def plot_diagnostics(trans,
name='simulated',
outdir='./',
date_format='%Y%m%d',
obs_vol=None,
obs_oht=None,
obs_sf=None,
obs_fc=None):
""" Plot volume and heat transport diagnostics against RAPID observations """
# Create basename for output files
dts = utils.get_ncdates(trans)
basename = utils.get_savename(outdir, name, dts, date_format, suffix='_')
# Plot data
plot_streamfunctions(trans, basename=basename, name=name, obs=obs_sf)
plot_streamfunction_hovmollers(trans,
basename=basename,
name=name,
obs=obs_sf)
plot_volume_components(trans,
basename=basename,
name=name,
obs_vol=obs_vol,
obs_fc=obs_fc)
plot_rapid_heat_components(trans,
basename=basename,
name=name,
obs=obs_oht)
plot_geometric_heat_components(trans,
basename=basename,
name=name,
obs=obs_oht)
plot_zonal_mean_temperature(trans,
basename=basename,
name=name,
obs=obs_oht)
plot_transport_profile(trans, basename=basename, name=name, obs=obs_oht)
plot_fc_transport_profile(trans, basename=basename, name=name, obs=obs_oht)
plot_moc_vs_oht(trans,
basename=basename,
name=name,
obs_vol=obs_vol,
obs_oht=obs_oht)
plot_vol_vs_heat_transports(trans,
basename=basename,
name=name,
obs_vol=obs_vol,
obs_oht=obs_oht)
def plot_rapid_heat_components(trans, basename='', name='simulated', obs=None):
""" Plot RAPID heat transport components """
# Add model data to sub-axis
fig = plt.figure(figsize=(8, 12))
fig.add_subplot(2, 1, 1)
dts = utils.get_ncdates(trans)
q_sum = trans.variables['q_sum_rapid'][:]
q_ek = trans.variables['q_ek'][:]
q_fc = trans.variables['q_fc'][:]
q_geoint = trans.variables['q_geoint'][:]
q_eddy = trans.variables['q_eddy'][:]
q_wbw = trans.variables['q_wbw'][:]
q_sum_label = 'Total (%4.2f PW)' % (q_sum.mean())
q_ek_label = 'Ekman (%4.2f PW)' % (q_ek.mean())
q_fc_label = 'Florida current (%4.2f PW)' % (q_fc.mean())
q_geoint_label = 'Geostrophic interior (%4.2f PW)' % (q_geoint.mean())
q_wbw_label = 'WBW (%4.2f PW)' % (q_wbw.mean())
q_eddy_label = 'Eddies (%4.2f PW)' % (q_eddy.mean())
plt.plot(dts, q_sum, linewidth=4, color='k', label=q_sum_label)
plt.plot(dts, q_ek, linewidth=4, color=c1, label=q_ek_label)
plt.plot(dts, q_fc, linewidth=4, color=c2, label=q_fc_label)
plt.plot(dts, q_wbw, linewidth=4, color=c3, label=q_wbw_label)
plt.plot(dts, q_geoint, linewidth=4, color=c4, label=q_geoint_label)
plt.plot(dts, q_eddy, linewidth=4, color='0.5', label=q_eddy_label)
plt.xlabel('Date')
plt.ylim([-4, 4])
plt.ylabel('PW')
plt.title('Heat transports relative to 0C at 26N in %s' % name)
plt.legend(loc=8, fontsize=8, ncol=2)
# Add observational data to sub-axis
if obs is not None:
fig.add_subplot(2, 1, 2)
q_sum_obs_label = 'Total (%4.2f PW)' % (obs.q_sum.mean())
q_ek_obs_label = 'Ekman (%4.2f PW)' % (obs.q_ek.mean())
q_fc_obs_label = 'Florida current (%4.2f PW)' % (obs.q_fc.mean())
q_geoint_obs_label = 'Geostrophic interior (%4.2f PW)' % (
obs.q_geoint.mean())
q_wbw_obs_label = 'WBW (%4.2f PW)' % (obs.q_wbw.mean())
q_eddy_obs_label = 'Eddies (%4.2f PW)' % (obs.q_eddy.mean())
plt.plot(obs.dates,
obs.q_sum,
linewidth=4,
color='k',
label=q_sum_obs_label)
