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.