purrr::map(
    # take the variable raster
    ~ raster_meteo_reference[.x] |>
      # convert to sf
      sf::st_as_sf(as_points = TRUE, na.rm = FALSE) |>
      # pivot the data for dates to be in one column
      tidyr::pivot_longer(cols = -geometry, names_to = "dates", values_to = .x) |>
      # convert to tibble to fasten the process
      dplyr::as_tibble() |>
      # convert to date and create stationID
      dplyr::mutate(
        dates = as.Date(dates),
        stationID = as.character(geometry)
      )
  ) |>
  # join all variables
  purrr::reduce(dplyr::left_join) |>
  # create the points sf object
  sf::st_as_sf()
#> Joining with `by = join_by(geometry, dates, stationID)`
#> Joining with `by = join_by(geometry, dates, stationID)`
#> Joining with `by = join_by(geometry, dates, stationID)`
#> Joining with `by = join_by(geometry, dates, stationID)`
#> Joining with `by = join_by(geometry, dates, stationID)`
#> Joining with `by = join_by(geometry, dates, stationID)`
#> Joining with `by = join_by(geometry, dates, stationID)`
#> Joining with `by = join_by(geometry, dates, stationID)`
#> Joining with `by = join_by(geometry, dates, stationID)`
#> Joining with `by = join_by(geometry, dates, stationID)`
#> Joining with `by = join_by(geometry, dates, stationID)`
#> Joining with `by = join_by(geometry, dates, stationID)`
#> Joining with `by = join_by(geometry, dates, stationID)`

points_meteo_reference
#> Simple feature collection with 3630 features and 16 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 1.6762 ymin: 41.65133 xmax: 1.781988 ymax: 41.75712
#> Geodetic CRS:  WGS 84
#> # A tibble: 3,630 × 17
#>             geometry dates      MeanTemperature stationID         MinTemperature
#>          <POINT [°]> <date>               <dbl> <chr>                      <dbl>
#>  1 (1.6762 41.75712) 2022-04-01            6.24 c(1.676199866843…          0.541
#>  2 (1.6762 41.75712) 2022-04-02            6.64 c(1.676199866843…         -1.50 
#>  3 (1.6762 41.75712) 2022-04-03            4.82 c(1.676199866843…         -3.13 
#>  4 (1.6762 41.75712) 2022-04-04            6.53 c(1.676199866843…         -1.59 
#>  5 (1.6762 41.75712) 2022-04-05            9.05 c(1.676199866843…         -1.51 
#>  6 (1.6762 41.75712) 2022-04-06           11.8  c(1.676199866843…          3.70 
#>  7 (1.6762 41.75712) 2022-04-07           14.1  c(1.676199866843…          3.67 
#>  8 (1.6762 41.75712) 2022-04-08           15.1  c(1.676199866843…          7.71 
#>  9 (1.6762 41.75712) 2022-04-09           13.6  c(1.676199866843…          6.79 
#> 10 (1.6762 41.75712) 2022-04-10           11.6  c(1.676199866843…          4.94 
#> # ℹ 3,620 more rows
#> # ℹ 12 more variables: MaxTemperature <dbl>, Precipitation <dbl>,
#> #   MeanRelativeHumidity <dbl>, MinRelativeHumidity <dbl>,
#> #   MaxRelativeHumidity <dbl>, Radiation <dbl>, WindSpeed <dbl>,
#> #   WindDirection <dbl>, PET <dbl>, elevation <dbl>, slope <dbl>, aspect <dbl>

And now we can use it to build an interpolator object:

with_meteo(points_meteo_reference) |>
  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.
#> stars object with 2 dimensions and 13 attributes
#> attribute(s):
#>                                   Min.     1st Qu.     Median       Mean
#> Temperature                 3.47453413  11.4535839  13.940206  13.471788
#> MinTemperature             -3.27100714   4.3824425   6.969667   5.860040
#> MaxTemperature              7.16265820  14.7798101  18.901768  18.420680
#> RelativeHumidity           35.08268335  56.7720829  64.269391  65.520741
#> Precipitation               0.00000000   0.0000000   0.000000   1.217155
#> Radiation                   7.54372475  15.9837124  20.690395  19.595816
#> WindDirection               0.18621266  71.4293342 198.955399 181.615512
#> WindSpeed                   0.02164994   0.9119314   1.252240   1.385929
#> elevation                 240.00000000 370.0000000 447.000000 460.322314
#> aspect                      5.19442749  74.7448807 174.369324 181.679232
#> slope                       1.43209624   5.7204332  11.348120  13.073426
#> SmoothedPrecipitation       0.25336109   1.5179424   3.874980   3.862412
#> SmoothedTemperatureRange    9.41552220  11.8383211  12.587418  12.458785
#>                               3rd Qu.       Max. NA's
#> Temperature                16.1442989  20.781408    0
#> MinTemperature              8.2924049  11.071295    0
#> MaxTemperature             22.0316730  28.801911    0
#> RelativeHumidity           75.5124486 100.000000    0
#> Precipitation               0.2922964  21.000409    0
#> Radiation                  23.7299248  27.982274    0
#> WindDirection             264.0404014 359.908196 1350
#> WindSpeed                   1.7444530   5.811866   30
#> elevation                 525.0000000 786.000000    0
#> aspect                    291.0375061 360.000000    0
#> slope                      19.7585106  31.071896    0
#> SmoothedPrecipitation       6.2789283  11.753856  505
#> SmoothedTemperatureRange   13.2623789  15.014616    0
#> dimension(s):
#>         from  to     offset  delta refsys point
#> date       1  30 2022-04-01 1 days   Date FALSE
#> station    1 121         NA     NA WGS 84  TRUE
#>                                                        values
#> date                                                     NULL
#> station POINT (1.6762 41.65133),...,POINT (1.781988 41.75712)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.

Reshaping meteorological data for meteoland

Victor Granda

Meteorological data format

Converting meteorological data to meteoland format

Converting from data frames

Meteorological data from other R packages

meteospain data

worldmet data

Meteorological data from raster sources

Converting meteorological data to `meteoland` format

`meteospain` data

`worldmet` data