plt.plot(obs.dates,
obs.q_ek,
linewidth=4,
color=c1,
label=q_ek_obs_label)
plt.plot(obs.dates,
obs.q_fc,
linewidth=4,
color=c2,
label=q_fc_obs_label)
plt.plot(obs.dates,
obs.q_wbw,
linewidth=4,
color=c3,
label=q_wbw_obs_label)
plt.plot(obs.dates,
obs.q_geoint,
linewidth=4,
color=c4,
label=q_geoint_obs_label)
plt.plot(obs.dates,
obs.q_eddy,
linewidth=4,
color='0.5',
label=q_eddy_obs_label)
plt.xlabel('Date')
plt.ylim([-4, 4])
plt.ylabel('PW')
plt.title(
'Heat transports relative to 0C at 26N in RAPID observations')
plt.legend(loc=8, fontsize=8, ncol=2)
# Save plot
plt.tight_layout()
savef = basename + 'heat_transports_rapid_decomposition_at_26n.png'
print 'SAVING: %s' % savef
fig.savefig(savef, resolution=300)
plt.close()
def plot_geometric_heat_components(trans,
basename='',
name='simulated',
obs=None):
""" Plot geometric heat transport components """
# Add model data to sub-axis (model v)
fig = plt.figure(figsize=(8, 12))
fig.add_subplot(3, 1, 1)
dts = utils.get_ncdates(trans)
q_sum_model = trans.variables['q_sum_model'][:]
q_gyre_model = trans.variables['q_gyre_model'][:]
q_ot_model = trans.variables['q_ot_model'][:]
q_net_model = trans.variables['q_net_model'][:]
q_sum_model_label = 'Total (%4.2f PW)' % (q_sum_model.mean())
q_gyre_model_label = 'Gyre (%4.2f PW)' % (q_gyre_model.mean())
q_ot_model_label = 'Overturning (%4.2f PW)' % (q_ot_model.mean())
q_net_model_label = 'Net (%4.2f PW)' % (q_net_model.mean())
plt.plot(dts, q_sum_model, linewidth=4, color='k', label=q_sum_model_label)
plt.plot(dts, q_ot_model, linewidth=4, color=c1, label=q_ot_model_label)
plt.plot(dts,
q_gyre_model,
linewidth=4,
color=c2,
label=q_gyre_model_label)
plt.plot(dts, q_net_model, linewidth=4, color=c3, label=q_net_model_label)
plt.xlabel('Date')
plt.ylim([-.5, 2.5])
plt.ylabel('PW')
plt.title('Heat transports at 26N in %s (model velocities)' % name)
plt.legend(loc=8, fontsize=8, ncol=2)
# Add model data to sub-axis (RAPID approx)
fig.add_subplot(3, 1, 2)
q_sum_rapid = trans.variables['q_sum_rapid'][:]
q_gyre_rapid = trans.variables['q_gyre_rapid'][:]
q_ot_rapid = trans.variables['q_ot_rapid'][:]
q_net_rapid = trans.variables['q_net_rapid'][:]
q_sum_rapid_label = 'Total (%4.2f PW)' % (q_sum_rapid.mean())
q_gyre_rapid_label = 'Gyre (%4.2f PW)' % (q_gyre_rapid.mean())
q_ot_rapid_label = 'Overturning (%4.2f PW)' % (q_ot_rapid.mean())
q_net_rapid_label = 'Net (%4.2f PW)' % (q_net_rapid.mean())
plt.plot(dts, q_sum_rapid, linewidth=4, color='k', label=q_sum_rapid_label)
plt.plot(dts, q_ot_rapid, linewidth=4, color=c1, label=q_ot_rapid_label)
plt.plot(dts,
q_gyre_rapid,
linewidth=4,
color=c2,
label=q_gyre_rapid_label)
plt.plot(dts, q_net_rapid, linewidth=4, color=c3, label=q_net_rapid_label)
plt.xlabel('Date')
plt.ylim([-.5, 2.5])
plt.ylabel('PW')
plt.title('Heat transports at 26N in %s (RAPID approx)' % name)
plt.legend(loc=8, fontsize=8, ncol=2)
# Add optional observational data to sub-axis
if obs is not None:
fig.add_subplot(3, 1, 3)
q_sum_obs_label = 'Total (%4.2f PW)' % (obs.q_sum.mean())
