def main(args):
nc_misval = 1.e20
#-- Define Regions and their associated masks
# Note: The Atlantic should include other smaller bays/seas that are
# included in the definition used in meridional_overturning.py
region = np.array([
'atlantic_arctic_ocean ', 'indian_pacific_ocean',
'global_ocean'
])
#-- Read basin masks
f_basin = nc.Dataset(args.basinfile)
basin_code = f_basin.variables['basin'][:]
atlantic_arctic_mask = basin_code * 0.
atlantic_arctic_mask[(basin_code == 2) | (basin_code == 4) |
(basin_code == 6) | (basin_code == 7) |
(basin_code == 8)] = 1.
indo_pacific_mask = basin_code * 0.
indo_pacific_mask[(basin_code == 3) | (basin_code == 5)] = 1.
#-- Read model data
f_in = nc.Dataset(args.infile)
#-- Read in existing dimensions from history netcdf file
xh = f_in.variables['xh']
yh = f_in.variables['yh']
yq = f_in.variables['yq']
z_l = f_in.variables['z_l']
z_i = f_in.variables['z_i']
tax = f_in.variables['time']
#-- Note: based on conversations with @adcroft, the overturning should be reported on the interfaces, z_i.
# Also, the nominal latitude is insufficient for the basin-average fields. Based on the methods in
# meridional_overturning.py, the latitude dimension should be:
#
# y = netCDF4.Dataset(cmdLineArgs.gridspec+'/ocean_hgrid.nc').variables['y'][::2,::2]
# yy = y[1:,:].max(axis=-1)+0*z
#
# The quanity 'yy' above is numerically-equivalent to 'yq'
#-- msftyyz
if 'vmo' in f_in.variables.keys():
varname = 'vmo'
msftyyz = np.ma.ones((len(tax), 3, len(z_i), len(yq))) * 0.
msftyyz[:, 0, :, :] = m6toolbox.moc_maskedarray(
f_in.variables[varname][:], mask=atlantic_arctic_mask)
msftyyz[:, 1, :, :] = m6toolbox.moc_maskedarray(
f_in.variables[varname][:], mask=indo_pacific_mask)
msftyyz[:, 2, :, :] = m6toolbox.moc_maskedarray(
f_in.variables[varname][:])
msftyyz[msftyyz.mask] = nc_misval
msftyyz = np.ma.array(msftyyz, fill_value=nc_misval)
msftyyz.long_name = 'Ocean Y Overturning Mass Streamfunction'
msftyyz.units = 'kg s-1'
msftyyz.coordinates = 'region'
msftyyz.cell_methods = 'z_i:point yq:point time:mean'
msftyyz.time_avg_info = 'average_T1,average_T2,average_DT'
msftyyz.standard_name = 'ocean_y_overturning_mass_streamfunction'
do_msftyyz = True
else:
do_msftyyz = False
#-- msftyzmpa
if 'vhGM' in f_in.variables.keys():
varname = 'vhGM'
msftyzmpa = np.ma.ones((len(tax), 3, len(z_i), len(yq))) * 0.
msftyzmpa[:, 0, :, :] = m6toolbox.moc_maskedarray(
f_in.variables[varname][:], mask=atlantic_arctic_mask)
msftyzmpa[:, 1, :, :] = m6toolbox.moc_maskedarray(
f_in.variables[varname][:], mask=indo_pacific_mask)
msftyzmpa[:, 2, :, :] = m6toolbox.moc_maskedarray(
f_in.variables[varname][:])
msftyzmpa[msftyzmpa.mask] = nc_misval
msftyzmpa = np.ma.array(msftyzmpa, fill_value=nc_misval)
msftyzmpa.long_name = 'ocean Y overturning mass streamfunction due to parameterized mesoscale advection'
msftyzmpa.units = 'kg s-1'
msftyzmpa.coordinates = 'region'
msftyzmpa.cell_methods = 'z_i:point yq:point time:mean'
msftyzmpa.time_avg_info = 'average_T1,average_T2,average_DT'
msftyzmpa.standard_name = 'ocean_y_overturning_mass_streamfunction_due_to_parameterized_'+\
'mesoscale_advection'
do_msftyzmpa = True
else:
do_msftyzmpa = False
#-- msftyzsmpa
if 'vhml' in f_in.variables.keys():
varname = 'vhml'
msftyzsmpa = np.ma.ones((len(tax), 3, len(z_i), len(yq))) * 0.
