def calc_CFSR_net(var1, var2, new_name, new_description):
"var1 - var2 = new_vame is the formula applied."
print("Calculating {0}".format(new_name))
fh_in_1 = netCDF4.Dataset(
'reanalysis_clean/cfsr.monthly.{0}.nc'.format(var1), 'r')
fh_in_2 = netCDF4.Dataset(
'reanalysis_clean/cfsr.monthly.{0}.nc'.format(var2), 'r')
# create_output_file(reanalysis, timeres,
# field, field_units, field_long_name,
# level, latitude, longitude):
fh_out = create_output_file("cfsr",
"monthly",
new_name,
"W m-2",
new_description,
None,
fh_in_1.variables['latitude'][:],
fh_in_1.variables['longitude'][:],
noclobber=False)
fh_out.variables[new_name][:] = fh_in_1[var1][:] - fh_in_2[var2][:]
fh_out.variables['time'][:] = fh_in_1['time'][:]
fh_out.close()
fh_in_1.close()
fh_in_2.close()
def calc_ERAI_rename_flipsign(var1, new_name, new_description):
"-var1 = new_vame is the formula applied."
print("Calculating {0}".format(new_name))
fh_in_1 = netCDF4.Dataset(
'reanalysis_clean/erai.monthly.{0}.nc'.format(var1), 'r')
fh_out = create_output_file("erai", "monthly", new_name, "W m-2",
new_description,
None,
fh_in_1.variables['latitude'][:],
fh_in_1.variables['longitude'][:],
noclobber=False)
fh_out.variables[new_name][:] = -1.0 * fh_in_1[var1][:]
fh_out.variables['time'][:] = fh_in_1['time'][:]
fh_out.close()
fh_in_1.close()
def clone_netcdf_skeleton(reanal, dsets, dset_variable, new_variable,
long_name, units, timeres='monthly'):
new_fn = varpath(reanal, new_variable)
dset = dsets[dset_variable]
if 'level' in dset.variables:
level = dset.variables['level'][:]
else:
level = None
latitude = dset.variables['latitude'][:]
longitude = dset.variables['longitude'][:]
fh = create_output_file(reanal, timeres,
new_variable, units, long_name,
level, latitude, longitude, noclobber=False)
fh.variables['time'][:] = dset.variables['time'][:]
return fh
def calc_ERAI_rename_flipsign(var1, new_name, new_description):
"-var1 = new_vame is the formula applied."
print("Calculating {0}".format(new_name))
fh_in_1 = netCDF4.Dataset(
'reanalysis_clean/erai.monthly.{0}.nc'.format(var1), 'r')
fh_out = create_output_file("erai",
"monthly",
new_name,
"W m-2",
new_description,
None,
fh_in_1.variables['latitude'][:],
fh_in_1.variables['longitude'][:],
noclobber=False)
fh_out.variables[new_name][:] = -1.0 * fh_in_1[var1][:]
fh_out.variables['time'][:] = fh_in_1['time'][:]
fh_out.close()
fh_in_1.close()
def calc_CFSR_net(var1, var2, new_name, new_description):
"var1 - var2 = new_vame is the formula applied."