q_gyre_obs_label = 'Gyre (%4.2f PW)' % (obs.q_gyre.mean())
q_ot_obs_label = 'Overturning (%4.2f PW)' % (obs.q_ot.mean())
plt.plot(obs.dates,
obs.q_sum,
linewidth=4,
color='k',
label=q_sum_obs_label)
plt.plot(obs.dates,
obs.q_ot,
linewidth=4,
color=c1,
label=q_ot_obs_label)
plt.plot(obs.dates,
obs.q_gyre,
linewidth=4,
color=c2,
label=q_gyre_obs_label)
plt.xlabel('Date')
plt.ylim([-.5, 2.5])
plt.ylabel('PW')
plt.title('Heat transports at 26N in RAPID observations')
plt.legend(loc=8, fontsize=8, ncol=2)
# Save plot
plt.tight_layout()
savef = basename + 'heat_transports_geometric_decomposition_at_26n.png'
print 'SAVING: %s' % savef
fig.savefig(savef, resolution=300)
plt.close()
def plot_streamfunction_hovmollers(trans,
name='simulated',
basename='',
obs=None):
""" Plot overturning stream function hovmoller diagrams"""
# Extract variables from data objects
dts = utils.get_ncdates(trans)
z = trans.variables['depth'][:]
sf_rapid = trans.variables['sf_rapid'][:]
sf_model = trans.variables['sf_model'][:]
# Set up figure
fig = plt.figure(figsize=(8, 12))
cmap = plt.cm.viridis
levels = np.arange(15) * 2 - 4
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
cmin, cmax = -5, 30
# Add model data to axis
fig.add_subplot(3, 1, 1)
plt.pcolormesh(dts,
-z,
sf_model.transpose(),
vmin=cmin,
vmax=cmax,
cmap=cmap,
norm=norm)
plt.colorbar(orientation='vertical')
plt.title('Overturning streamfunction at 26N in %s (model velocities)' %
name)
plt.xlabel('Dates')
plt.ylabel('Depth (m)')
# Add model data to axis (RAPID approx)
fig.add_subplot(3, 1, 2)
plt.pcolormesh(dts,
-z,
sf_rapid.transpose(),
vmin=cmin,
vmax=cmax,
cmap=cmap,
norm=norm)
plt.colorbar(orientation='vertical')
plt.title('Overturning streamfunction at 26N in %s (RAPID approx)' % name)
plt.xlabel('Dates')
plt.ylabel('Depth (m)')
# Add optional observed data to axis
if obs is not None:
fig.add_subplot(3, 1, 3)
plt.pcolormesh(obs.dates,
-obs.z,
obs.sf.transpose(),
vmin=cmin,
vmax=cmax,
cmap=cmap,
norm=norm)
plt.colorbar(orientation='vertical')
plt.title('Overturning streamfunction at 26N from RAPID array')
plt.xlabel('Dates')
plt.ylabel('Depth (m)')
# Save plot
plt.tight_layout()
savef = basename + 'overturning_streamfunction_at_26n_hovmoller.png'
print 'SAVING: %s' % savef
fig.savefig(savef, resolution=300)
plt.close()
def plot_vol_vs_heat_transports(trans,
basename='',
name='simulated',
obs_vol=None,
obs_oht=None):
""" Plot total overturning vs geometric heat transports """
# Extract model variables from data objects
dts = utils.get_ncdates(trans)
fc = trans.variables['fc'][:]
ek = trans.variables['ekman'][:]
umo = trans.variables['umo'][:]
q_ek = trans.variables['q_ek'][:]
q_fc = trans.variables['q_fc'][:]
q_mo = trans.variables['q_mo'][:]
q_fc_lin, q_fc_label = linreg_vol_vs_oht(fc, q_fc)
q_ek_lin, q_ek_label = linreg_vol_vs_oht(ek, q_ek)
q_mo_lin, q_mo_label = linreg_vol_vs_oht(umo, q_mo)
# Extract obs variables from data objects
if (obs_vol is not None) and (obs_oht is not None):
mindt, maxdt = utils.get_daterange(obs_vol.dates, obs_oht.dates)
vol_ind = utils.get_dateind(obs_vol.dates, mindt, maxdt)
