def pcent_vs_psi_eq(run, period_fac=1., lonin=[-1., 361.], do_plot=False):
#Load in data, add area to dataset
data = xr.open_dataset('/scratch/rg419/Data_moist/climatologies/' + run +
'.nc')
if lonin[1] > lonin[0]:
lons = [
data.lon[i] for i in range(len(data.lon))
if data.lon[i] >= lonin[0] and data.lon[i] < lonin[1]
]
else:
lons = [
data.lon[i] for i in range(len(data.lon))
if data.lon[i] >= lonin[0] or data.lon[i] < lonin[1]
]
psi = mass_streamfunction(data, mc.a, dp_in=50., lons=lons)
psi /= 1.e9
psi_eq = psi.sel(pfull=500.).isel(lat=[31, 32]).mean('lat')
psi_eq.coords['month'] = np.mod(psi_eq.xofyear - 1,
72. * period_fac) // (6 * period_fac) + 1
psi_eq_month = psi_eq.groupby('month').mean(('xofyear'))
p_cent = precip_centroid(run, lat_bound=45., lonin=lonin)
p_cent.coords['month'] = np.mod(p_cent.xofyear - 1,
72. * period_fac) // (6 * period_fac) + 1
p_cent_month = p_cent.groupby('month').mean(('xofyear'))
if do_plot:
plt.plot(p_cent, psi_eq, 'xk', alpha=0.7)
plt.plot(p_cent_month, psi_eq_month, 'xk', ms=7, mew=2)
for i in range(0, len(month_list)):
plt.text(p_cent_month[i] + 0.1,
psi_eq_month[i] + 0.1,
month_list[i],
fontsize=14)
plt.xlabel('Precipitation centroid')
plt.ylabel('500 hPa Mass Streamfunction at the Equator')
plt.grid(True, linestyle=':')
plt.ylim([-600, 600])
plt.xlim([-30, 30])
plt.savefig('/scratch/rg419/plots/aht_work/pcent_psi_' + run + '.pdf',
format='pdf')
plt.close()
return p_cent, p_cent_month, psi_eq, psi_eq_month
def pcent_vs_0psi(run, period_fac=1., lonin=[-1.,361.]):
#Load in data, add area to dataset
data = xr.open_dataset('/scratch/rg419/Data_moist/climatologies/' + run + '.nc')
psi_edge = get_edge_psi(run, thresh=0., period_fac=period_fac)
p_cent = precip_centroid(run, lat_bound = 45., lonin=lonin)
plt.plot(p_cent.sel(xofyear=psi_edge[0].xofyear), psi_edge[0], 'xk', alpha=0.7)
plt.xlabel('Precipitation centroid')
plt.ylabel('Latitude of 0 mass streamfunction')
plt.grid(True,linestyle=':')
plt.ylim([-40,40])
plt.xlim([-40,40])
plt.savefig('/scratch/rg419/plots/aht_work/pcent_0psi_'+run+'.pdf', format='pdf')
plt.close()
def precip_panel(run, axisno, period_fac=1.):
data = xr.open_dataset('/scratch/rg419/Data_moist/climatologies/' + run +
'.nc')
data['totp'] = ((data.precipitation) * 86400.).mean('lon')
data['mse'] = (data.temp * mc.cp_air + data.sphum * mc.L +
data.height * mc.grav).mean('lon') / 1000.
p_cent = precip_centroid(run, lat_bound=35.)
f1 = data.totp.plot.contourf(ax=axisno,
x='xofyear',
y='lat',
levels=plevels,
add_colorbar=False,
add_labels=False,
extend='max',
cmap='Blues')
#data.totp.plot.contour(ax=axisno, x='xofyear', y='lat',levels=np.arange(-92.,109.,100.), add_labels = False, add_colorbar=False, colors='k')
p_cent.plot(ax=axisno, color='w')
cs = data.mse.sel(pfull=850.).plot.contour(ax=axisno,
x='xofyear',
y='lat',
levels=np.arange(
200., 401., 20.),
add_labels=False,
colors='0.7',
add_colorbar=False)
plt.clabel(cs, fontsize=15, inline_spacing=-1, fmt='%1.0f')
axisno.set_ylabel('Latitude')
axisno.set_xlabel('')
axisno.set_ylim(-35, 35)
axisno.set_yticks(np.arange(-30., 31., 15.))
axisno.set_xticks(tickspace * period_fac)
axisno.set_xticklabels('')
axisno.grid(True, linestyle=':')
return f1
plt.xlim(0.625, 40)
plt.ylim(100, 600)
plt.xticks([1.25, 2.5, 5, 10, 20, 40])
plt.yticks([75, 150, 300, 600])
ax = plt.gca()
ax.get_xaxis().set_major_formatter(tk.ScalarFormatter())
ax.get_yaxis().set_major_formatter(tk.ScalarFormatter())
plt.tight_layout()
else:
plt.ylim([0, 600])
plt.xlim([0, 40])
plt.savefig('/scratch/rg419/plots/rotation/regimefig_pcent_' + plotname +
'.pdf',
format='pdf')
plt.close()
psimax_list = []
pcent_list = []
#for run in ['sn_1.000', 'rt_0.500', 'rt_2.000']:
#for run in ['sn_1.000', 'sn_0.500', 'sn_2.000']:
for run in ['sn_1.000', 'ap_20', 'ap_2']:
#for run in ['ap_2', 'ap_20']:
psimax_list.append(psi_max_500(run))
pcent_list.append(precip_centroid(run))
#psimax_list.append(psi_max_500('full_qflux', lonin=[60.,150.]))
#pcent_list.append(precip_centroid('full_qflux', lonin=[60.,150.]))
plot3_regime(psimax_list, pcent_list, 'mld', add_ss=False)
def get_pcent_eq(run):
p_cent = precip_centroid(run, lat_bound=35.)
eq_time = int(np.abs(p_cent[20:]).argmin('xofyear') + 20)
pcent_eq = p_cent[eq_time - 5:eq_time + 15]
return pcent_eq
plt.savefig(plot_dir + 'precip_mse_hm_centroid.pdf', format='pdf')
plt.close()
# Also plot centroid in 'real time' to allow comparison
# Identify when centroid is over the Equator:
def get_pcent_eq(run):
p_cent = precip_centroid(run, lat_bound=35.)
eq_time = int(np.abs(p_cent[20:]).argmin('xofyear') + 20)
pcent_eq = p_cent[eq_time - 5:eq_time + 15]
return pcent_eq
p_cent_05 = precip_centroid('sn_0.500', lat_bound=35.)
p_cent_10 = precip_centroid('sn_1.000', lat_bound=35.)
p_cent_20 = precip_centroid('sn_2.000', lat_bound=35.)
rcParams['figure.figsize'] = 9, 4
plt.plot(p_cent_05[20:36], 'b', LineWidth=2)
plt.plot(p_cent_10[39:55], 'k', LineWidth=2)
plt.plot(p_cent_20[78:94], 'r', LineWidth=2)
#plt.plot([20,20], [-30,30], '--k')
plt.xlim([0, 15])
plt.ylabel('Latitude')
plt.xlabel('Time')
plt.tight_layout()
plt.savefig(plot_dir + 'real_time_centroid.pdf', format='pdf')
plt.close()