DWD Dataset (Precipitation and Potential Evaporation) Averaged for Berlin
Source:R/dwd_berlin_monthly.R
dwd_berlin_monthly.RdA dataset containing the precipitation and potential diamonds.
Format
A data frame with 3852 rows and 12 variables:
- parameter_name
self-defined name for parameter
- parameter
original DWD parameter name (required for requests)
- file
name of raw data file
- year
year
- month
month
- mean
spatial mean value for month
- sd
spatial standard deviation value for month
- min
spatial minimum value for month
- max
spatial maximum value for month
- n_values
number of 1x1km2 grids used for spatial statistics calculation
- parameter
parameter name
- url
full url to raw data file
Examples
if (FALSE) { # \dontrun{
############################################################################
#### R code used for creation of "dwd_berlin_monthly.rds"
############################################################################
remotes::install_github("kwb-r/kwb.dwd")
library(kwb.impetus)
shape_obj <- kwb.dwd:::get_shape_of_german_region(name = "berlin")
shape_file <- "berlin.shp"
shape_obj %>%
sf::st_as_sf() %>%
sf::write_sf(shape_file)
### Plot to check if Berlin boundaries are plotted correctly.
### Set target CRS
crs_target <- 4326
shape_pt <- sf::st_read(shape_file) %>%
sf::st_transform(crs = crs_target)
basemap <- shape_pt %>%
leaflet::leaflet() %>%
leaflet::addTiles() %>%
leaflet::addProviderTiles(leaflet::providers$CartoDB.Positron) %>%
leaflet::addPolygons(color = "red", fill = FALSE)
basemap
yearmonth_start <- "188101"
yearmonth_end <- "202208"
kwb.dwd:::list_monthly_grids_germany_asc_gz("x")
dwd_monthly_vars <- c(#"air temperature (mean)" = "air_temperature_mean"#,
"drought index" = "drought_index",
"evaporation, potential" = "evapo_p",
"evaporation, real" = "evapo_r",
"precipitation" = "precipitation",
"soil moisture" = "soil_moist",
"soil temperature (5 cm)" = "soil_temperature_5cm"
)
system.time(
dwd_berlin_monthly_list <- stats::setNames(lapply(dwd_monthly_vars, function(dwd_var) {
kwb.dwd::read_monthly_data_over_shape(
file = shape_file,
variable = dwd_var,
from = yearmonth_start,
to = yearmonth_end,
quiet = TRUE
)
}), nm = dwd_monthly_vars))
dwd_berlin_monthly <- dplyr::bind_rows(dwd_berlin_monthly_list, .id = "parameter")
dwd_berlin_monthly <- tibble::tibble(parameter_name = names(dwd_monthly_vars),
parameter = as.character(dwd_monthly_vars)) %>%
dplyr::left_join(dwd_berlin_monthly)
usethis::use_data(dwd_berlin_monthly, overwrite = TRUE)
} # }
# Dataset
dwd_berlin_monthly
#> # A tibble: 3,852 × 11
#> parameter_name parameter file date year month mean sd min
#> <chr> <chr> <chr> <date> <int> <int> <dbl> <dbl> <dbl>
#> 1 drought index drought_index grids… 1970-01-01 1970 1 7.32 0.955 5
#> 2 drought index drought_index grids… 1971-01-01 1971 1 2.00 0.115 1
#> 3 drought index drought_index grids… 1972-01-01 1972 1 1.99 0.0938 1
#> 4 drought index drought_index grids… 1973-01-01 1973 1 2.48 0.500 2
#> 5 drought index drought_index grids… 1974-01-01 1974 1 2.02 0.151 2
#> 6 drought index drought_index grids… 1975-01-01 1975 1 2.87 0.332 2
#> 7 drought index drought_index grids… 1976-01-01 1976 1 9.05 0.500 8
#> 8 drought index drought_index grids… 1977-01-01 1977 1 2.23 0.421 2
#> 9 drought index drought_index grids… 1978-01-01 1978 1 1.76 0.428 1
#> 10 drought index drought_index grids… 1979-01-01 1979 1 5.10 0.528 4
#> # ℹ 3,842 more rows
#> # ℹ 2 more variables: max <dbl>, n_values <dbl>
# Covered time period for each parameter
dwd_berlin_monthly %>%
dplyr::group_by(.data$parameter_name,
.data$parameter) %>%
dplyr::summarise(date_min = min(.data$date),
date_max = max(.data$date))
#> `summarise()` has grouped output by 'parameter_name'. You can override using
#> the `.groups` argument.
#> # A tibble: 6 × 4
#> # Groups: parameter_name [6]
#> parameter_name parameter date_min date_max
#> <chr> <chr> <date> <date>
#> 1 drought index drought_index 1970-01-01 2022-08-01
#> 2 evaporation, potential evapo_p 1991-01-01 2022-08-01
#> 3 evaporation, real evapo_r 1991-01-01 2022-08-01
#> 4 precipitation precipitation 1881-01-01 2022-08-01
#> 5 soil moisture soil_moist 1991-01-01 2022-08-01
#> 6 soil temperature (5 cm) soil_temperature_5cm 1991-01-01 2022-08-01