oht_ind = utils.get_dateind(obs_oht.dates, mindt, maxdt)
ek_obs = obs_vol.ekman[vol_ind]
fc_obs = obs_vol.fc[vol_ind]
umo_obs = obs_vol.umo[vol_ind]
q_ek_obs = obs_oht.q_ek[oht_ind]
q_fc_obs = obs_oht.q_fc[oht_ind]
q_mo_obs = obs_oht.q_mo[oht_ind]
q_fc_obs_lin, q_fc_obs_label = linreg_vol_vs_oht(fc_obs, q_fc_obs)
q_ek_obs_lin, q_ek_obs_label = linreg_vol_vs_oht(ek_obs, q_ek_obs)
q_mo_obs_lin, q_mo_obs_label = linreg_vol_vs_oht(umo_obs, q_mo_obs)
# Plot ekman
fig = plt.figure(figsize=(15, 5))
fig.add_subplot(1, 3, 1)
plt.plot(ek, q_ek, 'x', color=c1)
plt.plot(ek, q_ek_lin, '-', color=c1, label='%s (%s)' % (name, q_ek_label))
if (obs_vol is not None) and (obs_oht is not None):
plt.plot(ek_obs, q_ek_obs, 'x', color='k')
plt.plot(ek_obs,
q_ek_obs_lin,
'-',
color='k',
label='RAPID observations (%s)' % q_ek_obs_label)
plt.xlabel('Volume transport (Sv)')
plt.ylabel('Heat transport (PW)')
plt.title('Ekman volume vs heat transport')
plt.legend(loc='best', fontsize=8)
# Plot florida current
fig.add_subplot(1, 3, 2)
plt.plot(fc, q_fc, 'x', color=c1)
plt.plot(fc, q_fc_lin, '-', color=c1, label='%s (%s)' % (name, q_fc_label))
if (obs_vol is not None) and (obs_oht is not None):
plt.plot(fc_obs, q_fc_obs, 'x', color='k')
plt.plot(fc_obs,
q_fc_obs_lin,
'-',
color='k',
label='RAPID observations (%s)' % q_fc_obs_label)
plt.xlabel('Volume transport (Sv)')
plt.ylabel('Heat transport (PW)')
plt.title('Florida current volume vs heat transport')
plt.legend(loc='best', fontsize=8)
# Plot mid-ocean
fig.add_subplot(1, 3, 3)
plt.plot(umo, q_mo, 'x', color=c1)
plt.plot(umo,
q_mo_lin,
'-',
color=c1,
label='%s (%s)' % (name, q_mo_label))
if (obs_vol is not None) and (obs_oht is not None):
plt.plot(umo_obs, q_mo_obs, 'x', color='k')
plt.plot(umo_obs,
q_mo_obs_lin,
'-',
color='k',
label='RAPID observations (%s)' % q_mo_obs_label)
plt.xlabel('Volume transport (Sv)')
plt.ylabel('Heat transport (PW)')
plt.title('Mid-ocean volume vs heat transport')
plt.legend(loc='best', fontsize=8)
# Save plot
plt.tight_layout()
savef = basename + 'volume_vs_heat_transports_at_26n.png'
print 'SAVING: %s' % savef
fig.savefig(savef, resolution=300)
plt.close()
def plot_moc_vs_oht(trans,
basename='',
name='simulated',
obs_vol=None,
obs_oht=None):
""" Plot total overturning vs geometric heat transports """
# Add model data to axis (model v)
fig = plt.figure(figsize=(15, 5))
fig.add_subplot(1, 3, 1)
dts = utils.get_ncdates(trans)
moc_model = trans.variables['moc_rapid'][:]
q_sum_model = trans.variables['q_sum_model'][:]
q_gyre_model = trans.variables['q_gyre_model'][:]
q_ot_model = trans.variables['q_ot_model'][:]
q_sum_model_lin, q_sum_model_label = linreg_vol_vs_oht(
moc_model, q_sum_model)
q_gyre_model_lin, q_gyre_model_label = linreg_vol_vs_oht(
moc_model, q_gyre_model)
q_ot_model_lin, q_ot_model_label = linreg_vol_vs_oht(moc_model, q_ot_model)
plt.plot(moc_model, q_sum_model, 'x', color='k')
plt.plot(moc_model,
q_sum_model_lin,
'-',
color='k',
label='total (%s)' % q_sum_model_label)
plt.plot(moc_model, q_ot_model, 'x', color=c1)
