def moc_flux(variables, **kwargs):
"""Mass weighted column integrated meridional flux by time and
zonal mean flow."""
# Specify upper bound of vertical integrals.
p_top = kwargs.get('p_top', 0.)
trop = np.where(nc.variables['level'][:] >= p_top)
# Apply mass flux correction to zonal mean data.
v_znl = np.squeeze(variables[-1][:,trop]).mean(axis=-1)
v_north = np.where(v_znl > 0., v_znl, 0.)
v_south = np.where(v_znl < 0., v_znl, 0.)
lev_thick = np.squeeze(level_thickness(nc)[:,trop])/grav
lev_thick = lev_thick[np.newaxis,:,np.newaxis]
# Adjustment imposes that column integrated mass flux is zero.
mass_adj = -((v_north*lev_thick).sum(axis=1) /
(v_south*lev_thick).sum(axis=1))
# Integrate the specified flux by the adjusted v vertically and zonally.
flux_type = kwargs['flux_type']
if flux_type == 'dse':
flux = (np.squeeze(dse(variables[:2])[:,trop]).mean(axis=-1) *
(v_north + v_south * mass_adj[:,np.newaxis,:]))
elif flux_type == 'mse':
flux = (np.squeeze(mse(variables[:3])[:,trop]).mean(axis=-1) *
(v_north + v_south * mass_adj[:,np.newaxis,:]))
elif flux_type == 'moisture':
flux = L_v*(np.squeeze(variables[0][:,trop]).mean(axis=-1) *
(v_north + v_south * mass_adj[:,np.newaxis,:]))
return (2.*np.pi*r_e*np.cos(np.deg2rad(nc.variables[LAT_STR][:])) *
(flux*lev_thick).sum(axis=1))
def moc_flux(variables, **kwargs):
"""Mass weighted column integrated meridional flux by time and
zonal mean flow."""
# Specify upper bound of vertical integrals.
p_top = kwargs.get('p_top', 0.)
trop = np.where(nc.variables['level'][:] >= p_top)
# Apply mass flux correction to zonal mean data.
v_znl = np.squeeze(variables[-1][:, trop]).mean(axis=-1)
v_north = np.where(v_znl > 0., v_znl, 0.)
v_south = np.where(v_znl < 0., v_znl, 0.)
lev_thick = np.squeeze(level_thickness(nc)[:, trop]) / grav
lev_thick = lev_thick[np.newaxis, :, np.newaxis]
# Adjustment imposes that column integrated mass flux is zero.
mass_adj = -((v_north * lev_thick).sum(axis=1) /
(v_south * lev_thick).sum(axis=1))
# Integrate the specified flux by the adjusted v vertically and zonally.
flux_type = kwargs['flux_type']
if flux_type == 'dse':
flux = (np.squeeze(dse(variables[:2])[:, trop]).mean(axis=-1) *
(v_north + v_south * mass_adj[:, np.newaxis, :]))
elif flux_type == 'mse':
flux = (np.squeeze(mse(variables[:3])[:, trop]).mean(axis=-1) *
(v_north + v_south * mass_adj[:, np.newaxis, :]))
elif flux_type == 'moisture':
flux = L_v * (np.squeeze(variables[0][:, trop]).mean(axis=-1) *
(v_north + v_south * mass_adj[:, np.newaxis, :]))
return (2. * np.pi * r_e * np.cos(np.deg2rad(nc.variables[LAT_STR][:])) *
(flux * lev_thick).sum(axis=1))
def mass_flux(variables, **kwargs):
"""Meridional mass flux by time and zonal mean flow."""
