def main():
outdir = '/gws/nopw/j04/primavera3/cache/sberthou/'
country = 'prudence'
frequency = 'd'
n512 = False
other = 'PRIMAVERA'
time_con = iris.Constraint(
time=lambda cell: 1950 <= cell.point.year < 2006)
# CORDEX grid, obtained by executing "cdo griddes CORDEX_file.nc > EUROCORDEX_grid.txt"
region_name = 'EUROCORDEX'
new_grid = '{}_grid.txt'.format(region_name)
runlist, _ = ems.get_runlist_region(frequency, n512, country, other=other)
for model in runlist:
#### 1st step: load all the data into one single cube and save it, so that CDO regrid is fast (calculates the weights only once, then applies it to the whole time series. ####
#### at the very end, once you're happy that everything has been regridded, don't forget to delete the large files, as they are enormous! ####
large_cube = '{}/pr_{}'.format(outdir, model)
if not os.path.exists('{}_with_grid.nc'.format(large_cube)):
if not os.path.exists('{}.nc'.format(large_cube)):
cubelist = iris.load(runlist[model],
time_con,
callback=callback_overwrite)
iris.util.unify_time_units(cubelist)
cube = cubelist.concatenate_cube()
print('cubes loaded')
iris.save(cube, large_cube + '.nc')
print('large cube saved')
elif 'EC-Earth' in model:
cube = iris.load_cube('{}.nc'.format(large_cube))
redefine_spatial_coords(cube)
iris.save(cube, '{}_tmp.nc'.format(large_cube))
cmd = 'mv {}_tmp.nc {}.nc'.format(large_cube, large_cube)
shellcmd(cmd, 'mv ECarth failed')
#### get the grid from the large netCDF file ####
cmd = 'cdo griddes {}.nc > init_grid.txt'.format(large_cube)
shellcmd(cmd, 'cdo griddes didn' 't complete')
#### set the grid in the file (not sure why you have to do this, I think you need it mostly for the grids which have 1D lat/lon, because cdo requires 2D lat,lons, calculated with cdo griddes ####
cmd = 'cdo -setgrid,init_grid.txt {}.nc {}_with_grid.nc'.format(
large_cube, large_cube)
shellcmd(cmd, 'cdo setgrid didn' 't complete')
#### remapping itself ####
if not os.path.exists('{}_regridded_on_{}.nc'.format(
large_cube, region_name)):
if not model == 'EC-Earth3P-HR':
cmd = 'cdo remapcon,{} {}_with_grid.nc {}_regridded_on_{}.nc'.format(
new_grid, large_cube, large_cube, region_name)
shellcmd(cmd, 'cdo remapcon didn' 't complete')
else:
cmd = 'cdo remapcon,{} {}.nc {}_regridded_on_{}.nc'.format(
new_grid, large_cube, large_cube, region_name)
shellcmd(cmd, 'cdo remapcon didn' 't complete')
distribution = args.b
hist_type = args.g
wet_or_dry_spells = args.w
thrs = args.t
#ensembles_to_compare = ['PRIMAVERA', 'CORDEX-44', 'obs_cordex50']
ensembles_to_compare = ['PRIMAVERA', 'CORDEX-11_regrid', 'obs_cordex50']
#ensembles_to_compare = [ 'PRIMAVERA']
bootstrap_or_centiles = 'centiles'