plt.plot(moc_model,
q_ot_model_lin,
'-',
color=c1,
label='overturning (%s)' % q_ot_model_label)
plt.plot(moc_model, q_gyre_model, 'x', color=c2)
plt.plot(moc_model,
q_gyre_model_lin,
'-',
color=c2,
label='gyre (%s)' % q_gyre_model_label)
plt.xlabel('MOC (Sv)')
plt.ylabel('Heat transport (PW)')
plt.title('MOC vs OHT in %s (model velocities)' % name)
plt.legend(
loc='best',
fontsize=8,
)
# Add model data to axis (RAPID approx)
fig.add_subplot(1, 3, 2)
moc_rapid = trans.variables['moc_rapid'][:]
q_sum_rapid = trans.variables['q_sum_rapid'][:]
q_gyre_rapid = trans.variables['q_gyre_rapid'][:]
q_ot_rapid = trans.variables['q_ot_rapid'][:]
q_sum_rapid_lin, q_sum_rapid_label = linreg_vol_vs_oht(
moc_rapid, q_sum_rapid)
q_gyre_rapid_lin, q_gyre_rapid_label = linreg_vol_vs_oht(
moc_rapid, q_gyre_rapid)
q_ot_rapid_lin, q_ot_rapid_label = linreg_vol_vs_oht(moc_rapid, q_ot_rapid)
plt.plot(moc_rapid, q_sum_rapid, 'x', color='k')
plt.plot(moc_rapid,
q_sum_rapid_lin,
'-',
color='k',
label='total (%s)' % q_sum_rapid_label)
plt.plot(moc_rapid, q_ot_rapid, 'x', color=c1)
plt.plot(moc_rapid,
q_ot_rapid_lin,
'-',
color=c1,
label='overturning (%s)' % q_ot_rapid_label)
plt.plot(moc_rapid, q_gyre_rapid, 'x', color=c2)
plt.plot(moc_rapid,
q_gyre_rapid_lin,
'-',
color=c2,
label='gyre (%s)' % q_gyre_rapid_label)
plt.xlabel('MOC (Sv)')
plt.ylabel('Heat transport (PW)')
plt.title('MOC vs OHT in %s (RAPID approx)' % name)
plt.legend(
loc='best',
fontsize=8,
)
# Add optional obs data to axis
if (obs_oht is not None) and (obs_vol is not None):
fig.add_subplot(1, 3, 3)
mindt, maxdt = utils.get_daterange(obs_vol.dates, obs_oht.dates)
vol_ind = utils.get_dateind(obs_vol.dates, mindt, maxdt)
oht_ind = utils.get_dateind(obs_oht.dates, mindt, maxdt)
q_sum_obs = obs_oht.q_sum[oht_ind]
q_gyre_obs = obs_oht.q_gyre[oht_ind]
q_ot_obs = obs_oht.q_ot[oht_ind]
moc_obs = obs_vol.moc[vol_ind]
q_sum_obs_lin, q_sum_obs_label = linreg_vol_vs_oht(moc_obs, q_sum_obs)
q_gyre_obs_lin, q_gyre_obs_label = linreg_vol_vs_oht(
moc_obs, q_gyre_obs)
q_ot_obs_lin, q_ot_obs_label = linreg_vol_vs_oht(moc_obs, q_ot_obs)
plt.plot(moc_obs, q_sum_obs, 'x', color='k')
plt.plot(moc_obs,
q_sum_obs_lin,
'-',
color='k',
label='total (%s)' % q_sum_obs_label)
plt.plot(moc_obs, q_ot_obs, 'x', color=c1)
plt.plot(moc_obs,
q_ot_obs_lin,
'-',
color=c1,
label='overturning (%s)' % q_ot_obs_label)
plt.plot(moc_obs, q_gyre_obs, 'x', color=c2)
plt.plot(moc_obs,
q_gyre_obs_lin,
'-',
color=c2,
label='gyre (%s)' % q_gyre_obs_label)
plt.xlabel('MOC (Sv)')
plt.ylabel('Heat transport (PW)')
plt.title('MOC vs OHT in RAPID observations')
plt.legend(
loc='best',
fontsize=8,
)
# Save plot
plt.tight_layout()
savef = basename + 'moc_vs_heat_transports_at_26n.png'
print 'SAVING: %s' % savef
fig.savefig(savef, resolution=300)
plt.close()
def plot_volume_components(trans,
basename='',
name='simulated',
obs_vol=None,
obs_fc=None):
""" Plot volume transport component time series """
# Add model data to sub-axis
fig = plt.figure(figsize=(8, 12))
fig.add_subplot(2, 1, 1)
dts = utils.get_ncdates(trans)
