def abs_vort_dt_at_pcent(run, rot_fac=1., lev=150.,lat_bound=45., res=0.01, interp=True):
'''Calculate the (normalised) vorticity tendency at the precipitation centroid'''
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' + run + '.nc')
# Interpolate in time if wanted
if interp:
times_new = np.arange(1., 72.2, 0.2)
precip_new = time_interp(data.precipitation, times_new)
u_new = time_interp(data.ucomp.sel(pfull=lev), times_new)
v_new = time_interp(data.vcomp.sel(pfull=lev), times_new)
else:
precip_new = data.precipitation
u_new = data.ucomp.sel(pfull=lev)
v_new = data.vcomp.sel(pfull=lev)
# Find precipitation centroid
precip_new = make_sym(precip_new)
data = xr.Dataset({'precipitation': precip_new}, coords=precip_new.coords)
precip_centroid(data, lat_bound=lat_bound, res=res)
# Calculate vorticity
v_dx = gr.ddx(v_new) # dvdx
u_dy = gr.ddy(u_new) # dudy
omega = 7.2921150e-5 * rot_fac
f = 2 * omega * np.sin(data.lat *np.pi/180)
vor = (v_dx - u_dy + f)*86400.
div = gr.ddx(u_new) + gr.ddy(v_new)
stretching_mean = (-86400. * vor * div).mean('lon')
vor = make_sym(vor, asym=True)
stretching_mean = make_sym(stretching_mean, asym=True)
# Take time derivative of absolute vorticity
if interp:
dvordt = gr.ddt(vor.mean('lon'))*86400.*5.
else:
dvordt = gr.ddt(vor.mean('lon'))*86400.
# Also normalise this by the value of vorticity
dvordtvor = dvordt/vor.mean('lon')
#dvordtvor = stretching_mean/vor.mean('lon')
# Interpolate vorticity in latitude to match precipitation centroid lats
lats = [data.lat[i] for i in range(len(data.lat)) if data.lat[i] >= -lat_bound and data.lat[i] <= lat_bound]
lats_new = np.arange(-lat_bound, lat_bound+res, res)
stretching_mean = lat_interp(stretching_mean.sel(lat=lats), lats_new)
dvordt = lat_interp(dvordt.sel(lat=lats), lats_new)
dvordtvor = lat_interp(dvordtvor.sel(lat=lats), lats_new)
# Get and return dvordt and dvordtvor at the precipitation centroid, as well as the precipitation centroid itself
st_pcent = [float(stretching_mean[i,:].sel(lat=data.p_cent.values[i]).values) for i in range(len(data.xofyear))]
st_pcent = xr.DataArray(np.asarray(st_pcent), coords=[stretching_mean.xofyear.values], dims=['xofyear'])
dvordt_pcent = [float(dvordt[i,:].sel(lat=data.p_cent.values[i]).values) for i in range(len(data.xofyear))]
dvordt_pcent = xr.DataArray(np.asarray(dvordt_pcent), coords=[dvordt.xofyear.values], dims=['xofyear'])
dvordtvor_pcent = [float(dvordtvor[i,:].sel(lat=data.p_cent.values[i]).values) for i in range(len(data.xofyear))]
dvordtvor_pcent = xr.DataArray(np.asarray(dvordtvor_pcent), coords=[dvordtvor.xofyear.values], dims=['xofyear'])
return dvordt_pcent, dvordtvor_pcent, data.p_cent, st_pcent
def div_at_pcent(run,
rot_fac=1.,
lev=150.,
lat_bound=45.,
res=0.01,
interp=True):
'''Calculate the (normalised) vorticity tendency at the precipitation centroid'''
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' +
run + '.nc')
# Interpolate in time if wanted
if interp:
times_new = np.arange(1., 72.2, 0.2)
precip_new = time_interp(data.precipitation, times_new)
u_new = time_interp(data.ucomp.sel(pfull=lev), times_new)
v_new = time_interp(data.vcomp.sel(pfull=lev), times_new)
else:
precip_new = data.precipitation
u_new = data.ucomp.sel(pfull=lev)
v_new = data.vcomp.sel(pfull=lev)
# Find precipitation centroid
precip_new = make_sym(precip_new)
data = xr.Dataset({'precipitation': precip_new}, coords=precip_new.coords)
precip_centroid(data, lat_bound=lat_bound, res=res)
# Calculate divergence
v_dx = gr.ddx(v_new) # dvdx
u_dy = gr.ddy(u_new) # dudy
div = (gr.ddx(u_new) + gr.ddy(v_new)).mean('lon') * 86400.
omega = 7.2921150e-5 * rot_fac
f = 2 * omega * np.sin(data.lat * np.pi / 180)
vor = (-1. * gr.ddy(u_new) * 0. + f).mean('lon') * 86400.
div = make_sym(div)
vor = make_sym(vor, asym=True)
div = div * vor
div = vor
# Interpolate divergence in latitude to match precipitation centroid lats
lats = [
data.lat[i] for i in range(len(data.lat))
if data.lat[i] >= -lat_bound and data.lat[i] <= lat_bound
]
lats_new = np.arange(-lat_bound, lat_bound + res, res)
div = lat_interp(div.sel(lat=lats), lats_new)
# Get and return dvordt and dvordtvor at the precipitation centroid, as well as the precipitation centroid itself
div_pcent = [
float(div[i, :].sel(lat=data.p_cent.values[i]).values)
for i in range(len(data.xofyear))
]
div_pcent = xr.DataArray(np.asarray(div_pcent),
coords=[div.xofyear.values],
dims=['xofyear'])
return div_pcent, data.p_cent
def abs_vort_dt_at_pcent(run, rot_fac=1., lev=150., lat_bound=45.):
data = xr.open_dataset('/scratch/rg419/Data_moist/climatologies/' + run +
'.nc')
data['precipitation'] = make_sym(data.precipitation)
precip_centroid(data, lat_bound=lat_bound)
v_dx = gr.ddx(data.vcomp) # dvdx
u_dy = gr.ddy(data.ucomp) # dudy
omega = 7.2921150e-5 * rot_fac
f = 2 * omega * np.sin(data.lat * np.pi / 180)
vor = (v_dx - u_dy + f).sel(pfull=lev) * 86400.
vor = make_sym(vor, asym=True)
# Take time derivative of absolute vorticity
dvordt = gr.ddt(vor.mean('lon')) * 86400.
# Select latitudes over which to evaluate precip centroid
lats = [
data.lat[i] for i in range(len(data.lat))
if data.lat[i] >= -lat_bound and data.lat[i] <= lat_bound
]
dvordt_lats = dvordt.sel(lat=lats).values
#f_lats = f.sel(lat=lats).values
# Interpolate vorticity tendency and f in latitude
lats_new = np.arange(-lat_bound, lat_bound + 0.1, 0.1)
fun = spint.interp1d(lats,
dvordt_lats,
axis=-1,
fill_value='extrapolate',
kind='quadratic')
dvordt_new = fun(lats_new)
dvordt = xr.DataArray(dvordt_new,
coords=[data.xofyear.values, lats_new],
dims=['xofyear', 'lat'])
#fun = spint.interp1d(lats, f_lats, axis=-1, fill_value='extrapolate', kind='quadratic')
#f_new = fun(lats_new)
#f = xr.DataArray(f_new, coords=[lats_new], dims=['lat'])
dvordt_pcent = [
float(dvordt[i, :].sel(lat=data.p_cent.values[i]).values)
for i in range(len(data.xofyear))
]
#f_pcent = [float(f.sel(lat=data.p_cent.values[i]).values*86400.) for i in range(len(data.xofyear))]
dvordt_pcent = xr.DataArray(np.asarray(dvordt_pcent),
coords=[data.xofyear.values],
dims=['xofyear'])
return dvordt_pcent
def p_cent_rate(data, days=False):
"""
Inputs:
data - xarray dataset climatology including precipitation as either precipitation or as convection_rain and condensation_rain
days - instructs if data is in daily or pentad means
Returns:
dpcentdt - rate of movement of the precipitation centroid
dpcentdt2 - rate of acceleration of the precipitation centroid
"""
# Get total precip
try:
data['precipitation'] = data.condensation_rain + data.convection_rain
except:
data['precipitation'] = data.precipitation
data['precipitation'] = make_sym(data.precipitation)
# Locate precipitation centroid
precip_centroid(data)
# If units in are days rather than pentads (e.g. for shorter years) convert to pentads
if days:
dpcentdt = gr.ddt(data.p_cent, secperunit=86400.) * 86400.
