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Hello @aho,

I am very new to startR and I would really appreciate any help.

I sould like to use startR to run, in the most efficient way, on Nord3v2 a Climpact function from the climdex.pcic.ncdf R package.

Here below my script so far, which however is not yet correct. I am using only two models for one SSP scenario and I would like to compute all the climpact indices available on original models' grids.

library(startR)

###1 Load data (does not return error but I am not sure if original horizontal resolution is kept)
data_load<- Start(dat = "/esarchive/scratch/pdeluca/landmarc/def/tasmax_tasmin_pr_$model$_ssp126_1949_2100.nc",
              var = c('tasmax', 'tasmin', 'pr'),
              model = c('CanESM5', 'INM-CM4-8'),
              #ssp = 'all',
              lat = 'all',
              lon = 'all',
              transform = NULL,
              synonims = list(lon = c('lon', 'longitude'),
                              lat = c('lat', 'latitude')),
              return_vars = list(time = NULL,
                              lat = 'dat',
                              lon = 'dat'),
              retrieve =  FALSE)


###2 Define function (to adjust)
library(climdex.pcic.ncdf)
library(stringr)

setwd("/esarchive/scratch/pdeluca/scripts/climpact-master")

create.indices.from.files(input.files= !!these are the datasets loaded in the previous step!!,

                            out.dir="/esarchive/scratch/pdeluca/landmarc/out/",

                            output.filename.template=paste("all_daily_cmip6_ssp126_",
                                                           !!here the models names in loaded in the previous step!!, "_1949-2100.nc", sep = ""),

                            author.data=list(institution="My University", institution_id="MU"),

                            climdex.vars.subset = NULL,
                            climdex.time.resolution = "all",

                            variable.name.map = c(tmax = "tasmax",
                                                  tmin = "tasmin",
                                                  prec = "pr"),

                            axis.to.split.on = "Y",
                            fclimdex.compatible = FALSE,

                            base.range = c(1981,2010),
                            parallel = 16,
                            verbose = TRUE,
                            thresholds.files = NULL,
                            thresholds.name.map = c(
                              tx05thresh= "tx05thresh",
                              tx10thresh ="tx10thresh",
                              tx50thresh = "tx50thresh",
                              tn05thresh = "tn05thresh",
                              tn10thresh = "tn10thresh",
                              tn50thresh = "tn50thresh",
                              tx90thresh = "tx90thresh",
                              tx95thresh = "tx95thresh",
                              tn90thresh = "tn90thresh",
                              tn95thresh = "tn95thresh",
                              tx90thresh_15days = "tx90thresh_15days",
                              tn90thresh_15days = "tn90thresh_15days",
                              tavg90thresh_15days = "tavg90thresh_15days",
                              tavg05thresh = "tavg05thresh",
                              tavg95thresh = "tavg95thresh",
                              r95thresh = "r95thresh",
                              r99thresh = "r99thresh",
                              txraw = "txraw",
                              tnraw = "tnraw",
                              precraw = "precraw"),

                            max.vals.millions = 30, cluster.type = "SOCK")



###3 Define the workflow (to adjust)
step <- Step(fun = climdex_ind,
               target_dims = list(tx = c('lat', 'time'),
                                  tn = c('lat', 'time'),
                                  pr = c('lat', 'time')),
               output_dims = list(fd = c('lat', 'time'),
                                  cdd = c('lat','time')))

wf <- AddStep(inputs = list(hist_tx, hist_tn, hist_pr), step,
              times = time, lats = lat)


###4 Define user and submit jobs (to adjust)
queue_host = 'nord3'   #your own host name for power9
temp_dir =  '/gpfs/scratch/bsc32/bsc32339/startR_hpc/'
ecflow_suite_dir = '/home/Earth/nperez/startR_local/'

res <- Compute(wf$fd,
               chunks = list(lon = 2),
                threads_load = 1,
                 threads_compute = 4,
                 cluster = list(queue_host = queue_host,
                                queue_type = 'lsf',
                                extra_queue_params = list('#BSUB -q bsc_es'),
                                cores_per_job = 4,
                                temp_dir = temp_dir,
                                polling_period = 10,
                                job_wallclock = '01:00',
                                max_jobs = 2,
                                bidirectional = FALSE),
                 ecflow_suite_dir = ecflow_suite_dir,
                 wait = TRUE)

Thanks

paolo