msftyzsmpa[:, 0, :, :] = m6toolbox.moc_maskedarray(
f_in.variables[varname][:], mask=atlantic_arctic_mask)
msftyzsmpa[:, 1, :, :] = m6toolbox.moc_maskedarray(
f_in.variables[varname][:], mask=indo_pacific_mask)
msftyzsmpa[:, 2, :, :] = m6toolbox.moc_maskedarray(
f_in.variables[varname][:])
msftyzsmpa[msftyzsmpa.mask] = nc_misval
msftyzsmpa = np.ma.array(msftyzsmpa, fill_value=nc_misval)
msftyzsmpa.long_name = 'ocean Y overturning mass streamfunction due to parameterized submesoscale advection'
msftyzsmpa.units = 'kg s-1'
msftyzsmpa.coordinates = 'region'
msftyzsmpa.cell_methods = 'z_i:point yq:point time:mean'
msftyzsmpa.time_avg_info = 'average_T1,average_T2,average_DT'
msftyzsmpa.standard_name = 'ocean_meridional_overturning_mass_streamfunction_due_to_parameterized_'+\
'submesoscale_advection'
do_msftyzsmpa = True
else:
do_msftyzsmpa = False
#-- wmo
if all(x in f_in.variables.keys() for x in ['umo', 'vmo']):
varname = 'wmo'
wmo = calc_w_from_convergence(f_in.variables['umo'],
f_in.variables['vmo'])
wmo[wmo.mask] = nc_misval
wmo = np.ma.array(wmo, fill_value=nc_misval)
wmo.long_name = 'Upward mass transport from resolved and parameterized advective transport'
wmo.units = 'kg s-1'
wmo.cell_methods = 'z_i:point xh:sum yh:sum time:mean'
wmo.time_avg_info = 'average_T1,average_T2,average_DT'
wmo.standard_name = 'upward_ocean_mass_transport'
wmo.cell_measures = 'area:areacello'
do_wmo = True
else:
do_wmo = False
#-- mfo
try:
_, mfo, straits = sum_transport_in_straits(args.straitdir,
monthly_average=True)
#mfo[mfo.mask] = nc_misval
mfo = np.ma.array(mfo, fill_value=nc_misval)
strait_names = np.array([strait.cmor_name for strait in straits])
mfo.long_name = 'Sea Water Transport'
mfo.units = 'kg s-1'
mfo.coordinates = 'strait'
mfo.cell_methods = 'time:mean'
mfo.time_avg_info = 'average_T1,average_T2,average_DT'
mfo.standard_name = 'sea_water_transport_across_line'
do_mfo = True
except:
do_mfo = False
#-- Read time bounds
nv = f_in.variables['nv']
average_T1 = f_in.variables['average_T1']
average_T2 = f_in.variables['average_T2']
average_DT = f_in.variables['average_DT']
time_bnds = f_in.variables['time_bnds']
if any([do_msftyyz, do_msftyzsmpa, do_msftyzmpa, do_wmo, do_mfo]):
#-- Generate output filename
if args.outfile is None:
if hasattr(f_in, 'filename'):
args.outfile = f_in.filename
else:
args.outfile = os.path.basename(args.infile)
args.outfile = args.outfile.split('.')
args.outfile[-2] = args.outfile[-2] + '_refined'
args.outfile = '.'.join(args.outfile)
if args.refineDiagDir is not None:
args.outfile = args.refineDiagDir + '/' + args.outfile
#-- Write output file
try:
os.remove(args.outfile)
except:
pass
if os.path.exists(args.outfile):
raise IOError('Output netCDF file already exists.')
