def main(variable):
# RMS errors
cubes = second_analysis.get_data('rms_error_24_air_pressure', 'full')
plot_errors(cubes, variable, '-kx')
cubes = second_analysis.get_data('mean_error_24_air_pressure', 'full')
plot_errors(cubes, variable, '--kx')
def profile(coord, mappings, domains, title, xlabel, ylabel, xlims, ylims):
ncols = len(mappings)
nrows = len(domains)
# Initialise the plot
fig = plt.figure(figsize=(18, 25))
# Loop over mappings
for m, domain in enumerate(domains):
cubes = second_analysis.get_data(coord, domain)
for n, mapping in enumerate(mappings):
mapping = second_analysis.mappings[mapping]
ax = plt.subplot2grid((nrows, ncols), (m, n))
profile_multi(cubes, ax, mapping, coord)
ax.set_xlim(*xlims[n])
ax.set_ylim(*ylims)
if m == 0:
ax.set_title(title[n])
else:
ax.set_title('')
if m == nrows - 1:
legend(ax,
key=second_analysis.get_idx,
loc='upper left',
ncol=2,
bbox_to_anchor=(0.05, -0.25))
else:
ax.get_xaxis().set_ticklabels([])
if n == 0:
if m == 1:
ax.set_ylabel(ylabel)
else:
ax.set_ylabel('')
else:
ax.set_ylabel('')
ax.get_yaxis().set_ticklabels([])
if m == nrows - 1 and n == 1:
ax.set_xlabel(xlabel)
else:
ax.set_xlabel('')
ax.axvline(color='k')
ax.axhline(color='k')
if coord == 'air_pressure':
ax.set_ylim(ax.get_ylim()[::-1])
multilabel(ax, n + m * ncols)
fig.subplots_adjust(bottom=0.4)
return
def _single_fit(coord, subdomain, dz, function, **kwargs):
cubes = second_analysis.get_data(coord, subdomain)
pv = convert.calc(variable, cubes)
x, y, dy = function(pv, coord, dz)
popt, pcov, rmse = fit_curve(x, y, dy)
print(popt, rmse)
plot_fit(x, y, dy, popt, **kwargs)
return popt, rmse
def tropopause_profile():
# Initialise the plot
fig = plt.figure(figsize=(18, 15))
# Loop over mappings
for n, mapping in enumerate(mappings):
mapping = second_analysis.mappings[mapping]
for m, domain in enumerate(['ridges', 'troughs']):
cubes = second_analysis.get_data(coord, domain)
ax = plt.subplot2grid((2, ncols), (m, n))
if n == 0:
profile_error(cubes, ax, mapping, coord)
else:
profile_multi(cubes, ax, mapping, coord)
plt.title(title[n])
ax.set_xlim(*xlims[n])
ax.set_ylim(*ylim)
legend(ax,
key=second_analysis.get_idx,
loc='best',
ncol=2,
bbox_to_anchor=(0.9, -0.2))
if n == 0:
ax.set_ylabel(ylabel)
else:
ax.set_ylabel('')
ax.get_yaxis().set_ticklabels([])
if n == 1:
ax.set_xlabel(xlabel)
else:
ax.set_xlabel('')
plt.title('')
plt.axvline(color='k')
plt.axhline(color='k')
# if coord == 'air_pressure':
# ax.set_ylim(ax.get_ylim()[::-1])
for n, ax in enumerate(fig.axes):
multilabel(ax, n)
fig.subplots_adjust(bottom=0.4)
#plt.savefig(plotdir + 'ch7_low/height_profile.pdf')
plt.show()
return
def pv_gradients(coord, alpha, fig):
# Rows are Ridges and troughs
for n, subdomain in enumerate(['ridges', 'troughs']):
cubes = second_analysis.get_data(coord, subdomain)
# Columns are for different mappings
for m, mapping in enumerate(['pv_full', 'pv_main', 'pv_phys']):
mapping = second_analysis.mappings[mapping]
ax = fig.axes[n * 3 + m]
pv_gradients_multi(cubes, coord, ax, mapping, alpha)
return
def make_table():
for coord in ([
'distance_from_dynamical_tropopause',
'distance_from_advection_only_tropopause'
]):
for subdomain in (['ridges', 'troughs']):
cubes = second_analysis.get_data(coord, subdomain)
pv = convert.calc(variable, cubes)[:, 1:]
x, y, dy = pv_contrast(pv, coord, 1000)
popt, pcov, rmse = fit_curve(x, y, dy)
print(subdomain.capitalize() + ' & ' + str(round(popt[0], 1)) +