# Apply mass flux correction.
lev_thick = level_thickness(nc)[np.newaxis,:,np.newaxis]/grav
v_znl = variables[0].mean(axis=-1)
v_north = np.where(v_znl > 0., v_znl, 0.)
v_south = np.where(v_znl < 0., v_znl, 0.)
mass_adj = -((v_north*lev_thick).sum(axis=1) /
(v_south*lev_thick).sum(axis=1))
# Integrate vertically and pick level where magnitude maximized.
int_flux = ((v_north + v_south*mass_adj[:,np.newaxis,:]) *
lev_thick).cumsum(axis=1)
flux_pos = np.amax(int_flux, axis=1)
flux_neg = np.amin(int_flux, axis=1)
return (2.*np.pi*r_e*np.cos(np.deg2rad(nc.variables[LAT_STR][:])) *
np.where(flux_pos > - flux_neg, flux_pos, flux_neg))
def mass_flux(variables, **kwargs):
"""Meridional mass flux by time and zonal mean flow."""
# Apply mass flux correction.
lev_thick = level_thickness(nc)[np.newaxis, :, np.newaxis] / grav
v_znl = variables[0].mean(axis=-1)
v_north = np.where(v_znl > 0., v_znl, 0.)
v_south = np.where(v_znl < 0., v_znl, 0.)
mass_adj = -((v_north * lev_thick).sum(axis=1) /
(v_south * lev_thick).sum(axis=1))
# Integrate vertically and pick level where magnitude maximized.
int_flux = ((v_north + v_south * mass_adj[:, np.newaxis, :]) *
lev_thick).cumsum(axis=1)
flux_pos = np.amax(int_flux, axis=1)
flux_neg = np.amin(int_flux, axis=1)
return (2. * np.pi * r_e * np.cos(np.deg2rad(nc.variables[LAT_STR][:])) *
np.where(flux_pos > -flux_neg, flux_pos, flux_neg))
def st_eddy_flux(variables, **kwargs):
"""Mass weighted column integrated meridional flux by stationary eddies."""
p_top = kwargs.get('p_top', 0.)
trop = np.where(nc.variables['level'][:] >= p_top)
v = np.squeeze(variables[-1][:,trop])
flux_type = kwargs['flux_type']
if flux_type == 'dse':
m = np.squeeze(dse(variables[:2])[:,trop])
elif flux_type == 'mse':
m = np.squeeze(mse(variables[:3])[:,trop])
elif flux_type == 'moisture':
m = np.squeeze(variables[0][:,trop])*L_v
lev_thick = np.squeeze(level_thickness(nc)[:,trop])/grav
lev_thick = lev_thick[np.newaxis,:,np.newaxis,np.newaxis]
flux = ((m - m.mean(axis=-1)[:,:,:,np.newaxis]) *
(v - v.mean(axis=-1)[:,:,:,np.newaxis]))
return (2.*np.pi*r_e*np.cos(np.deg2rad(nc.variables[LAT_STR][:])) *
(flux*lev_thick).sum(axis=1).mean(axis=-1))
def vert_centroid(arr, level, p_bot=850., p_top=150.):
"""
Compute the vertical centroid of some vertically defined field.
"""
desired_levs = np.where((level <= p_bot) & (level >= p_top))
lev_thick = level_thickness(level)/100.
# Add axes for later broadcasting and truncate to desired vertical levels.
level = level[desired_levs]; level = level[:,np.newaxis,np.newaxis]
lev_thick = lev_thick[desired_levs]
lev_thick = lev_thick[:,np.newaxis,np.newaxis]
arr = arr[desired_levs]
# For 1D arrays, have to move the vertical coordinate to leftmost dim.
if arr.ndim == 1:
arr = np.atleast_3d(arr).swapaxes(0,1)
else:
arr = np.atleast_3d(arr)
return (np.sum(arr*level*lev_thick, axis=0) /
np.sum(arr*lev_thick, axis=0))
def st_eddy_flux(variables, **kwargs):
"""Mass weighted column integrated meridional flux by stationary eddies."""
p_top = kwargs.get('p_top', 0.)