exit(1)
f_out = nc.Dataset(args.outfile, 'w', format='NETCDF3_CLASSIC')
f_out.setncatts(f_in.__dict__)
f_out.filename = os.path.basename(args.outfile)
time_dim = f_out.createDimension('time', size=None)
basin_dim = f_out.createDimension('basin', size=3)
strait_dim = f_out.createDimension('strait', size=len(straits))
strlen_dim = f_out.createDimension('strlen', size=31)
xh_dim = f_out.createDimension('xh', size=len(xh[:]))
yh_dim = f_out.createDimension('yh', size=len(yh[:]))
yq_dim = f_out.createDimension('yq', size=len(yq[:]))
z_l_dim = f_out.createDimension('z_l', size=len(z_l[:]))
z_i_dim = f_out.createDimension('z_i', size=len(z_i[:]))
nv_dim = f_out.createDimension('nv', size=len(nv[:]))
time_out = f_out.createVariable('time', np.float64, ('time'))
xh_out = f_out.createVariable('xh', np.float64, ('xh'))
yh_out = f_out.createVariable('yh', np.float64, ('yh'))
yq_out = f_out.createVariable('yq', np.float64, ('yq'))
region_out = f_out.createVariable('region', 'c', ('basin', 'strlen'))
strait_out = f_out.createVariable('strait', 'c', ('strait', 'strlen'))
z_l_out = f_out.createVariable('z_l', np.float64, ('z_l'))
z_i_out = f_out.createVariable('z_i', np.float64, ('z_i'))
nv_out = f_out.createVariable('nv', np.float64, ('nv'))
if do_msftyyz:
msftyyz_out = f_out.createVariable('msftyyz',
np.float32,
('time', 'basin', 'z_i', 'yq'),
fill_value=nc_misval)
msftyyz_out.missing_value = nc_misval
for k in msftyyz.__dict__.keys():
if k[0] != '_': msftyyz_out.setncattr(k, msftyyz.__dict__[k])
if do_msftyzsmpa:
msftyzsmpa_out = f_out.createVariable(
'msftyzsmpa',
np.float32, ('time', 'basin', 'z_i', 'yq'),
fill_value=nc_misval)
msftyzsmpa_out.missing_value = nc_misval
for k in msftyzsmpa.__dict__.keys():
if k[0] != '_':
msftyzsmpa_out.setncattr(k, msftyzsmpa.__dict__[k])
if do_msftyzmpa:
msftyzmpa_out = f_out.createVariable(
'msftyzmpa',
np.float32, ('time', 'basin', 'z_i', 'yq'),
fill_value=nc_misval)
msftyzmpa_out.missing_value = nc_misval
for k in msftyzmpa.__dict__.keys():
if k[0] != '_':
msftyzmpa_out.setncattr(k, msftyzmpa.__dict__[k])
if do_wmo:
wmo_out = f_out.createVariable('wmo',
np.float32,
('time', 'z_i', 'yh', 'xh'),
fill_value=nc_misval)
wmo_out.missing_value = nc_misval
for k in wmo.__dict__.keys():
if k[0] != '_': wmo_out.setncattr(k, wmo.__dict__[k])
if do_mfo:
mfo_out = f_out.createVariable('mfo',
np.float32, ('time', 'strait'),
fill_value=nc_misval)
mfo_out.missing_value = nc_misval
for k in mfo.__dict__.keys():
if k[0] != '_': mfo_out.setncattr(k, mfo.__dict__[k])
average_T1_out = f_out.createVariable('average_T1', np.float64,
('time'))
average_T2_out = f_out.createVariable('average_T2', np.float64,
('time'))
average_DT_out = f_out.createVariable('average_DT', np.float64,
('time'))
time_bnds_out = f_out.createVariable('time_bnds', np.float64,
('time', 'nv'))
time_out.setncatts(tax.__dict__)
xh_out.setncatts(xh.__dict__)
yh_out.setncatts(yh.__dict__)
yq_out.setncatts(yq.__dict__)
z_l_out.setncatts(z_l.__dict__)
z_i_out.setncatts(z_i.__dict__)
nv_out.setncatts(nv.__dict__)
region_out.setncattr('standard_name', 'region')
strait_out.setncattr('standard_name', 'region')
average_T1_out.setncatts(average_T1.__dict__)
average_T2_out.setncatts(average_T2.__dict__)
average_DT_out.setncatts(average_DT.__dict__)
time_bnds_out.setncatts(time_bnds.__dict__)
time_out[:] = np.array(tax[:])
xh_out[:] = np.array(xh[:])
yh_out[:] = np.array(yh[:])
yq_out[:] = np.array(yq[:])
z_l_out[:] = np.array(z_l[:])
z_i_out[:] = np.array(z_i[:])
nv_out[:] = np.array(nv[:])
if do_msftyyz: msftyyz_out[:] = np.ma.array(msftyyz[:])
if do_msftyzsmpa: msftyzsmpa_out[:] = np.ma.array(msftyzsmpa[:])
if do_msftyzmpa: msftyzmpa_out[:] = np.ma.array(msftyzmpa[:])
if do_wmo: wmo_out[:] = np.ma.array(wmo[:])
if do_mfo: mfo_out[:] = np.array(mfo[:])
average_T1_out[:] = average_T1[:]
average_T2_out[:] = average_T2[:]
average_DT_out[:] = average_DT[:]
time_bnds_out[:] = time_bnds[:]
region_out[:] = nc.stringtochar(region)
strait_out[:] = nc.stringtochar(strait_names)
f_out.close()
exit(0)
else:
print('RefineDiag for ocean_month_rho2 yielded no output.')
