Reshaping meteorological data for meteoland
Victor Granda
Source:vignettes/reshaping-meteo.Rmd
reshaping-meteo.Rmd
library(meteoland)
#> Package 'meteoland' [ver. 2.2.2]
library(stars)
#> Loading required package: abind
#> Loading required package: sf
#> Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 8.2.1; sf_use_s2() is TRUE
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, unionMeteorological 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 1.60 6.01 73.4
#> 2 2022-12-02 a 41.4 -0.33 11.5 20.0 54.4
#> 3 2022-12-03 a 41.4 -0.33 -4.62 5.22 56.1
#> 4 2022-12-04 a 41.4 -0.33 9.97 21.6 90.6
#> 5 2022-12-05 a 41.4 -0.33 13.7 21.0 52.8
#> 6 2022-12-01 b 40.1 0.12 16.9 28.3 46.7
#> 7 2022-12-02 b 40.1 0.12 -0.931 10.2 20
#> 8 2022-12-03 b 40.1 0.12 8.52 14.6 43.0
#> 9 2022-12-04 b 40.1 0.12 8.53 20.7 42.2
#> 10 2022-12-05 b 40.1 0.12 8.30 24.0 76.7
#> 11 2022-12-01 c 42 1.12 6.68 16.4 51.8
#> 12 2022-12-02 c 42 1.12 13.8 21.0 34.0
#> 13 2022-12-03 c 42 1.12 22.4 32.3 48.8
#> 14 2022-12-04 c 42 1.12 0.214 7.73 43.7
#> 15 2022-12-05 c 42 1.12 13.1 18.5 61.1
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 1.60 6.01 73.4 1.60 6.01
#> 2 2022-12-02 a 11.5 20.0 54.4 11.5 20.0
#> 3 2022-12-03 a -4.62 5.22 56.1 -4.62 5.22
#> 4 2022-12-04 a 9.97 21.6 90.6 9.97 21.6
#> 5 2022-12-05 a 13.7 21.0 52.8 13.7 21.0
#> 6 2022-12-01 b 16.9 28.3 46.7 16.9 28.3
#> 7 2022-12-02 b -0.931 10.2 20 -0.931 10.2
#> 8 2022-12-03 b 8.52 14.6 43.0 8.52 14.6
#> 9 2022-12-04 b 8.53 20.7 42.2 8.53 20.7
#> 10 2022-12-05 b 8.30 24.0 76.7 8.30 24.0
#> 11 2022-12-01 c 6.68 16.4 51.8 6.68 16.4
#> 12 2022-12-02 c 13.8 21.0 34.0 13.8 21.0
#> 13 2022-12-03 c 22.4 32.3 48.8 22.4 32.3
#> 14 2022-12-04 c 0.214 7.73 43.7 0.214 7.73
#> 15 2022-12-05 c 13.1 18.5 61.1 13.1 18.5
#> # 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 FALSEwe 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)