clim_dist_seasonal: Compute seasonal climate data spatial distributions
In leonawicz/snapprep: Prepare And Curate Useful Data Sets From Raw SNAP Data

Description Usage Arguments Details Examples

Compute seasonal climate data spatial probability distributions.

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clim_dist_seasonal(in_dir = snapdef()$ar5dir_dist_monthly,
  out_dir = snapdef()$ar5dir_dist_seasonal, variable, rcp,
  density.args = list(n = 200, adjust = 0.1), mc.cores = 32)

`in_dir`	input directory, e.g., `snapdef()$ar5dir_dist_monthly`.
`out_dir`	output directory, e.g., `snapdef()$ar5dir_dist_seasonal`
`variable`	character, optional, to split into smaller file batches. See details.
`rcp`	character, optional, to split into smaller file batches. See details.
`density.args`	arguments list passed to `density`.
`mc.cores`	number of CPUs when processing years in parallel. Defaults to 32 assuming Atlas compute node context.

Seasons are DJF, MAM, JJA and SON 3-month averages. A fifth "season" of full annual averages is also included. For efficiency, this function operates on outputs from clim_dist_monthly. It does not need to redundantly access source downscaled geotiffs.

Use variable to optionally specify a climate variable file identifier: "pr", "tas", "tasmin" or "tasmax". This will be used for pattern matching when listing files inside in_dir. Similarly, use rcp to process a smaller batch. These are helpful when there are many files, such that there could be RAM or time limitations.

## Not run: 
clim_dist_seasonal() # all variables
clim_dist_seasonal(variable = "pr") # precipitation
clim_dist_seasonal(variable = "pr", rcp = "rcp60") # precipitation and RCP 6.0

## End(Not run)