' & ' + str(round(popt[1], 3)) + ' & ' +
str(round(popt[2], 3)) + ' & ' + str(round(rmse, 3)))
return
def main(**kwargs):
# Initialise the plot
fig = plt.figure(figsize=(18, 15))
for n, coord in enumerate(coords):
for m, subdomain in enumerate(['ridges', 'troughs']):
cubes = second_analysis.get_data(coord, subdomain)
cube = convert.calc(name, cubes)
cube.coord(coord).convert_units('km')
mean, std_err = second_analysis.extract_statistics(
cube, 'forecast_index')
ax = plt.subplot2grid((2, 2), (n, m))
im = iplt.contourf(mean, even_cscale(0.18), cmap='coolwarm')
# X-axis - Same for both columns
ax.set_xticks([0, 12, 24, 36, 48, 60])
if n == 0:
ax.get_xaxis().set_ticklabels([])
ax.set_title(subdomain.capitalize())
# Y-axis - Same for both rows
ax.set_ylim(-2, 2)
ax.set_yticks([-2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2])
ax.axhline(color='k')
if m > 0:
ax.get_yaxis().set_ticklabels([])
add_labels(fig)
fig.text(0.5, 0.2, 'Forecast Lead Time (hours)', ha='center')
for n, axis in enumerate(fig.axes):
multilabel(axis, n)
cbar = plt.colorbar(im,
ax=fig.axes,
orientation='horizontal',
fraction=0.05,
spacing='proportional')
cbar.set_ticks([-0.18, -0.09, 0, 0.09, 0.18])
cbar.set_label('PVU')
plt.savefig(plotdir + 'inconsistency.pdf')
plt.show()
return
def tropopause_profile(coord, alpha, fig):
# Columns are Ridges and troughs
for n, subdomain in enumerate(['ridges', 'troughs']):
cubes = second_analysis.get_data(coord, subdomain)
# Rows are for different mappings
for m, mapping in enumerate(['pv_full', 'pv_main', 'pv_phys']):
mapping = second_analysis.mappings[mapping]
ax = fig.axes[n * 3 + m]
if m == 0:
# First column is forecast error
profile_error(cubes, coord, ax, mapping, alpha)
else:
# Columns 2 and 3 show full accumulation
profile_multi(cubes, coord, ax, mapping, alpha)
return
def varying_depth(widths, function):
fig = plt.figure(figsize=(18, 8))
axes = []
for n in range(1):
for m in range(2):
axes.append(plt.subplot2grid((1, 2), (n, m)))
for coord, color in coords:
for subdomain, linestyle in subdomains:
cubes = second_analysis.get_data(coord, subdomain)
pv = convert.calc(variable, cubes)
if subdomain == 'ridges':
x, y, dy = pv_contrast(pv, coord, 1000)
popt, pcov, rmse = fit_curve(x, y, dy)
axes[0].errorbar(x,
y,
yerr=dy,
linestyle=linestyle,
color=color)
pv = pv[:, 1:]
if subdomain == 'ridges':
x2, y, dy = pv_contrast(pv, coord, 1000)
popt, pcov, rmse = fit_curve(x2, y, dy)
axes[0].plot(x,
func(x, *popt),
color=color,
linestyle='',
marker='o')
timescale = []
grad_0 = []
grad_inf = []
errors = []
for dz in widths:
if dz == 200:
x, y, dy = pv_gradient(pv, coord, dz)
else:
x, y, dy = function(pv, coord, dz)
popt, pcov, rmse = fit_curve(x, y, dy)
timescale.append(popt[0])
grad_0.append(popt[1])
grad_inf.append(popt[2])
errors.append(rmse)
grad_0 = np.array(grad_0)
grad_inf = np.array(grad_inf)
# axes[2].plot(widths * 1e-3, grad_0,
# color=color, linestyle=linestyle, marker='x')
# axes[3].plot(widths * 1e-3, grad_0 - grad_inf,
# color=color, linestyle=linestyle, marker='x')
axes[1].plot(widths * 1e-3,
timescale,
color=color,
linestyle=linestyle,
marker='x')
# axes[5].plot(widths * 1e-3, errors,
# color=color, linestyle=linestyle, marker='x')
# Set figure labels
axes[0].set_title(r'$\Delta q_{adv}(t)$ in ridges')
axes[0].set_xlabel('Forecast Lead Time (hours)')
axes[0].set_ylabel('PVU')
axes[1].set_title('Timescale')
