Add `hyras_de` data (`daily` / `monthly` / `multi_annual`)

[mrustl]

Actually hyras_de offers these now:
https://opendata.dwd.de/climate_environment/CDC/grids_germany/daily/hyras_de/air_temperature_min/
https://opendata.dwd.de/climate_environment/CDC/grids_germany/daily/hyras_de/air_temperature_mean/
https://opendata.dwd.de/climate_environment/CDC/grids_germany/daily/hyras_de/air_temperature_max/
https://opendata.dwd.de/climate_environment/CDC/grids_germany/daily/hyras_de/humidity/
https://opendata.dwd.de/climate_environment/CDC/grids_germany/daily/hyras_de/precipitation/
https://opendata.dwd.de/climate_environment/CDC/grids_germany/daily/hyras_de/radiation_global/
https://opendata.dwd.de/climate_environment/CDC/grids_germany/monthly/hyras_de/precipitation/
https://opendata.dwd.de/climate_environment/CDC/grids_germany/multi_annual/hyras_de/precipitation/

These were for example used here:

https://github.com/AndreasWunsch/Long-Term-GWL-Simulations

I extended read_daily_data_over_shape() to support all possible variables. The function list_daily_grids_germany_tgz() tells you which variables are available:

> kwb.dwd:::list_daily_grids_germany_tgz("x")
 Error in match.arg(variable, c("evapo_p", "evapo_r", "frost_depth", "soil_moist", : 
'arg' should be one of “evapo_p”, “evapo_r”, “frost_depth”, “soil_moist”, “soil_temperature_5cm”

Unfortunately, precipitation data are not available on a daily basis.

I added a function read_monthly_data_over_shape() that can provide precipitation data as well as further variables. The function list_monthly_grids_germany_asc_gz() informs about possible variables:

> kwb.dwd:::list_monthly_grids_germany_asc_gz(variable = "x")
 Error in match.arg(variable, c("air_temperature_max", "air_temperature_mean", : 
'arg' should be one of “air_temperature_max”, “air_temperature_mean”, “air_temperature_min”, “drought_index”, “evapo_p”, “evapo_r”, “frost_depth”, “precipitation”, “soil_moist”, “soil_temperature_5cm”, “sunshine_duration”

Finally, our package fhpredict has a function to read (and cut) current precipitation data (RADOLAN), see: https://kwb-r.github.io/fhpredict/articles/tutorial.html#spacially-select-and-aggregate-rain-data

Altogether: Too many different functions that do too many different things...

Originally posted by @hsonne in #37 (comment)

[mrustl]

Example for download. File format is netCDF:

url <- "https://opendata.dwd.de/climate_environment/CDC/grids_germany/monthly/hyras_de/precipitation/pr_hyras_1_1931_v3-0_de_monsum.nc"

tfile <- basename(url)
download.file(url, tfile, mode = "wb")

hyras_sf <- sf::read_sf(tfile)
#> Warning in CPL_read_ogr(dsn, layer, query, as.character(options), quiet, :
#> GDAL Message 1: Recode from UTF-8 to CP_ACP failed with the error: "Invalid
#> argument".
hyras_sf
#> # A tibble: 12 × 2
#>     time number_of_station
#>    <dbl>             <dbl>
#>  1   378              881.
#>  2  1086              879.
#>  3  1794              876.
#>  4  2526              879.
#>  5  3258              883.
#>  6  3990              884.
#>  7  4722              883.
#>  8  5466              886.
#>  9  6198              886.
#> 10  6930              885.
#> 11  7662              888.
#> 12  8394              883.
hyras_raster <- raster::raster(tfile)
#> Lade nötigen Namensraum: ncdf4
hyras_raster
#> class      : RasterLayer 
#> band       : 1  (of  12  bands)
#> dimensions : 1100, 1200, 1320000  (nrow, ncol, ncell)
#> resolution : 1000, 1000  (x, y)
#> extent     : 3500000, 4700000, 2100000, 3200000  (xmin, xmax, ymin, ymax)
#> crs        : +proj=lcc +lat_0=52 +lon_0=10 +lat_1=35 +lat_2=65 +x_0=4000000 +y_0=2800000 +ellps=GRS80 +units=m +no_defs 
#> source     : pr_hyras_1_1931_v3-0_de_monsum.nc 
#> names      : Precipitation 
#> z-value    : 1931-01-16 18:00:00 
#> zvar       : pr

^{Created on 2022-09-22 by the reprex package (v2.0.1)}

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