Retrieving multiple models for decadal predictions
Hi @cdelgado,
Thanks for sharing the list and characteristics of decadal predictions. The aim of this issue is to check that Start() can retrieve multiple models in a single call. We know it is possible with other forecasts but given the complexity of decadal prediction storage, this exercise is needed.
Given the number of models
, variables
and frequencies
defined in the table, we may need to set priorities for testing. Do you think, @cdelgado, we can discuss this off-line and report here the outcome?
I provide a code to verify two models can be loaded simultaneously for daily
resolution and tasmin
variable.
Given the number of differences between version
, grid
and member
, the output will be filled with NA values. In order to adjust to user needs, @cdelgado, we can consider the FAQ #8: Define a path with multiple dependencies and the current open issue #61 (closed) about the same topic.
path_list <- list(list(name = 'EC-Earth',
path = '/esarchive/exp/ecearth/a1ua/cmorfiles/DCPP/EC-Earth-Consortium/EC-Earth3/dcppA-hindcast/$member$/day/$var$/$grid$/$version$/$var$_day_EC-Earth3_dcppA-hindcast_s$sdate$-$member$_$grid$_$fyear$.nc'),
list(name = 'HadGEM3',
path = '/esarchive/exp/CMIP6/dcppA-hindcast/hadgem3-gc31-mm/cmip6-dcppA-hindcast_i1p1/DCPP/MOHC/HadGEM3-GC31-MM/dcppA-hindcast/$member$/day/$var$/$grid$/$version$/$var$_day_HadGEM3-GC31-MM_dcppA-hidcast_s$sdate$_$member$_$grid$_$fyear$.nc'))
data <- Start(dataset = path_list,
var = 'tasmin',
grid = c('gr', 'gn'),
version = c('v20190713', 'v20200101'),
sdate = paste0(2018),
fmonth = 'all',
lat = values(list(0, 14)),
lon = values(list(0, 28)),
fyear = indices(1:2),
member = c('r1i1p1f1', 'r1i1p1f2'),
fyear_depends = 'sdate',
fmonth_across = 'fyear',
merge_across_dims = TRUE,
synonims = list(fmonth = c('fmonth','time'),
lon = c('lon', 'longitude'), lat = c('lat', 'latitude')),
transform = CDORemapper,
transform_extra_cells = 2,
transform_params = list(grid = 'r200x100', method = 'conservative', crop = c(0,28,0,14)),
transform_vars = c('lat', 'lon'),
return_vars = list(lat = 'dataset', lon = 'dataset'),
lat_reorder = Sort(),
num_procs = 1, retrieve = FALSE)
attributes(data)$ExpectedFiles
Cheers,
Núria
FYI @pabretonniere this issue is our next step in the data convention.