exit(1)
def main(args):
#-- Define Regions and their associated masks
# Note: The Atlantic should include other smaller bays/seas that are
# included in the definition used in meridional_overturning.py
region = np.array(
['atlantic_arctic_ocean', 'indian_pacific_ocean', 'global_ocean'])
_, nl = nc.stringtochar(region).shape
#-- Read basin masks
f_basin = nc.Dataset(args.basinfile)
basin_code = f_basin.variables['basin'][:]
atlantic_arctic_mask = basin_code * 0.
atlantic_arctic_mask[(basin_code == 2) | (basin_code == 4) |
(basin_code == 6) | (basin_code == 7) |
(basin_code == 8)] = 1.
indo_pacific_mask = basin_code * 0.
indo_pacific_mask[(basin_code == 3) | (basin_code == 5)] = 1.
#-- Read model data
f_in = nc.Dataset(args.infile)
#-- Read in existing dimensions from history netcdf file
yq = f_in.variables['yq']
rho2_l = f_in.variables['rho2_l']
rho2_i = f_in.variables['rho2_i']
tax = f_in.variables['time']
if (len(yq) == 1 + atlantic_arctic_mask.shape[0]): #symmetric case
atlantic_arctic_mask = np.append(atlantic_arctic_mask,
np.zeros(
(1,
atlantic_arctic_mask.shape[1])),
axis=0)
indo_pacific_mask = np.append(indo_pacific_mask,
np.zeros(
(1, indo_pacific_mask.shape[1])),
axis=0)
#-- msftyrho
if 'vmo' in list(f_in.variables.keys()):
varname = 'vmo'
msftyrho = np.ma.ones((len(tax), 3, len(rho2_i), len(yq))) * 0.
msftyrho[:, 0, :, :] = m6toolbox.moc_maskedarray(
f_in.variables[varname][:], mask=atlantic_arctic_mask)
msftyrho[:, 1, :, :] = m6toolbox.moc_maskedarray(
f_in.variables[varname][:], mask=indo_pacific_mask)
msftyrho[:, 2, :, :] = m6toolbox.moc_maskedarray(
f_in.variables[varname][:])
msftyrho[msftyrho.mask] = 1.e20
msftyrho = np.ma.array(msftyrho, fill_value=1.e20)
msftyrho.long_name = 'Ocean Y Overturning Mass Streamfunction'
msftyrho.units = 'kg s-1'
msftyrho.coordinates = 'region'
msftyrho.cell_methods = 'rho2_i:point yq:point time:mean'
msftyrho.time_avg_info = 'average_T1,average_T2,average_DT'
msftyrho.standard_name = 'ocean_y_overturning_mass_streamfunction'
do_msftyrho = True
else:
do_msftyrho = False
#-- msftyrhompa
if 'vhGM' in list(f_in.variables.keys()):
varname = 'vhGM'
msftyrhompa = np.ma.ones((len(tax), 3, len(rho2_i), len(yq))) * 0.
msftyrhompa[:, 0, :, :] = m6toolbox.moc_maskedarray(
f_in.variables[varname][:], mask=atlantic_arctic_mask)
msftyrhompa[:, 1, :, :] = m6toolbox.moc_maskedarray(
f_in.variables[varname][:], mask=indo_pacific_mask)
msftyrhompa[:, 2, :, :] = m6toolbox.moc_maskedarray(
f_in.variables[varname][:])
msftyrhompa[msftyrhompa.mask] = 1.e20
msftyrhompa = np.ma.array(msftyrhompa, fill_value=1.e20)
msftyrhompa.long_name = 'ocean Y overturning mass streamfunction due to parameterized mesoscale advection'
msftyrhompa.units = 'kg s-1'
msftyrhompa.coordinates = 'region'
msftyrhompa.cell_methods = 'rho2_i:point yq:point time:mean'
msftyrhompa.time_avg_info = 'average_T1,average_T2,average_DT'
msftyrhompa.standard_name = 'ocean_y_overturning_mass_streamfunction_due_to_parameterized_'+\
'mesoscale_advection'
do_msftyrhompa = True
else:
do_msftyrhompa = False
#-- Read time bounds
nv = f_in.variables['nv']
average_T1 = f_in.variables['average_T1']
average_T2 = f_in.variables['average_T2']
average_DT = f_in.variables['average_DT']
time_bnds = f_in.variables['time_bnds']
if any([do_msftyrho, do_msftyrhompa]):
#-- Generate output filename
if args.outfile is None:
if hasattr(f_in, 'filename'):
args.outfile = f_in.filename
else:
args.outfile = os.path.basename(args.infile)
args.outfile = args.outfile.split('.')