fc = trans.variables['fc'][:]
ekman = trans.variables['ekman'][:]
umo = trans.variables['umo'][:]
moc = trans.variables['moc_rapid'][:]
fc_label = 'Florida current (%6.1f Sv)' % (fc.mean())
ek_label = 'Ekman transport (%6.1f Sv)' % (ekman.mean())
umo_label = 'Upper-mid ocean (%4.1f Sv)' % (umo.mean())
moc_label = 'MOC (%6.1f Sv)' % (moc.mean())
plt.plot(dts, fc, linewidth=4, color=c1, label=fc_label)
plt.plot(dts, ekman, linewidth=4, color=c2, label=ek_label)
plt.plot(dts, umo, linewidth=4, color=c3, label=umo_label)
plt.plot(dts, moc, linewidth=4, color='k', label=moc_label)
plt.xlabel('Date')
plt.ylim([-50, 50])
plt.ylabel('Sverdrups')
plt.title('Overturning components at 26N in %s' % name)
plt.legend(loc=8, fontsize=8, ncol=2)
# Add optional observational data to sub-axis
if (obs_vol is not None) or (obs_fc is not None):
fig.add_subplot(2, 1, 2)
if obs_vol is not None:
fc_obs_label = 'Florida current (%6.1f Sv)' % (obs_vol.fc.mean())
ek_obs_label = 'Ekman transport (%6.1f Sv)' % (
obs_vol.ekman.mean())
umo_obs_label = 'Upper-mid ocean (%4.1f Sv)' % (obs_vol.umo.mean())
moc_obs_label = 'MOC (%6.1f Sv)' % (obs_vol.moc.mean())
plt.plot(obs_vol.dates,
obs_vol.fc,
linewidth=4,
color=c1,
label=fc_obs_label)
plt.plot(obs_vol.dates,
obs_vol.ekman,
linewidth=4,
color=c2,
label=ek_obs_label)
plt.plot(obs_vol.dates,
obs_vol.umo,
linewidth=4,
color=c3,
label=umo_obs_label)
plt.plot(obs_vol.dates,
obs_vol.moc,
linewidth=4,
color='k',
label=moc_obs_label)
if obs_fc is not None:
fc_obs_ext_label = 'FC - extended (%6.1f Sv)' % (obs_fc.fc.mean())
plt.plot(obs_fc.dates,
obs_fc.fc,
linewidth=4,
color=c4,
label=fc_obs_ext_label)
plt.xlabel('Date')
plt.ylim([-50, 50])
plt.ylabel('Sverdrups')
plt.title('Overturning components at 26N in RAPID observations')
plt.legend(loc=8, fontsize=8, ncol=2)
# Save plot
plt.tight_layout()
savef = basename + 'volume_transport_components_at_26n.png'
print 'SAVING: %s' % savef
fig.savefig(savef, resolution=300)
plt.close()
def plot_moc_vs_oft(trans,
basename='',
name='simulated',
obs_vol=None,
obs_oft=None):
""" Plot total overturning vs geometric freshwater transports """
# Add model data to axis (model v)
fig = plt.figure(figsize=(15, 5))
fig.add_subplot(1, 3, 1)
dts = utils.get_ncdates(trans)
moc_model = trans.variables['moc_model'][:]
fw_sum_model = trans.variables['fw_sum_model'][:]
fw_gyre_model = trans.variables['fw_gyre_model'][:]
fw_ot_model = trans.variables['fw_ot_model'][:]
fw_sum_model_lin, fw_sum_model_label = linreg(moc_model,
fw_sum_model,
units='Sv/Sv')
fw_gyre_model_lin, fw_gyre_model_label = linreg(moc_model,
fw_gyre_model,
units='Sv/Sv')
fw_ot_model_lin, fw_ot_model_label = linreg(moc_model,
fw_ot_model,
units='Sv/Sv')
plt.plot(moc_model,
fw_sum_model,
'x',
color='k',
label='total %s' % fw_sum_model_label)
plt.plot(moc_model,
fw_ot_model,
'x',
color=c1,
label='overturning %s' % fw_ot_model_label)
plt.plot(moc_model,
fw_gyre_model,
'x',
color=c2,
label='gyre %s' % fw_gyre_model_label)
if fw_sum_model_lin is not None:
plt.plot(moc_model, fw_sum_model_lin, '-', color='k')