### generate ensemble_dict: the dictionary containing all models names in each ensemble.
### The observations are not in this as interannual variability rather than inter-member is calculated
ensemble_dict = {}
for ens in ensembles_to_compare:
if not 'obs' in ens.lower():
runlist, _ = ems.get_runlist_region('d', False, 'prudence', other=ens)
ensemble_dict[ens] = runlist.keys()
## this provides the bootstrap configuration, if bootstrapping is chosen to compare the ensembles
nb_bootstrap_iter = 999999
bootstrap_config = {}
if bootstrap_or_centiles == 'bootstrap':
bootstrap_config = {
'PRIMAVERA': {
'bootstrap_produce_file':
False, # True if want to generate bootstrapping random years rather than use exisiting file
'bootstrap_no_years':
len(ensemble_dict['PRIMAVERA']), # number of PRIMAVERA models
'bootstrap_sample_size':
nb_bootstrap_iter, # NB if this goes > nb_bootstrap_iter (3 digits) formatting fails
'bootstrap_samples_split': [
country = args.c
n512 = bool_from_string(args.r)
frequency = args.f
other = args.o
season_list = args.s
print type(season_list), season_list
bintypes = args.b
run_on_spice = bool_from_string(args.e)
wet_dry_2dhist = args.w
if wet_dry_2dhist == 'None':
wet_dry_2dhist = None
thrs = args.t
# get the runlist and the region masks from european_masked_subregion.py
runlist, maskedreglist = ems.get_runlist_region(frequency,
n512,
country,
other=other)
# batch job characteristics:
spice_freq = {
'mem': {
'h': '40',
'd': '20'
}, ### ME: this is where you can change how much memory you use, 20GB by default
'queue': {
'h': 'normal',
'd': 'normal'
},
'time': {
'h': '180',
'd': '100'
def main():
datadir = '/home/users/sberthou/PrecipExtremes/'
frequency = 'd'
full_ensemble = False
if full_ensemble:
runlist, _ = ems.get_runlist_region(frequency, False, 'prudence',
'PRIMAVERA')
PRIMAVERA_ens = runlist.keys()
runlist1, _ = ems.get_runlist_region(frequency, False, 'prudence',
'CORDEX-44')
CORDEX_ens = runlist1.keys()
runlist2, _ = ems.get_runlist_region(frequency, False, 'prudence',
'CORDEX11')
CORDEX_ens_11 = runlist2.keys()
runlist3, _ = ems.get_runlist_region(frequency, False, 'prudence',
'CORDEX-11_regrid')
CORDEX_ens11_regrid = runlist3.keys()
else:
runlist, _ = ems.get_runlist_region(frequency, False, 'prudence',
'PRIMAVERA_reduced')
PRIMAVERA_ens = runlist.keys()
runlist1, _ = ems.get_runlist_region(frequency, False, 'prudence',
'CORDEX-44_reduced')
CORDEX_ens = runlist1.keys()
runlist3, _ = ems.get_runlist_region(frequency, False, 'prudence',
'CORDEX-11_regrid_reduced')
CORDEX_ens11_regrid = runlist3.keys()
obs_ens = [
'eobs_cordex50',
'ca_cordex50',
'france_cordex50',
'ibp_cordex50',
'alps_cordex50',
'uk_cordex50',
]
seasons = ['jja', 'djf', 'mam', 'son']
ref_grid = iris.load_cube(
'{}/mean_nc/ibp_cordex50_jja_mean_2000.nc'.format(datadir))
ensembles = {
'OBS': obs_ens,
'PRIMAVERA': PRIMAVERA_ens,
'EUR-44': CORDEX_ens, #'CORDEX-11': CORDEX_ens_11,
'EUR-11': CORDEX_ens11_regrid
}
for season in seasons:
print season
cubelist_dict = {}
for ensemble in ensembles:
print ensemble
cubelist = iris.cube.CubeList([])
for num, simu in enumerate(ensembles[ensemble]):
print simu
cubelist0 = iris.load('{}/mean_nc/{}_{}_mean*.nc'.format(
datadir, simu, season))
if len(cubelist0) == 1:
cube = cubelist0[0]
else:
cube = cubelist0.extract('precipitation_flux')[0]
modify_grid(cube, simu)
coord_names = [coord.name() for coord in cube.dim_coords]
print cube
try:
lonname, = filter(lambda x: re.search(r'[Ll]on|x', x),
coord_names)
except:
__init__.add_CORDEX_grid(cube, 'EUROCORDEX')
newcube = cube.regrid(ref_grid, iana.Linear())
if ensemble is not 'OBS':
member_coord = iris.coords.AuxCoord(
[num], long_name='member_number', units='1')
newcube.add_aux_coord(member_coord)
newcube.attributes = None
newcube.cell_methods = None
try:
newcube.remove_coord('time')
except:
pass
newcube1 = precip_to_mm(newcube)
cubelist.append(newcube1)
cubelist_dict[ensemble] = cubelist.merge()
print cubelist_dict[ensemble]
obs = combine_obs(cubelist_dict['OBS'])
add_reduced = ''
if not full_ensemble:
add_reduced = '_reduced'
figfile = '{}/images/mean_bias{}_{}'.format(datadir, add_reduced,
season)
plot_maps(obs,
cubelist_dict,
plot_order=['PRIMAVERA', 'EUR-11', 'EUR-44'],
figfile=figfile,
season=season)