args.outfile[-2] = args.outfile[-2] + '_refined'
args.outfile = '.'.join(args.outfile)
if args.refineDiagDir is not None:
args.outfile = args.refineDiagDir + '/' + args.outfile
#-- Write output file
try:
os.remove(args.outfile)
except:
pass
if os.path.exists(args.outfile):
raise IOError('Output netCDF file already exists.')
exit(1)
f_out = nc.Dataset(args.outfile, 'w', format='NETCDF3_CLASSIC')
ncattrs = f_in.__dict__
ncattrs.pop('associated_files', '') # not needed for these fields
f_out.setncatts(ncattrs)
f_out.filename = os.path.basename(args.outfile)
time_dim = f_out.createDimension('time', size=None)
basin_dim = f_out.createDimension('basin', size=3)
strlen_dim = f_out.createDimension('strlen', size=nl)
yq_dim = f_out.createDimension('yq', size=len(yq[:]))
rho2_l_dim = f_out.createDimension('rho2_l', size=len(rho2_l[:]))
rho2_i_dim = f_out.createDimension('rho2_i', size=len(rho2_i[:]))
nv_dim = f_out.createDimension('nv', size=len(nv[:]))
time_out = f_out.createVariable('time', np.float64, ('time'))
yq_out = f_out.createVariable('yq', np.float64, ('yq'))
region_out = f_out.createVariable('region', 'c', ('basin', 'strlen'))
rho2_l_out = f_out.createVariable('rho2_l', np.float64, ('rho2_l'))
rho2_i_out = f_out.createVariable('rho2_i', np.float64, ('rho2_i'))
nv_out = f_out.createVariable('nv', np.float64, ('nv'))
if do_msftyrho:
msftyrho_out = f_out.createVariable(
'msftyrho',
np.float32, ('time', 'basin', 'rho2_i', 'yq'),
fill_value=1.e20)
msftyrho_out.missing_value = 1.e20
for k in list(msftyrho.__dict__.keys()):
if k[0] != '_': msftyrho_out.setncattr(k, msftyrho.__dict__[k])
if do_msftyrhompa:
msftyrhompa_out = f_out.createVariable(
'msftyrhompa',
np.float32, ('time', 'basin', 'rho2_i', 'yq'),
fill_value=1.e20)
msftyrhompa_out.missing_value = 1.e20
for k in list(msftyrhompa.__dict__.keys()):
if k[0] != '_':
msftyrhompa_out.setncattr(k, msftyrhompa.__dict__[k])
region_out.setncattr('standard_name', 'region')
average_T1_out = f_out.createVariable('average_T1', np.float64,
('time'))
average_T2_out = f_out.createVariable('average_T2', np.float64,
('time'))
average_DT_out = f_out.createVariable('average_DT', np.float64,
('time'))
time_bnds_out = f_out.createVariable('time_bnds', np.float64,
('time', 'nv'))
time_out.setncatts(tax.__dict__)
yq_out.setncatts(yq.__dict__)
rho2_l_out.setncatts(rho2_l.__dict__)
rho2_i_out.setncatts(rho2_i.__dict__)
nv_out.setncatts(nv.__dict__)
average_T1_out.setncatts(average_T1.__dict__)
average_T2_out.setncatts(average_T2.__dict__)
average_DT_out.setncatts(average_DT.__dict__)
time_bnds_out.setncatts(time_bnds.__dict__)
time_out[:] = np.array(tax[:])
yq_out[:] = np.array(yq[:])
rho2_l_out[:] = np.array(rho2_l[:])
rho2_i_out[:] = np.array(rho2_i[:])
nv_out[:] = np.array(nv[:])
if do_msftyrho: msftyrho_out[:] = np.ma.array(msftyrho[:])
if do_msftyrhompa: msftyrhompa_out[:] = np.ma.array(msftyrhompa[:])
average_T1_out[:] = average_T1[:]
average_T2_out[:] = average_T2[:]
average_DT_out[:] = average_DT[:]
time_bnds_out[:] = time_bnds[:]
region_out[:] = nc.stringtochar(region)
f_out.close()
exit(0)
else:
print('RefineDiag for ocean_month_rho2 yielded no output.')
exit(1)