trop = np.where(nc.variables['level'][:] >= p_top)
v = np.squeeze(variables[-1][:, trop])
flux_type = kwargs['flux_type']
if flux_type == 'dse':
m = np.squeeze(dse(variables[:2])[:, trop])
elif flux_type == 'mse':
m = np.squeeze(mse(variables[:3])[:, trop])
elif flux_type == 'moisture':
m = np.squeeze(variables[0][:, trop]) * L_v
lev_thick = np.squeeze(level_thickness(nc)[:, trop]) / grav
lev_thick = lev_thick[np.newaxis, :, np.newaxis, np.newaxis]
flux = ((m - m.mean(axis=-1)[:, :, :, np.newaxis]) *
(v - v.mean(axis=-1)[:, :, :, np.newaxis]))
return (2. * np.pi * r_e * np.cos(np.deg2rad(nc.variables[LAT_STR][:])) *
(flux * lev_thick).sum(axis=1).mean(axis=-1))
def vert_centroid(arr, level, p_bot=850., p_top=150.):
"""
Compute the vertical centroid of some vertically defined field.
"""
desired_levs = np.where((level <= p_bot) & (level >= p_top))
lev_thick = level_thickness(level) / 100.
# Add axes for later broadcasting and truncate to desired vertical levels.
level = level[desired_levs]
level = level[:, np.newaxis, np.newaxis]
lev_thick = lev_thick[desired_levs]
lev_thick = lev_thick[:, np.newaxis, np.newaxis]
arr = arr[desired_levs]
# For 1D arrays, have to move the vertical coordinate to leftmost dim.
if arr.ndim == 1:
arr = np.atleast_3d(arr).swapaxes(0, 1)
else:
arr = np.atleast_3d(arr)
return (np.sum(arr * level * lev_thick, axis=0) /
np.sum(arr * lev_thick, axis=0))
def moc_flux_raw(variables, **kwargs):
"""Mass weighted column integrated meridional flux by time and zonal mean flow, without applying column mass flux correction."""
# Specify upper bound of vertical integrals.
p_top = kwargs.get('p_top', 0.)
trop = np.where(nc.variables['level'][:] >= p_top)
# Take zonal mean and calculate grid level thicknesses.
v_znl = np.squeeze(variables[-1][:,trop]).mean(axis=-1)
lev_thick = np.squeeze(level_thickness(nc)[:,trop])/grav
lev_thick = lev_thick[np.newaxis,:,np.newaxis]
# Integrate the specified flux vertically and zonally.
flux_type = kwargs.get('flux_type', 'mse')
if flux_type == 'dse':
flux = np.squeeze(dse(variables[:2])[:,trop]).mean(axis=-1) * v_znl
elif flux_type == 'mse':
flux = np.squeeze(mse(variables[:3])[:,trop]).mean(axis=-1) * v_znl
elif flux_type == 'moisture':
flux = L_v*np.squeeze(variables[0][:,trop]).mean(axis=-1) * v_znl
return (2.*np.pi*r_e*np.cos(np.deg2rad(nc.variables[LAT_STR][:])) *
(flux*lev_thick).sum(axis=1))
def moc_flux_raw(variables, **kwargs):
"""Mass weighted column integrated meridional flux by time and zonal mean flow, without applying column mass flux correction."""
# Specify upper bound of vertical integrals.
p_top = kwargs.get('p_top', 0.)
trop = np.where(nc.variables['level'][:] >= p_top)
# Take zonal mean and calculate grid level thicknesses.
v_znl = np.squeeze(variables[-1][:, trop]).mean(axis=-1)
lev_thick = np.squeeze(level_thickness(nc)[:, trop]) / grav
lev_thick = lev_thick[np.newaxis, :, np.newaxis]
# Integrate the specified flux vertically and zonally.
flux_type = kwargs.get('flux_type', 'mse')
if flux_type == 'dse':
flux = np.squeeze(dse(variables[:2])[:, trop]).mean(axis=-1) * v_znl
elif flux_type == 'mse':
flux = np.squeeze(mse(variables[:3])[:, trop]).mean(axis=-1) * v_znl
elif flux_type == 'moisture':
flux = L_v * np.squeeze(variables[0][:, trop]).mean(axis=-1) * v_znl
return (2. * np.pi * r_e * np.cos(np.deg2rad(nc.variables[LAT_STR][:])) *
(flux * lev_thick).sum(axis=1))