axes[1].set_xlabel(r'$\pm \tilde{z}$ (km)', fontsize=20)
axes[1].set_ylabel(r'$\tau$ (Hours)')
"""
axes[2].set_title(r'$\Delta q_{adv}(0)$')
axes[2].set_xlabel(r'$\pm \tilde{z}$ (km)', fontsize=20)
axes[2].set_ylabel('PVU')
#axes[2].set_ylim(0, 5)
axes[3].set_title(r'$\Delta q_{adv}(0) - \Delta q_{adv}(\infty)$')
axes[3].set_xlabel(r'$\pm \tilde{z}$ (km)', fontsize=20)
axes[3].set_ylabel('PVU')
#axes[3].set_ylim(0, 5)
"""
for n, axis in enumerate(axes):
multilabel(axis, n)
# Create legend table
fig2 = plt.figure()
ax = fig2.add_subplot(111)
lines = []
for subdomain, linestyle in subdomains:
for coord, color in coords:
lines.append(
ax.plot([0, 1], [0, 1], linestyle=linestyle, color=color)[0])
# create blank rectangle
extra = Rectangle((0, 0),
1,
1,
fc="w",
fill=False,
edgecolor='none',
linewidth=0)
# Create organized list containing all handles for table. Extra represent
# empty space
legend_handle = [
extra, extra, extra, extra, lines[0], lines[1], extra, lines[2],
lines[3]
]
# Define the labels
label_column_1 = ["", r"$z(q{=}2)$", r"$z(q_{adv}{=}2)$"]
label_column_2 = ["Ridges", "", ""]
label_column_3 = ["Troughs", "", ""]
# organize labels for table construction
legend_labels = np.concatenate(
[label_column_1, label_column_2, label_column_3])
# Create legend
axes[1].legend(legend_handle,
legend_labels,
loc='best',
ncol=3,
shadow=True,
handletextpad=-2)
fig.savefig(plotdir + 'q_adv_decay_parameters.pdf')
# plt.show()
return
def humidity_gradients():
# Initialise the plot
fig = plt.figure(figsize=(18, 15))
# Columns are Ridges and troughs
for n, variable in enumerate(variables):
row = n / ncols
col = n - row * ncols
print(row, col)
ax = plt.subplot2grid((nrows, ncols), (row, col))
for subdomain, linestyle in [('ridges', '-'), ('troughs', '--')]:
cubes = second_analysis.get_data(coord, subdomain)
cube = convert.calc(variable, cubes)
cube.coord(coord).convert_units('km')
mean, std_err = second_analysis.extract_statistics(
cube, 'forecast_index')
if variable == 'vertical_vorticity':
mean.data = mean.data + 1e-4
else:
mean.data = mean.data * 1e3
std_err.data = std_err.data * 1e3
iplt.plot(
mean[0],
mean.coord(coord), # xerr=std_err[0],
linestyle=linestyle,
label=subdomain.capitalize(),
color='k',
marker='x',
ms=5)
ax.set_ylabel('')
ax.set_ylim(-2, 2)
if col > 0:
ax.get_yaxis().set_ticklabels([])
if variable == 'specific_humidity':
ax.set_title('Specific Humidity')
ax.set_xlabel(r'Mass Fraction (g kg$^{-1}$)')
elif variable == 'vertical_vorticity':
ax.set_title('Vertical Vorticity')
ax.set_xlabel(r'Vorticity (s$^{-1}$)')
elif variable == 'mass_fraction_of_cloud_liquid_water_in_air':
ax.set_title('Cloud Liquid')
ax.set_xlabel(r'Mass Fraction (g kg$^{-1}$)')
elif variable == 'mass_fraction_of_cloud_ice_in_air':
ax.set_title('Cloud Ice')
ax.set_xlabel(r'Mass Fraction (g kg$^{-1}$)')
plt.axhline(color='k')
multilabel(ax, n)
legend(ax=fig.axes[0], loc='best')
fig.text(0.075,
0.5,
'Vertical distance from tropopause (km)',
va='center',
rotation='vertical')
plt.savefig(plotdir + 'analysis_profiles.pdf')
plt.show()
def main():
# Initialise the plot
fig = plt.figure(figsize=(18, 12))
# Add subfigures
for n in range(2):
for m in range(3):
plt.subplot2grid((2, 3), (n, m))
# Plot composites
pv_gradients('distance_from_dynamical_tropopause', 1, fig)
# Add faint lines for q_adv
#pv_gradients('distance_from_advection_only_tropopause', 0.25, fig)
