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#----------------------------------------------------------------------------
# Author: An-Chi Ho
# Date: 20th May 2021
#
# This script shows how to load irregular grid data by Start(), then regrid it
# by s2dv::CDORemap in the workflow. It is a solution before Start() can deal
# with irregular regridding directly.
#----------------------------------------------------------------------------
library(startR)
path <- paste0('/esarchive/exp/CMIP6/dcppA-hindcast/cmcc-cm2-sr5/cmip6-dcppA-hindcast_i1p1/',
'DCPP/CMCC/CMCC-CM2-SR5/dcppA-hindcast/$member$/Omon/$var$/gn/v20210312/',
'$var$_*_s$sdate$-$member$_gn_$aux$.nc')
data <- Start(dataset = path,
var = 'tos',
sdate = c('1960', '1961'),
aux = 'all',
aux_depends = 'sdate',
j = 'all',
i = 'all',
time = indices(1:12),
member = 'r1i1p1f1',
return_vars = list(j = NULL, i = NULL,
latitude = NULL, longitude = NULL),
retrieve = F)
func_regrid <- function(data) {
lons <- attr(data, 'Variables')$common$longitude
lats <- attr(data, 'Variables')$common$latitude
data <- s2dv::CDORemap(data, lons, lats, grid = 'r360x180',
method = 'bil', crop = FALSE)
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lons_reg <- data[['lons']]
lats_reg <- data[['lats']]
return(list(data = data[[1]], lats = lats_reg, lons = lons_reg))
}
#NOTE: The data transposes if target_dims are only 'j' and 'i'.
# If only 'j' and 'i', output_dims will be 'lat', 'lon'.
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step <- Step(fun = func_regrid,
target_dims = list(data = c('var', 'aux', 'j', 'i')),
output_dims = list(data = c('var', 'aux','lon', 'lat'),
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lats = 'lat', lons = 'lon'),
use_attributes = list(data = "Variables"))
wf <- AddStep(data, step)
res <- Compute(workflow = wf$data,
chunks = list(sdate = 2, time = 2))
names(res)
#[1] "data" "lats" "lons"
dim(res$data)
# lat lon dataset var sdate aux time member
# 180 360 1 1 2 1 12 1
dim(res$lons)
# lon dataset var sdate aux time member
# 360 1 1 2 1 12 1
dim(res$lats)
# lat dataset var sdate aux time member
# 180 1 1 2 1 12 1
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s2dv::PlotEquiMap(drop(res$data)[ , , 1, 1],
lon = drop(res$lons)[, 1, 1],
lat = drop(res$lats)[, 1, 1], fileout = '~/irre.png')
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