print("Calculating {0}".format(new_name))
fh_in_1 = netCDF4.Dataset(
'reanalysis_clean/cfsr.monthly.{0}.nc'.format(var1), 'r')
fh_in_2 = netCDF4.Dataset(
'reanalysis_clean/cfsr.monthly.{0}.nc'.format(var2), 'r')
# create_output_file(reanalysis, timeres,
# field, field_units, field_long_name,
# level, latitude, longitude):
fh_out = create_output_file("cfsr", "monthly", new_name, "W m-2",
new_description,
None,
fh_in_1.variables['latitude'][:],
fh_in_1.variables['longitude'][:],
noclobber=False)
fh_out.variables[new_name][:] = fh_in_1[var1][:] - fh_in_2[var2][:]
fh_out.variables['time'][:] = fh_in_1['time'][:]
fh_out.close()
fh_in_1.close()
fh_in_2.close()
def create_sfn_velpot_vortdiv(reanal):
ds_U = netCDF4.Dataset(
'reanalysis_clean/{0}.monthly.U.nc'.format(reanal))
ds_VOR = netCDF4.Dataset(
'reanalysis_clean/{0}.monthly.VORTICITY.nc'.format(reanal))
ds_DIV = netCDF4.Dataset(
'reanalysis_clean/{0}.monthly.DIVERGENCE.nc'.format(reanal))
U = ds_U.variables['U']
VOR = ds_VOR.variables['VORTICITY']
DIV = ds_DIV.variables['DIVERGENCE']
ntime, nlev, nlat, nlon = U.shape
assert(U.shape == VOR.shape)
assert(U.shape == DIV.shape)
out_fh_1 = create_output_file(reanal, 'monthly',
'HORIZ_SFN', 'm2 s-1',
"Atmospheric Horizontal Streamfunction",
ds_U.variables['level'][:],
ds_U.variables['latitude'][:],
ds_U.variables['longitude'][:],
noclobber=True,
compress=True)
out_fh_2 = create_output_file(reanal, 'monthly',
'HORIZ_VEL_POT', 'm2 s-1',
"Atmospheric Horizontal Streamfunction",
ds_U.variables['level'][:],
ds_U.variables['latitude'][:],
ds_U.variables['longitude'][
:], noclobber=True,
compress=True)
horiz_sfn = out_fh_1.variables['HORIZ_SFN']
vel_pot = out_fh_2.variables['HORIZ_VEL_POT']
time_1 = out_fh_1.variables['time']
time_2 = out_fh_2.variables['time']
lats = ds_U.variables['latitude'][:]
lons = ds_U.variables['longitude'][:]
# spharm requires grid to go from North to South
# and from 0 to 360 positive
# Some data can be from -180 to +180 but since a sphere is invariant under
# rotations, that should be fine, as long as the direction is eastward.
if lats[-1] > lats[0]:
lats_increasing = True
assert(np.allclose(np.linspace(-90, 90, nlat), lats, 1.E-5, 1.E-5))
else:
lats_increasing = False
assert(np.allclose(np.linspace(90, -90, nlat), lats, 1.E-5, 1.E-5))
if lons[-1] > lons[0]:
lons_increasing = True
else:
lons_increasing = False
# check if the grid is regular and increasing
assert(lons[1]-lons[0] > 0.)
assert(np.allclose(np.diff(lons), lons[1]-lons[0], 1.0E-4))
psi = np.zeros(U[0].shape, dtype=np.float32)
chi = np.zeros(U[0].shape, dtype=np.float32)
# this could be written in a much faster way, but it's fast enough for now
s = spharm.Spharmt(nlon=nlon, nlat=nlat)
for itime in range(ntime):
print("{0}/{1}".format(itime+1, ntime))
time_1[itime] = ds_U.variables['time'][itime]
time_2[itime] = ds_U.variables['time'][itime]
vor_current = VOR[itime]
div_current = DIV[itime]
if type(vor_current) is np.ma.core.MaskedArray:
vor_current = vor_current.filled(0.)
if type(div_current) is np.ma.core.MaskedArray:
div_current = div_current.filled(0.)
if lats_increasing:
vor_current = vor_current[:, ::-1, :]
div_current = div_current[:, ::-1, :]
if not lons_increasing:
vor_current = vor_current[:, :, ::-1]
div_current = div_current[:, :, ::-1]
for ilev in range(nlev):
print("\t{0}".format(ilev+1))
vor_spec = s.grdtospec(vor_current[ilev])
div_spec = s.grdtospec(div_current[ilev])
psi_spec = _spherepack.invlap(vor_spec, s.rsphere)
chi_spec = _spherepack.invlap(div_spec, s.rsphere)
psi_current = np.squeeze(s.spectogrd(psi_spec))
chi_current = np.squeeze(s.spectogrd(chi_spec))
print("\t\t{0:12.4g}\t{1:12.4g}".format(
np.mean(psi_current), np.mean(chi_current)))
psi[ilev] = psi_current
chi[ilev] = chi_current