else:
dpcentdt = gr.ddt(data.p_cent) * 86400.
dpcentdt2 = gr.ddt(dpcentdt, secperunit=86400.) * 86400.
return dpcentdt, dpcentdt2
def rate_at_eq(runs, do_make_sym=True, days=None):
dpdt_eq = []
if days == None:
days = [False] * len(runs)
j = 0
for run in runs:
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' +
run + '.nc')
if do_make_sym: # If symmetric data is wanted, average over both hemispheres (NB currently only set up for a climatology 2/05/18)
data['precipitation'] = make_sym(data.precipitation)
# Locate precipitation centroid
precip_centroid(data)
# Get rate of movement of precip centroid
if days[j]:
dpcentdt = gr.ddt(data.p_cent, secperunit=86400.) * 86400.
else:
dpcentdt = gr.ddt(data.p_cent) * 86400.
#dpcentdt_max = dpcentdt_pmask.where(dpcentdt_pmask==dpcentdt_pmask.max('xofyear'),drop=True) # Find the maximum rate
p_cent = np.abs(data.p_cent.where(dpcentdt >= 0.))
dpdt_eq_j = dpcentdt.where(p_cent == p_cent.min('xofyear'), drop=True)
dpdt_eq.append(dpdt_eq_j.values[0])
j = j + 1
return np.asarray(dpdt_eq)
def precip_psi_plot(run, ax, label='a)', p_cent=True):
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' +
run + '.nc')
psi = mass_streamfunction(data, a=6376.0e3, dp_in=50.)
psi /= 1.e9
psi = make_sym(psi, asym=True)
data['precipitation'] = make_sym(data.precipitation)
f1 = precip_mse_plot(data,
ax,
plot_type='precip',
precip_contour=None,
p_cent=p_cent)
psi.sel(pfull=500).plot.contour(ax=ax,
x='xofyear',
y='lat',
levels=np.arange(-500., 0., 100.),
add_labels=False,
colors='0.7',
linewidths=2,
linestyles='--',
alpha=0.7)
psi.sel(pfull=500).plot.contour(ax=ax,
x='xofyear',
y='lat',
levels=np.arange(0., 510., 100.),
add_labels=False,
colors='0.7',
linewidths=2,
alpha=0.7)
psi.sel(pfull=500).plot.contour(ax=ax,
x='xofyear',
y='lat',
levels=np.arange(-1000., 1010., 1000.),
add_labels=False,
colors='0.5',
linewidths=2,
alpha=0.7)
ax.text(-10, 60, label)
return f1
def vor_plot(run, ax, tf, linestyle='-', rot_fac=1., lev=150.):
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' +
run + '.nc')
# Calculate vorticity
v_dx = gr.ddx(data.vcomp) # dvdx
u_dy = gr.ddy(data.ucomp) # dudy
omega = 7.2921150e-5 * rot_fac
f = 2 * omega * np.sin(data.lat * np.pi / 180)
vor = (v_dx - u_dy + f).sel(pfull=lev) * 86400.
div = (gr.ddx(data.ucomp) + gr.ddy(data.vcomp)).sel(pfull=lev)
vor = (make_sym(vor, asym=True)).mean('lon')
div = (make_sym(div)).mean('lon') * 8640.
m = ((mc.omega * mc.a**2. * np.cos(data.lat * np.pi / 180.)**2. +
data.ucomp.mean('lon') * mc.a * np.cos(data.lat * np.pi / 180.)).sel(
pfull=lev)) / (mc.omega * mc.a**2.)
#dvordt = gr.ddt(vor.mean('lon'))*86400.
#stretching = (-86400. * vor * div).mean('lon')
#adv = -86400. * (data.vcomp.sel(pfull=lev) * gr.ddy(vor, vector=False)).mean('lon')
#dvordy = gr.ddy(vor, vector=False).mean('lon')*8640.
#dvordy[tf[0]:tf[1],:].mean('xofyear').plot(ax=ax, color='r', linestyle=linestyle)
#m[:,tf[0]:tf[1]].mean('xofyear').plot(ax=ax, color='r', linestyle=linestyle)
vor[tf[0]:tf[1], :].mean('xofyear').plot(ax=ax,
color='k',
linestyle=linestyle)
#stretching[tf[0]:tf[1],:].mean('xofyear').plot(ax=ax, color='b', linestyle=linestyle)
#adv[tf[0]:tf[1],:].mean('xofyear').plot(ax=ax, color='r', linestyle=linestyle)
#(stretching + adv)[tf[0]:tf[1],:].mean('xofyear').plot(ax=ax, color='r', linestyle=linestyle)
ax.set_title('')
ax.set_xlabel('')
#ax.set_ylim(0.5,1.)
ax.set_ylim(-10, 10)
ax.grid(True, linestyle=':')
ax.set_ylabel('Absolute vorticity, day$^{-1}$')
def p_cent_rate_max(runs, days=None):
# Get the maximum rate of change of the precipitation centroid latitude, and the latitude at which this occurs.
max_rate = []
max_rate_lat = []
if days==None:
days=[False]*len(runs)
j=0
for run in runs:
# Open dataset
data = xr.open_dataset('/scratch/rg419/Data_moist/climatologies/' + run + '.nc')
# Get total precip
try:
data['precipitation'] = data.condensation_rain + data.convection_rain
except:
data['precipitation'] = data.precipitation
data['precipitation'] = make_sym(data.precipitation)
# Locate precipitation centroid
precip_centroid(data)
# Get rate of movement of precip centroid
if days[j]:
dpcentdt = gr.ddt(data.p_cent, secperunit = 86400.) * 86400.
else:
dpcentdt = gr.ddt(data.p_cent) * 86400.
dpcentdt_ppos = dpcentdt.where(data.p_cent>=0.) # Find precip centroid rate where precip centroid is in the northern hemisphere
dpcentdt_max = dpcentdt_ppos.where(dpcentdt_ppos==dpcentdt_ppos.max(),drop=True) # Find the maximum of the above
if len(dpcentdt_max) > 1:
dpcentdt_max = dpcentdt_max[0]
pcent_dtmax = data.p_cent.sel(xofyear=dpcentdt_max.xofyear) # Find the location of the preciptiation when the rate is maximum
print(dpcentdt_max.values, pcent_dtmax.values) # Print both
max_rate.append(dpcentdt_max)
max_rate_lat.append(pcent_dtmax)
j=j+1
max_rate = np.asarray(max_rate)
max_rate_lat = np.asarray(max_rate_lat)
return max_rate, max_rate_lat
def plot_bs_fig_9_moist(run, pentads=[35, 40, 45, 50], rotfac=1.):
plot_dir = '/scratch/rg419/plots/paper_2_figs/revisions/heatbudg_moist/'
mkdir = sh.mkdir.bake('-p')
mkdir(plot_dir)
rcParams['figure.figsize'] = 5.5, 7.
rcParams['font.size'] = 14
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' +
run + '.nc')
data['precipitation'] = make_sym(data.precipitation)
precip_centroid(data) # Locate precipitation centroid
dpcentdt = gr.ddt(data.p_cent) * 86400.
