Extract climate data and compute distributions

Extract climate data and estimate monthly spatial probability distributions.

clim_dist_monthly(inputs, in_dir = snapdef()$ar5dir,
  out_dir = snapdef()$ar5dir_dist_monthly, na.rm = TRUE,
  density.args = list(n = 200, adjust = 0.1), sample.size = 10000,
  verbose = TRUE, overwrite = FALSE, move_akcan = TRUE, mc.cores = 32)

Arguments

inputs

data frame of inputs (one row). See details.
in_dir

input directory, e.g., snapdef()$ar5dir.
out_dir

output directory, e.g., snapdef()$ar5dir_dist_monthly
na.rm

logical, remove NAs.
density.args

arguments list passed to density.
sample.size

numeric, sample size.
verbose

logical, verbose progress.
overwrite

logical, overwrite existing files.
move_akcan

logical, relocate the AK-CAN domain from Political Boundaries subdirectory to top level as its own location group.
mc.cores

number of CPUs when processing years in parallel. Defaults to 32 assuming Atlas compute node context.

Value

invisible, writes files.

Details

inputs generally comes from clim_inputs_table. clim_dist_monthly processes data sets referred to by one row of this data frame at a time. Internally processing uses 32 CPUs on an Atlas compute node. It is expected that the different data sets in the full inputs be processed serially. See example call below.

Examples

# NOT RUN {
purrr::walk(1:nrow(inputs), ~slice(inputs, .x) %>% clim_dist_monthly())
# }