for n, subdomain in enumerate(['ridges', 'troughs']):
coord = 'distance_from_advection_only_tropopause'
alpha = 0.3
cubes = second_analysis.get_data(coord, subdomain)
m = 1
mapping = second_analysis.mappings['pv_main']
mapping = {
k: mapping[k]
for k in ('dynamics_tracer_inconsistency',
'sum_of_physics_pv_tracers')
}
ax = fig.axes[n * 3 + m]
pv_gradients_multi(cubes, coord, ax, mapping, alpha)
fig.subplots_adjust(bottom=0.2)
# Set labels and limits on plots
for n, subdomain in enumerate(['ridges', 'troughs']):
for m in range(3):
ax = fig.axes[n * 3 + m]
# X-axis - Same for both rows
ax.set_xticks([0, 12, 24, 36, 48, 60])
if n == 0:
ax.get_xaxis().set_ticklabels([])
# Set Titles
if m == 0:
ax.set_title('Forecast')
elif m == 1:
ax.set_title('PV budget')
elif m == 2:
ax.set_title('Physics PV tracers')
else:
legend(ax,
key=second_analysis.get_idx,
loc='best',
ncol=2,
bbox_to_anchor=(1.0, -0.2),
fontsize=25)
if m == 1:
ax.set_xlabel('Forecast lead time (hours)')
# Y-Axis
if m == 0:
# First column custom
if n == 0:
ax.set_ylim(3.0, 3.6)
else:
ax.set_ylim(2.4, 3.0)
elif m == 1:
# Columns 2
ax.set_ylim(-0.1, 0.5)
else:
ax.set_ylim(-0.05, 0.25)
multilabel(ax, n * 3 + m)
fig.text(0.075,
0.55,
'PV (PVU)',
va='center',
rotation='vertical',
fontsize=20)
fig.text(0.05,
0.75,
'Ridges',
va='center',
rotation='vertical',
fontsize=20)
fig.text(0.05,
0.35,
'Troughs',
va='center',
rotation='vertical',
fontsize=20)
plt.savefig(plotdir + 'pv_gradients_new.pdf')
plt.show()
return
def main():
# Initialise the plot
fig = plt.figure(figsize=(18, 12))
# Add subfigures
for n in range(2):
for m in range(3):
plt.subplot2grid((2, 3), (n, m))
# Plot composites
tropopause_profile('distance_from_dynamical_tropopause', 1, fig)
# Add faint lines for q_adv
#tropopause_profile('distance_from_advection_only_tropopause', 0.25, fig)
for n, subdomain in enumerate(['ridges', 'troughs']):
coord = 'distance_from_advection_only_tropopause'
alpha = 0.3
cubes = second_analysis.get_data(coord, subdomain)
m = 1
mapping = second_analysis.mappings['pv_main']
mapping = {
k: mapping[k]
for k in ('dynamics_tracer_inconsistency',
'sum_of_physics_pv_tracers')
}
ax = fig.axes[n * 3 + m]
profile_multi(cubes, coord, ax, mapping, alpha)
fig.subplots_adjust(bottom=0.2)
# Set labels and limits on plots
for n, subdomain in enumerate(['ridges', 'troughs']):
for m in range(3):
ax = fig.axes[n * 3 + m]
# X-axis - Same for all plots
if m == 0:
ax.set_xlim(-0.5, 0.2)
ax.set_xticks([-0.4, -0.2, 0, 0.2])
else:
ax.set_xlim(-0.2, 0.3)
ax.set_xticks([-0.2, -0.1, 0, 0.1, 0.2, 0.3])
if n == 0:
ax.get_xaxis().set_ticklabels([])
# Set Titles
if m == 0:
ax.set_title('Forecast minus analysis')
elif m == 1:
ax.set_title('PV budget')
elif m == 2:
ax.set_title('Physics PV tracers')
else:
legend(ax,
key=second_analysis.get_idx,
loc='best',
ncol=2,
bbox_to_anchor=(1.0, -0.2),
fontsize=25)
if m == 1:
ax.set_xlabel('PV (PVU)')
# Y-axis - Same for all plots
ax.set_ylim(-2, 2)
ax.set_yticks([-2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2])
if m != 0:
ax.get_yaxis().set_ticklabels([])
multilabel(ax, n * 3 + m)
fig.text(0.075,
0.55,
'Vertical distance from tropopause (km)',
va='center',
rotation='vertical',
fontsize=20)
fig.text(0.05,
0.75,
'Ridges',
va='center',
rotation='vertical',
fontsize=20)
fig.text(0.05,
0.35,
'Troughs',
va='center',
rotation='vertical',
fontsize=20)
plt.savefig(plotdir + 'tropopause_profile_new.pdf')
# plt.show()
return