plt.plot(moc_model, fw_ot_model_lin, '-', color=c1)
plt.plot(moc_model, fw_gyre_model_lin, '-', color=c2)
plt.xlabel('MOC (Sv)')
plt.ylabel('Equivalent freshwater transport (Sv)')
plt.title('MOC vs FWT in %s (model velocities)' % name)
plt.legend(
loc='best',
fontsize=8,
)
# Add model data to axis (RAPID approx)
fig.add_subplot(1, 3, 2)
moc_rapid = trans.variables['moc_rapid'][:]
fw_sum_rapid = trans.variables['fw_sum_rapid'][:]
fw_gyre_rapid = trans.variables['fw_gyre_rapid'][:]
fw_ot_rapid = trans.variables['fw_ot_rapid'][:]
fw_sum_rapid_lin, fw_sum_rapid_label = linreg(moc_rapid,
fw_sum_rapid,
units='Sv/Sv')
fw_gyre_rapid_lin, fw_gyre_rapid_label = linreg(moc_rapid,
fw_gyre_rapid,
units='Sv/Sv')
fw_ot_rapid_lin, fw_ot_rapid_label = linreg(moc_rapid,
fw_ot_rapid,
units='Sv/Sv')
plt.plot(moc_rapid,
fw_sum_rapid,
'x',
color='k',
label='total %s' % fw_sum_rapid_label)
plt.plot(moc_rapid,
fw_ot_rapid,
'x',
color=c1,
label='overturning %s' % fw_ot_rapid_label)
plt.plot(moc_rapid,
fw_gyre_rapid,
'x',
color=c2,
label='gyre %s' % fw_gyre_rapid_label)
if fw_sum_rapid_lin is not None:
plt.plot(moc_rapid, fw_sum_rapid_lin, '-', color='k')
plt.plot(moc_rapid, fw_ot_rapid_lin, '-', color=c1)
plt.plot(moc_rapid, fw_gyre_rapid_lin, '-', color=c2)
plt.xlabel('MOC (Sv)')
plt.ylabel('Equivalent freshwater transport (Sv)')
plt.title('MOC vs OFT in %s (RAPID approx)' % name)
plt.legend(
loc='best',
fontsize=8,
)
# Add optional obs data to axis
if (obs_oft is not None) and (obs_vol is not None):
fig.add_subplot(1, 3, 3)
mindt, maxdt = utils.get_daterange(obs_vol.dates, obs_oft.dates)
vol_ind = utils.get_dateind(obs_vol.dates, mindt, maxdt)
oft_ind = utils.get_dateind(obs_oft.dates, mindt, maxdt)
fw_sum_obs = obs_oft.fw_sum[oft_ind]
fw_gyre_obs = obs_oft.fw_gyre[oft_ind]
fw_ot_obs = obs_oft.fw_ot[oft_ind]
moc_obs = obs_vol.moc[vol_ind]
fw_sum_obs_lin, fw_sum_obs_label = linreg(moc_obs,
fw_sum_obs,
units='Sv/Sv')
fw_gyre_obs_lin, fw_gyre_obs_label = linreg(moc_obs,
fw_gyre_obs,
units='Sv/Sv')
fw_ot_obs_lin, fw_ot_obs_label = linreg(moc_obs,
fw_ot_obs,
units='Sv/Sv')
plt.plot(moc_obs,
fw_sum_obs,
'x',
color='k',
label='total %s' % fw_sum_obs_label)
plt.plot(moc_obs,
fw_ot_obs,
'x',
color=c1,
label='overturning %s' % fw_ot_obs_label)
plt.plot(moc_obs,
fw_gyre_obs,
'x',
color=c2,
label='gyre %s' % fw_gyre_obs_label)
if fw_sum_obs_lin is not None:
plt.plot(moc_obs, fw_sum_obs_lin, '-', color='k')
plt.plot(moc_obs, fw_ot_obs_lin, '-', color=c1)
plt.plot(moc_obs, fw_gyre_obs_lin, '-', color=c2)
plt.xlabel('MOC (Sv)')
plt.ylabel('Equivalent freshwater transport (Sv)')
plt.title('MOC vs OFT in RAPID observations')
plt.legend(
loc='best',
fontsize=8,
)
# Save plot
plt.tight_layout()
savef = basename + 'moc_vs_freshwater_transports_at_26n.png'
print 'SAVING: %s' % savef
fig.savefig(savef, resolution=300)
plt.close()
def plot_vol_vs_fw_transports(trans,
basename='',
name='simulated',
obs_vol=None,
obs_oft=None):
""" Plot volume vs freshwater transport for RAPID components """
# Extract model variables from data objects
dts = utils.get_ncdates(trans)
