Download and Create a Tidy Data Frame of CRU CL v. 2.0 Climatology Variables

This function automates downloading and importing CRU CL v. 2.0 climatology data and creates a tidy data frame of the data. If requested, minimum and maximum temperature may also be automatically calculated as described in the data readme.txt file. Data may be cached for later use by this function, saving time downloading files in future use of the function.

Nomenclature and units from readme.txt:

pre

precipitation (millimetres/month)

rd0

wet-days (number days with >0.1mm rain per month)

tmp

mean temperature (degrees Celsius)

dtr

mean diurnal temperature range (degrees Celsius)

reh

relative humidity (percent)

sunp

sunshine (percent of maximum possible (percent of day length))

frs

ground-frost (number of days with ground-frost per month)

wnd

10 metre windspeed (metres/second)

elv

elevation (automatically converted to metres)

For more information see the description of the data provided by CRU, https://crudata.uea.ac.uk/cru/data/hrg/tmc/readme.txt

get_CRU_df(
  pre = FALSE,
  pre_cv = FALSE,
  rd0 = FALSE,
  tmp = FALSE,
  dtr = FALSE,
  reh = FALSE,
  tmn = FALSE,
  tmx = FALSE,
  sunp = FALSE,
  frs = FALSE,
  wnd = FALSE,
  elv = FALSE,
  cache = FALSE
)

Arguments

pre	Logical. Fetch precipitation (millimetres/month) from server and return in the data frame? Defaults to FALSE.
pre_cv	Logical. Fetch cv of precipitation (percent) from server and return in the data frame? Defaults to FALSE. NOTE. Setting this to TRUE will always results in pre being set to TRUE and returned as well.
rd0	Logical. Fetch wet-days (number days with >0.1millimetres rain per month) and return in the data frame? Defaults to FALSE.
tmp	Logical. Fetch temperature (degrees Celsius) and return it in the data frame? Defaults to FALSE.
dtr	Logical. Fetch mean diurnal temperature range (degrees Celsius) and return it in the data frame? Defaults to FALSE.
reh	Logical. Fetch relative humidity and return it in the data frame? Defaults to FALSE.
tmn	Logical. Calculate minimum temperature values (degrees Celsius) and return it in the data frame? Defaults to FALSE.
tmx	Logical. Calculate maximum temperature (degrees Celsius) and return it in the data frame? Defaults to FALSE.
sunp	Logical. Fetch sunshine, percent of maximum possible (percent of day length) and return it in data frame? Defaults to FALSE.
frs	Logical. Fetch ground-frost records (number of days with ground- frost per month) and return it in data frame? Defaults to FALSE.
wnd	Logical. Fetch 10m wind speed (metres/second) and return it in the data frame? Defaults to FALSE.
elv	Logical. Fetch elevation (converted to metres) and return it in the data frame? Defaults to FALSE.
cache	Logical. Store CRU CL v. 2.0 data files locally for later use? If FALSE, the downloaded files are removed when R session is closed. To take advantage of cached files in future sessions, use cache = TRUE after the initial download and caching. Defaults to FALSE.

Value

A tidy data frame of CRU CL v. 2.0 climatology elements as a tibble object

Note

This package automatically converts elevation values from kilometres to metres.

See also

create_CRU_stack manage_cache

Author

Adam H. Sparks, adamhsparks@gmail.com

Examples

# \donttest{
# Download data and create a data frame of precipitation and temperature
# without caching the data files
CRU_pre_tmp <- get_CRU_df(pre = TRUE, tmp = TRUE)

head(CRU_pre_tmp)
#> # A tibble: 6 x 5
#>     lat   lon month   pre   tmp
#>   <dbl> <dbl> <fct> <dbl> <dbl>
#> 1 -59.1 -26.6 jan   105.    0.2
#> 2 -58.4 -26.4 jan   107.    0.4
#> 3 -58.4 -26.2 jan   106.    0.6
#> 4 -55.9 -67.2 jan    73.1   8  
#> 5 -55.8 -67.6 jan    71.7   8.4
#> 6 -55.8 -67.4 jan    70.2   8  

library(tibble)
CRU_pre_tmp
#> # A tibble: 6,795,144 x 5
#>      lat   lon month   pre   tmp
#>    <dbl> <dbl> <fct> <dbl> <dbl>
#>  1 -59.1 -26.6 jan   105.    0.2
#>  2 -58.4 -26.4 jan   107.    0.4
#>  3 -58.4 -26.2 jan   106.    0.6
#>  4 -55.9 -67.2 jan    73.1   8  
#>  5 -55.8 -67.6 jan    71.7   8.4
#>  6 -55.8 -67.4 jan    70.2   8  
#>  7 -55.8 -67.2 jan    68.7   8.2
#>  8 -55.6 -68.2 jan    73.8   8.4
#>  9 -55.6 -68.1 jan    71.6   8.2
#> 10 -55.6 -67.6 jan    66.5   8.6
#> # … with 6,795,134 more rows
# }