assert(not (np.allclose(psi[ilev], chi[ilev], 1.0E-6, 1.0E-6)))
if lats_increasing:
psi = psi[:, ::-1, :]
chi = chi[:, ::-1, :]
if not lons_increasing:
psi = psi[:, :, ::-1]
chi = chi[:, :, ::-1]
horiz_sfn[itime] = psi
vel_pot[itime] = chi
if itime % 10 == 0:
out_fh_1.sync()
out_fh_2.sync()
out_fh_1.close()
out_fh_2.close()
def create_sfn_velpot_vortdiv(reanal):
ds_U = netCDF4.Dataset('reanalysis_clean/{0}.monthly.U.nc'.format(reanal))
ds_VOR = netCDF4.Dataset(
'reanalysis_clean/{0}.monthly.VORTICITY.nc'.format(reanal))
ds_DIV = netCDF4.Dataset(
'reanalysis_clean/{0}.monthly.DIVERGENCE.nc'.format(reanal))
U = ds_U.variables['U']
VOR = ds_VOR.variables['VORTICITY']
DIV = ds_DIV.variables['DIVERGENCE']
ntime, nlev, nlat, nlon = U.shape
assert (U.shape == VOR.shape)
assert (U.shape == DIV.shape)
out_fh_1 = create_output_file(reanal,
'monthly',
'HORIZ_SFN',
'm2 s-1',
"Atmospheric Horizontal Streamfunction",
ds_U.variables['level'][:],
ds_U.variables['latitude'][:],
ds_U.variables['longitude'][:],
noclobber=True,
compress=True)
out_fh_2 = create_output_file(reanal,
'monthly',
'HORIZ_VEL_POT',
'm2 s-1',
"Atmospheric Horizontal Streamfunction",
ds_U.variables['level'][:],
ds_U.variables['latitude'][:],
ds_U.variables['longitude'][:],
noclobber=True,
compress=True)
horiz_sfn = out_fh_1.variables['HORIZ_SFN']
vel_pot = out_fh_2.variables['HORIZ_VEL_POT']
time_1 = out_fh_1.variables['time']
time_2 = out_fh_2.variables['time']
lats = ds_U.variables['latitude'][:]
lons = ds_U.variables['longitude'][:]
# spharm requires grid to go from North to South
# and from 0 to 360 positive
# Some data can be from -180 to +180 but since a sphere is invariant under
# rotations, that should be fine, as long as the direction is eastward.
if lats[-1] > lats[0]:
lats_increasing = True
assert (np.allclose(np.linspace(-90, 90, nlat), lats, 1.E-5, 1.E-5))
else:
lats_increasing = False
assert (np.allclose(np.linspace(90, -90, nlat), lats, 1.E-5, 1.E-5))
if lons[-1] > lons[0]:
lons_increasing = True
else:
lons_increasing = False
# check if the grid is regular and increasing
assert (lons[1] - lons[0] > 0.)
assert (np.allclose(np.diff(lons), lons[1] - lons[0], 1.0E-4))
psi = np.zeros(U[0].shape, dtype=np.float32)
chi = np.zeros(U[0].shape, dtype=np.float32)
# this could be written in a much faster way, but it's fast enough for now
s = spharm.Spharmt(nlon=nlon, nlat=nlat)
for itime in range(ntime):
print("{0}/{1}".format(itime + 1, ntime))
time_1[itime] = ds_U.variables['time'][itime]
time_2[itime] = ds_U.variables['time'][itime]
vor_current = VOR[itime]
div_current = DIV[itime]
if type(vor_current) is np.ma.core.MaskedArray:
vor_current = vor_current.filled(0.)
if type(div_current) is np.ma.core.MaskedArray:
div_current = div_current.filled(0.)
if lats_increasing:
vor_current = vor_current[:, ::-1, :]
div_current = div_current[:, ::-1, :]
if not lons_increasing:
vor_current = vor_current[:, :, ::-1]
div_current = div_current[:, :, ::-1]
for ilev in range(nlev):
print("\t{0}".format(ilev + 1))
vor_spec = s.grdtospec(vor_current[ilev])
div_spec = s.grdtospec(div_current[ilev])
psi_spec = _spherepack.invlap(vor_spec, s.rsphere)
chi_spec = _spherepack.invlap(div_spec, s.rsphere)
psi_current = np.squeeze(s.spectogrd(psi_spec))
chi_current = np.squeeze(s.spectogrd(chi_spec))
print("\t\t{0:12.4g}\t{1:12.4g}".format(np.mean(psi_current),
np.mean(chi_current)))
psi[ilev] = psi_current
chi[ilev] = chi_current
assert (not (np.allclose(psi[ilev], chi[ilev], 1.0E-6, 1.0E-6)))
if lats_increasing:
psi = psi[:, ::-1, :]
chi = chi[:, ::-1, :]
if not lons_increasing:
psi = psi[:, :, ::-1]
chi = chi[:, :, ::-1]
horiz_sfn[itime] = psi
vel_pot[itime] = chi
if itime % 10 == 0:
out_fh_1.sync()
out_fh_2.sync()
out_fh_1.close()
out_fh_2.close()