fc = trans.variables['fc'][:]
ek = trans.variables['ekman'][:]
umo = trans.variables['umo'][:]
fw_ek = trans.variables['fw_ek'][:]
fw_fc = trans.variables['fw_fc'][:]
fw_mo = trans.variables['fw_mo'][:]
fw_fc_lin, fw_fc_label = linreg(fc, fw_fc, units='Sv/Sv')
fw_ek_lin, fw_ek_label = linreg(ek, fw_ek, units='Sv/Sv')
fw_mo_lin, fw_mo_label = linreg(umo, fw_mo, units='Sv/Sv')
# Extract obs variables from data objects
if (obs_vol is not None) and (obs_oft is not None):
mindt, maxdt = utils.get_daterange(obs_vol.dates, obs_oft.dates)
vol_ind = utils.get_dateind(obs_vol.dates, mindt, maxdt)
oft_ind = utils.get_dateind(obs_oft.dates, mindt, maxdt)
ek_obs = obs_vol.ekman[vol_ind]
fc_obs = obs_vol.fc[vol_ind]
umo_obs = obs_vol.umo[vol_ind]
fw_ek_obs = obs_oft.fw_ek[oft_ind]
fw_fc_obs = obs_oft.fw_fc[oft_ind]
fw_mo_obs = obs_oft.fw_mo[oft_ind]
fw_fc_obs_lin, fw_fc_obs_label = linreg(fc_obs,
fw_fc_obs,
units='Sv/Sv')
fw_ek_obs_lin, fw_ek_obs_label = linreg(ek_obs,
fw_ek_obs,
units='Sv/Sv')
fw_mo_obs_lin, fw_mo_obs_label = linreg(umo_obs,
fw_mo_obs,
units='Sv/Sv')
# Plot ekman
fig = plt.figure(figsize=(15, 5))
fig.add_subplot(1, 3, 1)
plt.plot(ek, fw_ek, 'x', color=c1, label='%s %s' % (name, fw_ek_label))
if fw_ek_lin is not None:
plt.plot(ek, fw_ek_lin, '-', color=c1)
if (obs_vol is not None) and (obs_oft is not None):
plt.plot(ek_obs,
fw_ek_obs,
'x',
color='k',
label='RAPID observations %s' % fw_ek_obs_label)
if fw_ek_obs_lin is not None:
plt.plot(ek_obs, fw_ek_obs_lin, '-', color='k')
plt.xlabel('Volume transport (Sv)')
plt.ylabel('Equivalent freshwater transport (Sv)')
plt.title('Ekman volume vs freshwater transport')
plt.legend(loc='best', fontsize=8)
# Plot florida current
fig.add_subplot(1, 3, 2)
plt.plot(fc, fw_fc, 'x', color=c1, label='%s %s' % (name, fw_fc_label))
if fw_fc_lin is not None:
plt.plot(fc, fw_fc_lin, '-', color=c1)
if (obs_vol is not None) and (obs_oft is not None):
plt.plot(fc_obs,
fw_fc_obs,
'x',
color='k',
label='RAPID observations %s' % fw_fc_obs_label)
if fw_fc_obs_lin is not None:
plt.plot(fc_obs, fw_fc_obs_lin, '-', color='k')
plt.xlabel('Volume transport (Sv)')
plt.ylabel('Equivalent freshwater transport (Sv)')
plt.title('Florida Current volume vs freshwater transport')
plt.legend(loc='best', fontsize=8)
# Plot mid-ocean
fig.add_subplot(1, 3, 3)
plt.plot(umo, fw_mo, 'x', color=c1, label='%s %s' % (name, fw_mo_label))
if fw_mo_lin is not None:
plt.plot(umo, fw_mo_lin, '-', color=c1)
if (obs_vol is not None) and (obs_oft is not None):
plt.plot(umo_obs,
fw_mo_obs,
'x',
color='k',
label='RAPID observations %s' % fw_mo_obs_label)
if fw_mo_obs_lin is not None:
plt.plot(umo_obs, fw_mo_obs_lin, '-', color='k')
plt.xlabel('Volume transport (Sv)')
plt.ylabel('Equivalent freshwater transport (Sv)')
plt.title('Mid-ocean volume vs freshwater transport')
plt.legend(loc='best', fontsize=8)
# Save plot
plt.tight_layout()
savef = basename + 'volume_vs_freshwater_transports_at_26n.png'
print 'SAVING: %s' % savef
fig.savefig(savef, resolution=300)
plt.close()