p_cent_pos = np.abs(data.p_cent.where(dpcentdt >= 0.))
eq_time = p_cent_pos.xofyear[p_cent_pos.argmin('xofyear').values]
peak_rate_time = dpcentdt.xofyear[dpcentdt.argmax('xofyear').values]
max_lat_time = data.p_cent.xofyear[data.p_cent.argmax('xofyear').values]
edge_loc, psi_max, psi_max_loc = get_edge_psi(data,
lev=850.,
thresh=0.,
intdown=True)
fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, sharex=True)
axes = [ax1, ax2, ax3, ax4]
pentads = [
eq_time.values, peak_rate_time.values,
np.floor((peak_rate_time.values + max_lat_time.values) / 2.),
max_lat_time.values
]
for i in range(4):
fig_9_moist(run, ax=axes[i], pentad=pentads[i])
edge = edge_loc.sel(xofyear=pentads[i])
axes[i].plot([edge, edge], [-250., 250.], 'k:')
axes[i].text(20, 240., 'Pentad ' + str(int(pentads[i])))
ax4.set_xlabel('Latitude')
#ax1.text(-55, 315., 'a)')
#ax2.text(-55, 315., 'b)')
#ax3.text(-55, 315., 'c)')
#ax4.text(-55, 315., 'd)')
plt.subplots_adjust(left=0.15, right=0.95, top=0.97, bottom=0.1)
plt.savefig(plot_dir + 'sb08_fig9_moist_' + run + '.pdf', format='pdf')
plt.close()
def elliptical_equation(run, rotfac=1.):
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' + run + '.nc')
data = data.mean('lon')
data['precipitation'] = make_sym(data.precipitation)
precip_centroid(data) # Locate precipitation centroid
dpcentdt = gr.ddt(data.p_cent) * 86400.
p_cent_pos = np.abs(data.p_cent.where(dpcentdt>=0.))
eq_time = p_cent_pos.xofyear[p_cent_pos.argmin('xofyear').values]
peak_rate_time = dpcentdt.xofyear[dpcentdt.argmax('xofyear').values]
max_lat_time = data.p_cent.xofyear[data.p_cent.argmax('xofyear').values]
convTtotheta=(1000./data.pfull)**mc.kappa
data['theta'] = data.temp*convTtotheta
#data['vcomp_theta'] = data.vcomp_temp*convTtotheta
#data['omega_theta'] = data.omega_temp*convTtotheta
#heating = data.dt_tg_condensation + data.dt_tg_convection + data.dt_tg_diffusion + data.tdt_rad
#data['heating_theta'] = heating*convTtotheta
sinphi = np.sin(data.lat * np.pi/180.)
cosphi = np.cos(data.lat * np.pi/180.)
#Calculate psi
#psi = mass_streamfunction(data, a=6376.0e3, dp_in=50.)
dpsidydy = gr.ddy(data.omega, vector=False)
rho = data.pfull*100./mc.rdgas/data.temp
dthetadp = gr.ddp(data.theta)
brunt_fac = -1./rho/data.theta * dthetadp
psi_yy = (brunt_fac * dpsidydy)
dpsidpdp = gr.ddp(-1.*data.vcomp)
coriolis = 2.* rotfac * mc.omega * sinphi * data.vcomp
dudy = gr.ddy(data.ucomp)
cor_fac = -1.*dudy*coriolis + coriolis**2.
psi_pp = (cor_fac * dpsidydy)
dpsidpdy = gr.ddy(-1.*data.vcomp)
dudp = gr.ddp(data.ucomp)
psi_py = (2.*dudp*dpsidpdy*coriolis)
duvdy = gr.ddy(data.ucomp_vcomp, uv=True)
duwdp = gr.ddp(data.ucomp_omega)
vdudy_mean = data.vcomp * dudy
wdudp_mean = data.omega * dudp
eddy_tend = -1.*duvdy -1.*duwdp + vdudy_mean + wdudp_mean
M = data.dt_ug_diffusion + eddy_tend
dMdp = (gr.ddp(M) * coriolis)
dthetady = gr.ddy(data.theta, vector=False)
#dvtdy = gr.ddy(data.vcomp_theta)
#dwtdp = gr.ddp(data.omega_theta)
vdtdy_mean = data.vcomp * dthetady
wdtdp_mean = data.omega * dthetadp
#eddy_tend_th = -1.*dvtdy -1.*dwtdp + vdtdy_mean + wdtdp_mean
#J = data.heating_theta #+ eddy_tend_th
#dJdy = (-1.* gr.ddy(J, vector=False)/rho/data.theta)
plot_dir = '/scratch/rg419/plots/paper_2_figs/revisions/elliptical_eq/'
mkdir = sh.mkdir.bake('-p')
mkdir(plot_dir)
rcParams['figure.figsize'] = 20., 12.
levels = np.arange(-1.e-12,1.1e-12,0.1e-12)
fig, ((ax1, ax2, ax3, ax4, ax5), (ax6, ax7, ax8, ax9, ax10), (ax11, ax12, ax13, ax14, ax15)) = plt.subplots(3, 5, sharey='row', sharex='col')
axes = [ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8, ax9, ax10, ax11, ax12, ax13, ax14, ax15]
i=0
for time in [eq_time, peak_rate_time, max_lat_time]:
f1 = psi_yy.sel(xofyear=time).plot.contourf(ax=axes[5*i], x='lat', y='pfull', yincrease=False, levels=levels, add_labels=False, extend='both', add_colorbar=False)
psi_pp.sel(xofyear=time).plot.contourf(ax=axes[5*i+1], x='lat', y='pfull', yincrease=False, levels=levels, add_labels=False, extend='both', add_colorbar=False)
psi_py.sel(xofyear=time).plot.contourf(ax=axes[5*i+2], x='lat', y='pfull', yincrease=False, levels=levels, add_labels=False, extend='both', add_colorbar=False)
dMdp.sel(xofyear=time).plot.contourf(ax=axes[5*i+3], x='lat', y='pfull', yincrease=False, levels=levels, add_labels=False, extend='both', add_colorbar=False)
#dJdy.sel(xofyear=time).plot.contourf(ax=axes[5*i+4], x='lat', y='pfull', yincrease=False, levels=levels, add_labels=False, extend='both', add_colorbar=False)
i=i+1
for ax in axes:
ax.set_xlim(-30.,30.)
ax1.set_title('N$^2d^2psi/dy^2$')
ax2.set_title('$f(f-du/dy)d^2psi/dp^2$')
ax3.set_title('Cross term')
ax4.set_title('dM/dp')
ax5.set_title('dJ/dy')
plt.subplots_adjust(right=0.97, left=0.1, top=0.95, bottom=0., hspace=0.25, wspace=0.12)
#Colorbar
cb1=fig.colorbar(f1, ax=axes, use_gridspec=True, orientation = 'horizontal',fraction=0.15, pad=0.15, aspect=30, shrink=0.5)
figname = 'elliptical_eq_' +run+ '.pdf'
plt.savefig(plot_dir + figname, format='pdf')
plt.close()
def abs_vort_dt_plot(run, rot_fac=1., lev=150.):
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' +
run + '.nc')
psi = mass_streamfunction(data, a=6376.0e3, dp_in=50.)
psi /= 1.e9
data['precipitation'] = make_sym(data.precipitation)
precip_centroid(data)
v_dx = gr.ddx(data.vcomp) # dvdx
u_dy = gr.ddy(data.ucomp) # dudy
omega = 7.2921150e-5 * rot_fac
f = 2 * omega * np.sin(data.lat * np.pi / 180)
vor = (v_dx - u_dy + f).sel(pfull=lev) * 86400.
