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# Author: Chihchung Chou, An-Chi Ho
# Date: 1st July 2021
# ------------------------------------------------------------------
# This use case uses experimental and the corresponding observational data to calculate
# the temporal mean and spatial weighted mean. Notice that the spatial resolutions of the
# two datasets are different, but it still works because lat and lon are target dimensions.
# ------------------------------------------------------------------
library(startR)
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repos_exp <- paste0('/esarchive/exp/ecearth/a1tr/cmorfiles/CMIP/EC-Earth-Consortium/',
'EC-Earth3/historical/r24i1p1f1/Amon/$var$/gr/v20190312/',
'$var$_Amon_EC-Earth3_historical_r24i1p1f1_gr_$sdate$01-$sdate$12.nc')
exp <- Start(dat = repos_exp,
var = 'tas',
sdate = as.character(c(2005:2008)),
time = indices(1:3),
lat = 'all',
lon = 'all',
synonims = list(lat = c('lat', 'latitude'),
lon = c('lon', 'longitude')),
return_vars = list(lon = NULL,
lat = NULL,
time = 'sdate'),
retrieve = FALSE)
lons_exp <- as.vector(attr(exp, 'Variables')$common$lon)
lats_exp <- as.vector(attr(exp, 'Variables')$common$lat)
dates_exp <- attr(exp, 'Variables')$common$time
attr(exp, 'Dimensions')
# dat var sdate time lat lon
# 1 1 4 3 256 512
dates_exp
# [1] "2005-01-16 12:00:00 UTC" "2006-01-16 12:00:00 UTC"
# [3] "2007-01-16 12:00:00 UTC" "2008-01-16 12:00:00 UTC"
# [5] "2005-02-15 00:00:00 UTC" "2006-02-15 00:00:00 UTC"
# [7] "2007-02-15 00:00:00 UTC" "2008-02-15 12:00:00 UTC"
# [9] "2005-03-16 12:00:00 UTC" "2006-03-16 12:00:00 UTC"
#[11] "2007-03-16 12:00:00 UTC" "2008-03-16 12:00:00 UTC"
# obs
path.obs <- '/esarchive/recon/ecmwf/era5/monthly_mean/$var$_f1h-r1440x721cds/$var$_$date$.nc'
obs <- Start(dat = path.obs,
var = 'tas',
date = unique(format(dates_exp, '%Y%m')),
time = values(dates_exp),
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time_across = 'date',
merge_across_dims = TRUE,
split_multiselected_dims = TRUE,
lat = 'all',
lon = 'all',
synonims = list(lon = c('lon', 'longitude'),
lat = c('lat', 'latitude')),
return_vars = list(lon = NULL,
lat = NULL,
time = 'date'),
retrieve = FALSE)
lons_obs <- as.vector(attr(obs, 'Variables')$common$lon)
lats_obs <- as.vector(attr(obs, 'Variables')$common$lat)
dates_obs <- attr(obs, 'Variables')$common$time
attr(obs, 'Dimensions')
# data var sdate time lat lon
# 1 1 4 3 721 1440
dates_obs
# [1] "2005-01-16 11:30:00 UTC" "2006-01-16 11:30:00 UTC"
# [3] "2007-01-16 11:30:00 UTC" "2008-01-16 11:30:00 UTC"
# [5] "2005-02-14 23:30:00 UTC" "2006-02-14 23:30:00 UTC"
# [7] "2007-02-14 23:30:00 UTC" "2008-02-15 11:30:00 UTC"
# [9] "2005-03-16 11:30:00 UTC" "2006-03-16 11:30:00 UTC"
#[11] "2007-03-16 11:30:00 UTC" "2008-03-16 11:30:00 UTC"
fun <- function(exp, obs,
lons_exp = lons_exp, lats_exp = lats_exp,
lons_obs = lons_obs, lats_obs = lats_obs) {
# exp
e <- s2dv::MeanDims(drop(exp), 'time')
sst.e <- ClimProjDiags::WeightedMean(e, lons_exp, lats_exp,
londim = which(names(dim(e)) == 'lon'),
latdim = which(names(dim(e)) == 'lat'))
index.exp <- (sst.e - mean(sst.e))/sd(sst.e)
# obs
o <- s2dv::MeanDims(drop(obs), 'time')
sst.o <- ClimProjDiags::WeightedMean(o, lons_obs, lats_obs,
londim = which(names(dim(o)) == 'lon'),
latdim = which(names(dim(o)) == 'lat'))
index.obs <- (sst.o - mean(sst.o))/sd(sst.o)
# give dim name
dim(index.exp) <- c(sdate = length(index.exp))
dim(index.obs) <- c(sdate = length(index.obs))
return(list(ind_exp = index.exp, ind_obs = index.obs))
}
# If ClimProjDiags::WeightedMean accepts two-dim input, 'sdate' can be margin dimension.
step <- Step(fun,
target_dims = list(exp = c('sdate', 'time', 'lat', 'lon'),
obs = c('sdate', 'time', 'lat', 'lon')),
output_dims = list(ind_exp = 'sdate', ind_obs = 'sdate'))
workflow <- AddStep(list(exp = exp, obs = obs), step,
lons_exp = lons_exp, lats_exp = lats_exp,
lons_obs = lons_obs, lats_obs = lats_obs)
res <- Compute(workflow$ind_exp,
chunks = list(var = 1))
str(res)
#List of 2
# $ ind_exp: num [1:4, 1, 1] 1.195 0.422 -0.6 -1.017
# $ ind_obs: num [1:4, 1, 1] 0.4642 0.0206 0.9123 -1.3971
# ...
# ...