# Take gradients of vorticity
dvordx = gr.ddx(vor)
dvordy = gr.ddy(vor, vector=False)
# Horizontal material derivative
horiz_md_mean = -86400. * (data.ucomp.sel(pfull=lev) * dvordx +
data.vcomp.sel(pfull=lev) * dvordy)
# Calculate divergence and stretching term
div = gr.ddx(data.ucomp.sel(pfull=lev)) + gr.ddy(data.vcomp.sel(pfull=lev))
stretching_mean = -86400. * vor * div
#vor = make_sym(vor, asym=True)
#psi = make_sym(psi, asym=True)
# Take time derivative of absolute vorticity
dvordt = gr.ddt(vor.mean('lon')) * 86400.
stretching_mean = stretching_mean.mean('lon')
horiz_md_mean = horiz_md_mean.mean('lon')
plot_dir = '/scratch/rg419/plots/paper_2_figs/abs_vort_dt/'
mkdir = sh.mkdir.bake('-p')
mkdir(plot_dir)
rcParams['figure.figsize'] = 5, 12
rcParams['font.size'] = 14
fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1)
f1 = dvordt.plot.contourf(ax=ax1,
x='xofyear',
y='lat',
levels=np.arange(-0.06, 0.07, 0.01),
add_colorbar=False,
add_labels=False)
psi.sel(pfull=500).plot.contour(ax=ax1,
x='xofyear',
y='lat',
levels=np.arange(-500., 0., 100.),
add_labels=False,
colors='0.7',
linewidths=2,
linestyles='--')
psi.sel(pfull=500).plot.contour(ax=ax1,
x='xofyear',
y='lat',
levels=np.arange(0., 510., 100.),
add_labels=False,
colors='0.7',
linewidths=2)
psi.sel(pfull=500).plot.contour(ax=ax1,
x='xofyear',
y='lat',
levels=np.arange(-1000., 1010., 1000.),
add_labels=False,
colors='0.5',
linewidths=2)
data.p_cent.plot.line(ax=ax1, color='k', linewidth=2)
ax1.set_ylim([-60, 60])
ax1.set_ylabel('Latitude')
ax1.set_xticks([12, 24, 36, 48, 60, 72])
ax1.set_yticks([-60, -30, 0, 30, 60])
ax1.set_xlabel('Pentad')
ax1.grid(True, linestyle=':')
f1 = stretching_mean.plot.contourf(ax=ax2,
x='xofyear',
y='lat',
levels=np.arange(-1.5, 1.6, 0.25),
add_colorbar=False,
add_labels=False)
psi.sel(pfull=500).plot.contour(ax=ax2,
x='xofyear',
y='lat',
levels=np.arange(-500., 0., 100.),
add_labels=False,
colors='0.7',
linewidths=2,
linestyles='--')
psi.sel(pfull=500).plot.contour(ax=ax2,
x='xofyear',
y='lat',
levels=np.arange(0., 510., 100.),
add_labels=False,
colors='0.7',
linewidths=2)
psi.sel(pfull=500).plot.contour(ax=ax2,
x='xofyear',
y='lat',
levels=np.arange(-1000., 1010., 1000.),
add_labels=False,
colors='0.5',
linewidths=2)
data.p_cent.plot.line(ax=ax2, color='k', linewidth=2)
ax2.set_ylim([-60, 60])
ax2.set_ylabel('Latitude')
ax2.set_xticks([12, 24, 36, 48, 60, 72])
ax2.set_yticks([-60, -30, 0, 30, 60])
ax2.set_xlabel('Pentad')
ax2.grid(True, linestyle=':')
f1 = horiz_md_mean.plot.contourf(ax=ax3,
x='xofyear',
y='lat',
levels=np.arange(-1.5, 1.6, 0.25),
add_colorbar=False,
add_labels=False)
psi.sel(pfull=500).plot.contour(ax=ax3,
x='xofyear',
y='lat',
levels=np.arange(-500., 0., 100.),
add_labels=False,
colors='0.7',
linewidths=2,
linestyles='--')
psi.sel(pfull=500).plot.contour(ax=ax3,
x='xofyear',
y='lat',
levels=np.arange(0., 510., 100.),
add_labels=False,
colors='0.7',
linewidths=2)
psi.sel(pfull=500).plot.contour(ax=ax3,
x='xofyear',
y='lat',
levels=np.arange(-1000., 1010., 1000.),
add_labels=False,
colors='0.5',
linewidths=2)
data.p_cent.plot.line(ax=ax3, color='k', linewidth=2)
ax3.set_ylim([-60, 60])
ax3.set_ylabel('Latitude')
ax3.set_xticks([12, 24, 36, 48, 60, 72])
ax3.set_yticks([-60, -30, 0, 30, 60])
ax3.set_xlabel('Pentad')
ax3.grid(True, linestyle=':')
f1 = (stretching_mean + horiz_md_mean).plot.contourf(ax=ax4,
x='xofyear',
y='lat',
levels=np.arange(
-1.5, 1.6, 0.25),
add_colorbar=False,
add_labels=False)
psi.sel(pfull=500).plot.contour(ax=ax4,
x='xofyear',
y='lat',
levels=np.arange(-500., 0., 100.),
add_labels=False,
colors='0.7',
linewidths=2,
linestyles='--')
psi.sel(pfull=500).plot.contour(ax=ax4,
x='xofyear',
y='lat',
levels=np.arange(0., 510., 100.),
add_labels=False,
colors='0.7',
linewidths=2)
psi.sel(pfull=500).plot.contour(ax=ax4,
x='xofyear',
y='lat',
levels=np.arange(-1000., 1010., 1000.),
add_labels=False,
colors='0.5',
linewidths=2)
data.p_cent.plot.line(ax=ax4, color='k', linewidth=2)
ax4.set_ylim([-60, 60])
ax4.set_ylabel('Latitude')
ax4.set_xticks([12, 24, 36, 48, 60, 72])
ax4.set_yticks([-60, -30, 0, 30, 60])
ax4.set_xlabel('Pentad')
ax4.grid(True, linestyle=':')
plt.subplots_adjust(left=0.15, right=0.95, top=0.95, bottom=0.05)
cb1 = fig.colorbar(f1,
ax=ax1,
use_gridspec=True,
orientation='horizontal',
fraction=0.15,
pad=0.2,
aspect=40)
cb1.set_label('Absolute vorticity tendency, day$^{-2}$')
plt.savefig(plot_dir + 'vort_terms_' + run + '.pdf', format='pdf')
plt.close()
def local_pcent_plots(run,
regions=[[350, 10], [80, 100], [170, 190], [260, 280]],
do_make_sym=True):
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' +
run + '.nc')
if do_make_sym:
data['precipitation'] = make_sym(data.precipitation)
data = precip_centroid_ll(data, lat_bound=30.)
def get_lons(lonin, data):
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]
]
return lons
lons_1 = get_lons(regions[0], data)
lons_2 = get_lons(regions[1], data)
lons_3 = get_lons(regions[2], data)
lons_4 = get_lons(regions[3], data)
dpcentdt_1 = gr.ddt(data.p_cent.sel(lon=lons_1).mean('lon')) * 86400.
dpcentdt_2 = gr.ddt(data.p_cent.sel(lon=lons_2).mean('lon')) * 86400.
dpcentdt_3 = gr.ddt(data.p_cent.sel(lon=lons_3).mean('lon')) * 86400.
dpcentdt_4 = gr.ddt(data.p_cent.sel(lon=lons_4).mean('lon')) * 86400.
# Set figure parameters
rcParams['figure.figsize'] = 10, 7
rcParams['font.size'] = 14
# Start figure with 4 subplots
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
ax1.plot(data.p_cent.sel(lon=lons_1).mean('lon'),
dpcentdt_1,
'kx',
mew=2,
ms=10,
alpha=0.5)
ax1.plot(data.p_cent.sel(lon=lons_1).mean('lon'),
dpcentdt_1,
alpha=0.3,
color='k',
linewidth=2)
ax1.set_title('West coast')
ax2.plot(data.p_cent.sel(lon=lons_2).mean('lon'),
dpcentdt_2,
'kx',
mew=2,
ms=10,
alpha=0.5)
ax2.plot(data.p_cent.sel(lon=lons_2).mean('lon'),
dpcentdt_2,
alpha=0.3,
color='k',
linewidth=2)
ax2.set_title('Land')
ax3.plot(data.p_cent.sel(lon=lons_3).mean('lon'),
dpcentdt_3,
'kx',
mew=2,
ms=10,
alpha=0.5)
ax3.plot(data.p_cent.sel(lon=lons_3).mean('lon'),
dpcentdt_3,
alpha=0.3,
color='k',
linewidth=2)
ax3.set_title('East coast')
ax4.plot(data.p_cent.sel(lon=lons_4).mean('lon'),
dpcentdt_4,
'kx',
mew=2,
ms=10,
alpha=0.5)
ax4.plot(data.p_cent.sel(lon=lons_4).mean('lon'),
dpcentdt_4,
alpha=0.3,
color='k',
linewidth=2)
ax4.set_title('Ocean')
ax3.set_xlabel('ITCZ latitude')
ax4.set_xlabel('ITCZ latitude')
ax1.set_ylabel('ITCZ migration rate')
ax3.set_ylabel('ITCZ migration rate')
for ax in [ax1, ax2, ax3, ax4]:
ax.grid(True, linestyle=':')
ax.set_xlim(-25, 25)
ax.set_ylim(-1.0, 1.0)
plt.subplots_adjust(left=0.1,
right=0.97,
top=0.95,
bottom=0.1,
hspace=0.3,
wspace=0.3)
# Save as a pdf
plt.savefig(plot_dir + 'bowties_' + run + '.pdf', format='pdf')
plt.close()
# Start figure with 4 subplots
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
precip_mse_plot(data, ax1, lonin=regions[0], lat_bound=30.)
precip_mse_plot(data, ax2, lonin=regions[1], lat_bound=30.)
precip_mse_plot(data, ax3, lonin=regions[2], lat_bound=30.)
precip_mse_plot(data, ax4, lonin=regions[3], lat_bound=30.)
ax1.set_title('West coast')
ax2.set_title('Land')
ax3.set_title('East coast')
ax4.set_title('Ocean')
ax3.set_xlabel('Pentad')
ax4.set_xlabel('Pentad')
ax1.set_ylabel('Latitude')
ax3.set_ylabel('Latitude')
for ax in [ax1, ax2, ax3, ax4]:
ax.grid(True, linestyle=':')
plt.subplots_adjust(left=0.1,
right=0.97,
top=0.95,
bottom=0.1,
hspace=0.3,
wspace=0.3)
# Save as a pdf
plt.savefig(plot_dir + 'precip_' + run + '.pdf', format='pdf')
plt.close()
data.close()
def abs_vort_dt_plot(run, rot_fac=1., lev=150.):
data = xr.open_dataset('/scratch/rg419/Data_moist/climatologies/' + run +
'.nc')
psi = mass_streamfunction(data, a=6376.0e3, dp_in=50.)
psi /= 1.e9
data['precipitation'] = make_sym(data.precipitation)
precip_centroid(data)
v_dx = gr.ddx(data.vcomp) # dvdx
u_dy = gr.ddy(data.ucomp) # dudy
omega = 7.2921150e-5 * rot_fac
f = 2 * omega * np.sin(data.lat * np.pi / 180)
vor = (v_dx - u_dy + f).sel(pfull=lev) * 86400.
vor = make_sym(vor, asym=True)
psi = make_sym(psi, asym=True)
# Take time derivative of absolute vorticity
dvordt = gr.ddt(vor.mean('lon')) * 86400.
plot_dir = '/scratch/rg419/plots/paper_2_figs/abs_vort_dt/'
mkdir = sh.mkdir.bake('-p')
mkdir(plot_dir)
rcParams['figure.figsize'] = 5.5, 4.3
rcParams['font.size'] = 16
fig = plt.figure()
ax1 = fig.add_subplot(111)
f1 = dvordt.plot.contourf(ax=ax1,
x='xofyear',
y='lat',
levels=np.arange(-0.06, 0.07, 0.01),
add_colorbar=False,
add_labels=False)
psi.sel(pfull=500).plot.contour(ax=ax1,
x='xofyear',
y='lat',
levels=np.arange(-500., 0., 100.),
add_labels=False,
colors='0.7',
linewidths=2,
linestyles='--')
psi.sel(pfull=500).plot.contour(ax=ax1,
x='xofyear',
y='lat',
levels=np.arange(0., 510., 100.),
add_labels=False,
colors='0.7',
linewidths=2)
psi.sel(pfull=500).plot.contour(ax=ax1,
x='xofyear',
y='lat',
levels=np.arange(-1000., 1010., 1000.),
add_labels=False,
colors='0.5',
linewidths=2)
data.p_cent.plot.line(color='k', linewidth=2)
ax1.set_ylim([-60, 60])
ax1.set_ylabel('Latitude')
ax1.set_xticks([12, 24, 36, 48, 60, 72])
ax1.set_yticks([-60, -30, 0, 30, 60])
ax1.set_xlabel('Pentad')
ax1.grid(True, linestyle=':')
plt.subplots_adjust(left=0.15, right=0.95, top=0.95, bottom=0.05)
cb1 = fig.colorbar(f1,
ax=ax1,
use_gridspec=True,
orientation='horizontal',
fraction=0.15,
pad=0.2,
aspect=40)
cb1.set_label('Absolute vorticity tendency, day$^{-2}$')
plt.savefig(plot_dir + 'abs_vort_dt_' + run + '.pdf', format='pdf')
plt.close()
def ang_mom_grad_plot(run, rot_fac=1., lev=150.):
'''Plot dvordt or 1/vor * dvordt'''
#Load data
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' +
run + '.nc')
#Calculate psi to overplot
psi = mass_streamfunction(data, a=6376.0e3, dp_in=50.)
psi /= 1.e9
psi = make_sym(psi, asym=True)
# Make precip symmetric and find the precip centroid
data['precipitation'] = make_sym(data.precipitation)
precip_centroid(data)
data['ucomp'] = make_sym(data.ucomp)
data['vcomp'] = make_sym(data.vcomp, asym=True)
data['omega'] = make_sym(data.omega)
# Calculate vorticity
v_dx = gr.ddx(data.vcomp) # dvdx
u_dy = gr.ddy(data.ucomp) # dudy
u_dp = gr.ddp(data.ucomp) # dudp
omega = 7.2921150e-5 * rot_fac
f = 2 * omega * np.sin(data.lat * np.pi / 180)
vor = (v_dx - u_dy + f).sel(pfull=lev).mean('lon') * 86400.
vertical_term = (-1. * data.omega / data.vcomp *
u_dp).sel(pfull=lev).mean('lon') * 86400.
ang_mom_grad = vor + vertical_term
# Plot!
plot_dir = '/scratch/rg419/plots/paper_2_figs/ang_mom_grad/'
mkdir = sh.mkdir.bake('-p')
mkdir(plot_dir)
rcParams['figure.figsize'] = 5.5, 4.3
rcParams['font.size'] = 14
fig = plt.figure()
ax1 = fig.add_subplot(111)
f1 = ang_mom_grad.plot.contourf(ax=ax1,
x='xofyear',
y='lat',
levels=np.arange(-6., 6.1, 1.),
add_colorbar=False,
add_labels=False,
extend='both')
psi.sel(pfull=lev).plot.contour(ax=ax1,
x='xofyear',
y='lat',
levels=np.arange(-500., 0., 100.),
add_labels=False,
colors='0.7',
linewidths=2,
linestyles='--')
psi.sel(pfull=lev).plot.contour(ax=ax1,
x='xofyear',
y='lat',
levels=np.arange(0., 510., 100.),
add_labels=False,
colors='0.7',
linewidths=2)
psi.sel(pfull=lev).plot.contour(ax=ax1,
x='xofyear',
y='lat',
levels=np.arange(-1000., 1010., 1000.),
add_labels=False,
colors='0.5',
linewidths=2)
data.p_cent.plot.line(ax=ax1, color='k', linewidth=2)
ax1.set_ylim([-60, 60])
ax1.set_ylabel('Latitude')
ax1.set_xticks([12, 24, 36, 48, 60, 72])
ax1.set_yticks([-60, -30, 0, 30, 60])
ax1.set_xlabel('Pentad')
ax1.grid(True, linestyle=':')
plt.subplots_adjust(left=0.17,
right=0.9,
top=0.97,
bottom=0.07,
hspace=0.3)
cb1 = fig.colorbar(f1,
ax=ax1,
use_gridspec=True,
orientation='horizontal',
fraction=0.15,
pad=0.2,
aspect=40)
cb1.set_label('Angular momentum gradient')
plt.savefig(plot_dir + 'ang_mom_grad_' + run + '.pdf', format='pdf')
plt.close()
def div_plot(run, ax, rot_fac=1., lev=150.):
'''Plot dvordt or 1/vor * dvordt'''
#Load data
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' +
run + '.nc')
#Calculate psi to overplot
psi = mass_streamfunction(data, a=6376.0e3, dp_in=50.)
psi /= 1.e9
psi = make_sym(psi, asym=True)
# Make precip symmetric and find the precip centroid
data['precipitation'] = make_sym(data.precipitation)
precip_centroid(data)
# Calculate divergence
u_dx = gr.ddx(data.ucomp) # dudx
v_dy = gr.ddy(data.vcomp) # dvdy
div = (u_dx + v_dy).sel(pfull=lev) * 86400.
div = make_sym(div)
div = div.mean('lon')
f1 = div.plot.contourf(ax=ax,
x='xofyear',
y='lat',
levels=np.arange(-1.2, 1.3, 0.2),
add_colorbar=False,
add_labels=False)
psi.sel(pfull=500).plot.contour(ax=ax,
x='xofyear',
y='lat',
levels=np.arange(-500., 0., 100.),
add_labels=False,
colors='0.7',
linewidths=2,
linestyles='--')
psi.sel(pfull=500).plot.contour(ax=ax,
x='xofyear',
y='lat',
levels=np.arange(0., 510., 100.),
add_labels=False,
colors='0.7',
linewidths=2)
psi.sel(pfull=500).plot.contour(ax=ax,
x='xofyear',
y='lat',
levels=np.arange(-1000., 1010., 1000.),
add_labels=False,
colors='0.5',
linewidths=2)
data.p_cent.plot.line(ax=ax, color='k', linewidth=2)
ax.set_ylim([-60, 60])
ax.set_ylabel('Latitude')
ax.set_xticks([12, 24, 36, 48, 60, 72])
ax.set_yticks([-60, -30, 0, 30, 60])
ax.set_xlabel('Pentad')
ax.grid(True, linestyle=':')
box = ax.get_position()
axColor = plt.axes(
[box.x0 + box.width * 0.92, box.y0 + box.y0 * 0.12, 0.015, box.height])
cb1 = fig.colorbar(f1, cax=axColor, orientation='vertical')
def vdtdy_plot(run, ax, lev=850.):
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' +
run + '.nc')
psi = mass_streamfunction(data, a=6376.0e3, dp_in=50.)
psi /= 1.e9
try:
data['precipitation'] = make_sym(data.precipitation)
except:
data['precipitation'] = data.convection_rain + data.condensation_rain
data['precipitation'] = make_sym(data.precipitation)
precip_centroid(data)
convTtotheta = (1000. / data.pfull)**(2. / 7.)
theta = data.temp * convTtotheta
rho = lev * 100. / Rd / data.temp.sel(
pfull=lev) / (1 + (Rv - Rd) / Rd * data.sphum.sel(pfull=lev))
expansion_term = (1. / rho / cp *
data.omega.sel(pfull=lev)).mean('lon') * 86400.
#expansion_term = (1./rho/cp ).mean('lon') * 86400.
v = data.vcomp.sel(pfull=lev) # v
T_dy = -86400. * gr.ddy(data.temp.sel(pfull=lev), vector=False) # dTdy
vdTdy = v.mean('lon') * T_dy.mean('lon') # [v][dTdy]
w = data.omega.sel(pfull=lev) # w
T_dp = -86400. * (gr.ddp(data.temp)).sel(pfull=lev) # dTdp
wdTdp = w.mean('lon') * T_dp.mean('lon') # [w][dTdp]
dTdt = gr.ddt(data.temp).sel(pfull=lev).mean('lon') * 86400.
#dvtdy = -1.*(gr.ddy(data.vcomp * data.temp)).mean('lon').sel(pfull=lev)*86400.
#dwtdp = -1.*(gr.ddp(data.omega * data.temp)).mean('lon').sel(pfull=lev)*86400.
#dvtdy_colav = -1.*gr.ddy((data.vcomp * theta).mean('lon').sel(pfull=np.arange(50.,501.,50.)).mean('pfull')*5000./9.8)*1004.64
#dvHdy_colav = -1.*gr.ddy((data.vcomp * (data.temp*1004.64 + data.sphum*2.500e6)).mean('lon').mean('pfull')*5000./9.8)
#f1 = (dvtdy_colav).plot.contourf(ax=ax, x='xofyear', y='lat', add_labels=False, levels=np.arange(-30.,33.,3.), extend='both', add_colorbar=False)
#f1 = (vdTdy).plot.contourf(ax=ax, x='xofyear', y='lat', add_labels=False, levels=np.arange(-5.,5.5,0.5), extend='both', add_colorbar=False)
f1 = (vdTdy + wdTdp).plot.contourf(ax=ax,
x='xofyear',
y='lat',
add_labels=False,
levels=np.arange(-3., 3.1, 0.5),
extend='both',
add_colorbar=False)
#f1 = (dTdt).plot.contourf(ax=ax, x='xofyear', y='lat', add_labels=False, levels=np.arange(-0.2,0.21,0.02), extend='both', add_colorbar=False)
#f1 = (vdTdy + wdTdp + expansion_term).plot.contourf(ax=ax, x='xofyear', y='lat', add_labels=False, extend='both', add_colorbar=False)
#f1 = (-1.*expansion_term-T_dp.mean('lon')).plot.contourf(ax=ax, x='xofyear', y='lat', add_labels=False, extend='both', add_colorbar=False)
psi.sel(pfull=500).plot.contour(ax=ax,
x='xofyear',
y='lat',
levels=np.arange(-500., 0., 100.),
add_labels=False,
colors='0.7',
linewidths=2,
linestyles='--')
psi.sel(pfull=500).plot.contour(ax=ax,
x='xofyear',
y='lat',
levels=np.arange(0., 510., 100.),
add_labels=False,
colors='0.7',
linewidths=2)
psi.sel(pfull=500).plot.contour(ax=ax,
x='xofyear',
y='lat',
levels=np.arange(-1000., 1010., 1000.),
add_labels=False,
colors='0.5',
linewidths=2)
data.p_cent.plot.line(ax=ax, color='k', linewidth=2)
ax.set_xlabel('')
ax.set_ylim([-60, 60])
ax.set_ylabel('Latitude')
ax.set_xticks([12, 24, 36, 48, 60, 72])
ax.set_yticks([-60, -30, 0, 30, 60])
ax.grid(True, linestyle=':')
return f1
def abs_vort_dt_plot(run, ax, rot_fac=1., lev=150., dvordt_flag=False):
'''Plot dvordt or 1/vor * dvordt'''
#Load data
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' + run + '.nc')
#Calculate psi to overplot
psi = mass_streamfunction(data, a=6376.0e3, dp_in=50.)
psi /= 1.e9
psi = make_sym(psi, asym=True)
# Make precip symmetric and find the precip centroid
data['precipitation'] = make_sym(data.precipitation)
precip_centroid(data)
# Calculate vorticity
v_dx = gr.ddx(data.vcomp) # dvdx
u_dy = gr.ddy(data.ucomp) # dudy
omega = 7.2921150e-5 * rot_fac
f = 2 * omega * np.sin(data.lat *np.pi/180)
vor = (v_dx - u_dy + f).sel(pfull=lev)*86400.
vor = make_sym(vor, asym=True)
# Take time derivative of absolute vorticity
dvordt = gr.ddt(vor.mean('lon'))*86400.
# Also normalise this by the value of vorticity
dvordtvor = dvordt/vor.mean('lon')
# Plot!
plot_dir = '/scratch/rg419/plots/paper_2_figs/abs_vort_dt/'
mkdir = sh.mkdir.bake('-p')
mkdir(plot_dir)
rcParams['figure.figsize'] = 5.5, 4.3
rcParams['font.size'] = 14
#fig = plt.figure()
#ax1 = fig.add_subplot(111)
if dvordt_flag:
f1 = dvordt.plot.contourf(ax=ax, x='xofyear', y='lat', levels=np.arange(-0.06,0.07,0.01), add_colorbar=False, add_labels=False)
else:
f1 = dvordtvor.plot.contourf(ax=ax, x='xofyear', y='lat', levels=np.arange(-0.05,0.055,0.005), add_colorbar=False, add_labels=False)
#psi.sel(pfull=500).plot.contour(ax=ax, x='xofyear', y='lat', levels=np.arange(-500.,0.,100.), add_labels=False, colors='0.7', linewidths=2, linestyles='--')
#psi.sel(pfull=500).plot.contour(ax=ax, x='xofyear', y='lat', levels=np.arange(0.,510.,100.), add_labels=False, colors='0.7', linewidths=2)
#psi.sel(pfull=500).plot.contour(ax=ax, x='xofyear', y='lat', levels=np.arange(-1000.,1010.,1000.), add_labels=False, colors='0.5', linewidths=2)
data.p_cent.plot.line(ax=ax, color='k', linewidth=2)
ax.set_ylim([-60,60])
ax.set_ylabel('Latitude')
ax.set_xticks([12,24,36,48,60,72])
ax.set_yticks([-60,-30,0,30,60])
ax.set_xlabel('Pentad')
ax.grid(True,linestyle=':')
#originalSize = get(gca, 'Position')
#set(f1, 'Position', originalSize)
#plt.subplots_adjust(left=0.15, right=0.95, top=0.95, bottom=0.05)
box = ax.get_position()
#ax.set_position([box.x0*1.05, box.y0, box.width, box.height])
axColor = plt.axes([box.x0 + box.width*0.92, box.y0+box.y0*0.12, 0.015, box.height])
#cb1=fig.colorbar(f1, ax=ax, use_gridspec=True, orientation = 'horizontal',fraction=0.15, pad=0.2, aspect=40)
#cb1=plt.colorbar(f1, ax=axColor, use_gridspec=True, orientation = 'vertical',fraction=0.15, pad=0.05, aspect=30)
cb1=fig.colorbar(f1, cax=axColor, orientation = 'vertical')
def rossby_plot(run,
ax,
rot_fac=1.,
lev=200.,
type='vor_only',
plottype='rossby'):
'''Plot dvordt or 1/vor * dvordt'''
#Load data
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' +
run + '.nc')
#Calculate psi to overplot
psi = mass_streamfunction(data, a=6376.0e3, dp_in=50.)
psi /= 1.e9
psi = make_sym(psi, asym=True)
# Make precip symmetric and find the precip centroid
data['precipitation'] = make_sym(data.precipitation)
data['ucomp'] = make_sym(data.ucomp)
data['vcomp'] = make_sym(data.vcomp, asym=True)
data['omega'] = make_sym(data.omega)
precip_centroid(data)
# Calculate vorticity
v_dx = gr.ddx(data.vcomp) # dvdx
u_dy = gr.ddy(data.ucomp) # dudy
u_dp = gr.ddp(data.ucomp) # dudp
omega = 7.2921150e-5 * rot_fac
f = 2 * omega * np.sin(data.lat * np.pi / 180)
vor = (v_dx - u_dy).sel(pfull=lev)
metric = (data.ucomp / mc.a *
np.tan(data.lat * np.pi / 180)).sel(pfull=lev)
vertical_term = (-1. * data.omega / data.vcomp * u_dp).sel(pfull=lev)
#vor = make_sym(vor, asym=True)
#metric = make_sym(metric, asym=True)
#vertical_term = make_sym(vertical_term, asym=True)
if type == 'vor_only':
rossby = (-1. * vor / f).mean('lon')
elif type == 'metric':
rossby = (-1. * (metric + vor) / f).mean('lon')
elif type == 'vertical':
rossby = (-1. * (vor + vertical_term) / f).mean('lon')
elif type == 'full':
rossby = (-1. * (metric + vor + vertical_term) / f).mean('lon')
levels = np.arange(-0.9, 1.0, 0.3)
if plottype == 'drodt':
rossby = gr.ddt(rossby) * 84600.
levels = np.arange(-0.1, 0.1, 0.01)
f1 = rossby.plot.contourf(ax=ax,
x='xofyear',
y='lat',
levels=levels,
add_colorbar=False,
add_labels=False,
extend='both')
psi.sel(pfull=lev).plot.contour(ax=ax,
x='xofyear',
y='lat',
levels=np.arange(-500., 0., 100.),
add_labels=False,
colors='0.7',
linewidths=2,
linestyles='--')
psi.sel(pfull=lev).plot.contour(ax=ax,
x='xofyear',
y='lat',
levels=np.arange(0., 510., 100.),
add_labels=False,
colors='0.7',
linewidths=2)
psi.sel(pfull=lev).plot.contour(ax=ax,
x='xofyear',
y='lat',
levels=np.arange(-1000., 1010., 1000.),
add_labels=False,
colors='0.5',
linewidths=2)
data.p_cent.plot.line(ax=ax, color='k', linewidth=2)
ax.set_ylim([-30, 30])
ax.set_ylabel('Latitude')
ax.set_xticks([12, 24, 36, 48, 60, 72])
ax.set_yticks([-30, -15, 0, 15, 30])
ax.set_xlabel('Pentad')
ax.grid(True, linestyle=':')
#plt.subplots_adjust(left=0.15, right=0.95, top=0.95, bottom=0.05)
cb1 = fig.colorbar(f1,
ax=ax,
use_gridspec=True,
orientation='horizontal',
fraction=0.15,
pad=0.2,
aspect=40)
cb1.set_label('Rossby number')
def evap_parts(run, lonin=[-1.,361.], do_make_sym=True):
rcParams['figure.figsize'] = 6, 9
rcParams['font.size'] = 16
plot_dir = '/scratch/rg419/plots/surface_fluxes/'
mkdir = sh.mkdir.bake('-p')
mkdir(plot_dir)
data = xr.open_dataset('/scratch/rg419/Data_moist/climatologies/' + run + '.nc')
lons = pick_lons(data, lonin)
# Absolute wind mag, including 1m/s vertical gust
v_a = np.sqrt(data.ucomp_sq.sel(pfull=950.) + data.vcomp_sq.sel(pfull=950.) + 1.)
# Density of lowest level air
rho_a = 95000./Rd/data.temp.sel(pfull=950.) / (1 + (Rv - Rd)/Rd*data.sphum.sel(pfull=950.))
# difference between lowest level and surface specific humidities
q_s = Rd/Rv * 610.78/100000. * np.exp(-L/Rv * (1/data.temp.sel(pfull=950.) - 1/273.16))
# Look at an average over a zonal region
v_a = v_a.sel(lon=lons).mean('lon')
rho_a = rho_a.sel(lon=lons).mean('lon')
q_a = data.sphum.sel(pfull=950.).sel(lon=lons).mean('lon')
q_s = q_s.sel(lon=lons).mean('lon')
qdiff = (q_a-q_s)*1000.
#(v_a*qdiff).plot.contourf(x='xofyear', y='lat', levels=np.arange(-0.05,0.05,0.005))
#plt.show()
#q_a.plot.contourf(x='xofyear', y='lat', levels=np.arange(0,0.02,0.001))
#plt.figure(2)
#q_s.plot.contourf(x='xofyear', y='lat', levels=np.arange(0,0.02,0.001))
#plt.show()
if do_make_sym:
qdiff = make_sym(qdiff)
rho_a = make_sym(rho_a)
v_a = make_sym(v_a)
f, (ax1, ax2, ax3) = plt.subplots(3, sharex=True)
qdiff.plot.contourf(x='xofyear', y='lat', levels=np.arange(-4.5,4.6,0.5), ax=ax1, extend = 'both', add_labels=False)
ax1.set_title('q$_{a}$ - q$_{s}$')
rho_a.plot.contourf(x='xofyear', y='lat', levels=np.arange(1.,1.41,0.01), ax=ax2, extend = 'both', add_labels=False)
ax2.set_title('rho$_{a}$')
v_a.plot.contourf(x='xofyear', y='lat', levels=np.arange(0.,20.,2.), ax=ax3, extend = 'both', add_labels=False)
ax3.set_title('v$_{a}$')
for ax in [ax1,ax2,ax3]:
ax.set_ylim([-60,60])
ax.set_yticks([-60,-30,0,30,60])
ax.set_ylabel('Latitude')
ax.grid(True,linestyle=':')
ax3.set_xticks([12,24,36,48,60,72])
ax3.set_xlabel('Pentad')
plt.subplots_adjust(left=0.15, right=0.95, top=0.95, bottom=0.1)
plt.savefig(plot_dir + 'evap_parts_' + run + '.pdf', format='pdf')
plt.close()
]
else:
lons = [
data.lon[i] for i in range(len(data.lon))
if data.lon[i] >= lonin[0] or data.lon[i] < lonin[1]
]
return lons
plot_dir = '/scratch/rg419/plots/itcz_plots/'
mkdir = sh.mkdir.bake('-p')
mkdir(plot_dir)
data = xr.open_dataset(
'/disca/share/rg419/Data_moist/climatologies/half_shallow.nc')
data['precipitation'] = make_sym(data.precipitation) * 86400.
data_gpcp = xr.open_dataset(
'/scratch/rg419/obs_and_reanalysis/gpcp_detrendedpentads.nc')
data_gpcp = data_gpcp.rename({'precip_clim': 'precipitation'})
mn_dic = month_dic(1)
tickspace = [13, 31, 49, 68]
labels = ['Mar', 'Jun', 'Sep', 'Dec']
# Set figure parameters
rcParams['figure.figsize'] = 15, 6
rcParams['font.size'] = 16
# Start figure with 6 subplots
fig, ((ax1, ax2, ax3), (ax4, ax5, ax6)) = plt.subplots(2,
3,
def surface_plot(run,
lonin=[-1., 361.],
diff_run=None,
scale_fac=1.,
do_make_sym=True):
print('what?')
rcParams['figure.figsize'] = 15, 10
rcParams['font.size'] = 14
plot_dir = '/scratch/rg419/plots/surface_fluxes/'
mkdir = sh.mkdir.bake('-p')
mkdir(plot_dir)
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' +
run + '.nc')
#Calculate psi to overplot
psi = mass_streamfunction(data, a=6376.0e3, dp_in=50.)
psi /= 1.e9
psi = make_sym(psi, asym=True)
# Make precip symmetric and find the precip centroid
data['precipitation'] = make_sym(data.precipitation)
precip_centroid(data)
data['flux_lw_up'] = data.t_surf**4. * mc.stefan
data['dTdt'] = gr.ddt(data.t_surf) * 86400. * scale_fac
if do_make_sym:
for field in [
't_surf', 'dTdt', 'flux_sw', 'flux_lw', 'flux_lw_up', 'flux_t',
'flux_lhe'
]:
data[field] = make_sym(data[field])
lons = pick_lons(data, lonin)
if not diff_run == None:
diff_data = xr.open_dataset(
'/disca/share/rg419/Data_moist/climatologies/' + diff_run + '.nc')
data = (data - diff_data).sel(lon=lons).mean('lon')
levels_t = np.arange(-10., 11., 1.)
levels_dt = np.arange(-0.5, 0.51, 0.05)
levels_flux = np.arange(-80., 81., 5.)
else:
data = data.sel(lon=lons).mean('lon')
levels_t = np.arange(270., 306., 2.5)
levels_dt = np.arange(-0.3, 0.31, 0.05)
levels_flux = np.arange(-300., 305., 20.)
f, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots(3,
2,
sharex='col',
sharey='row')
left_column = [ax1, ax3, ax5]
bottom_row = [ax5, ax6]
all_plots = [ax1, ax2, ax3, ax4, ax5, ax6]
#check = data.flux_sw + (data.flux_lw - flux_lw_up) - data.flux_t - data.flux_lhe
#check = data.flux_sw - data.flux_lhe
data.t_surf.plot.contourf(x='xofyear',
y='lat',
levels=levels_t,
ax=ax1,
extend='both',
add_labels=False)
ax1.set_title('SST')
data.dTdt.plot.contourf(x='xofyear',
y='lat',
ax=ax2,
levels=levels_dt,
extend='both',
add_labels=False,
cmap='RdBu_r')
ax2.set_title('d(SST)/dt')
data.flux_sw.plot.contourf(x='xofyear',
y='lat',
levels=levels_flux,
ax=ax3,
extend='both',
add_labels=False,
cmap='RdBu_r')
ax3.set_title('Net downward SW flux')
(data.flux_lw - data.flux_lw_up).plot.contourf(x='xofyear',
y='lat',
levels=levels_flux,
ax=ax4,
extend='both',
add_labels=False,
cmap='RdBu_r')
ax4.set_title('Net downward LW flux')
(-1. * data.flux_t).plot.contourf(x='xofyear',
y='lat',
levels=levels_flux,
ax=ax5,
extend='both',
add_labels=False,
cmap='RdBu_r')
#check.plot.contourf(x='xofyear', y='lat', levels=np.arange(-300.,305.,20.), ax=ax5, extend = 'both', add_labels=False, cmap='RdBu_r')
ax5.set_title('Downward sensible heat flux')
(-1. * data.flux_lhe).plot.contourf(x='xofyear',
y='lat',
levels=levels_flux,
ax=ax6,
extend='both',
add_labels=False,
cmap='RdBu_r')
ax6.set_title('Downward latent heat flux')
i = 0
labels = ['a)', 'b)', 'c)', 'd)', 'e)', 'f)']
for ax in all_plots:
ax.grid(True, linestyle=':')
ax.set_xticks([12, 24, 36, 48, 60, 72])
ax.set_ylim([-60, 60])
ax.set_yticks([-60, -30, 0, 30, 60])
ax.text(-8, 60., labels[i])
psi.sel(pfull=500).plot.contour(ax=ax,
x='xofyear',
y='lat',
levels=np.arange(-500., 0., 100.),
add_labels=False,
colors='0.7',
linewidths=2,
linestyles='--')
psi.sel(pfull=500).plot.contour(ax=ax,
x='xofyear',
y='lat',
levels=np.arange(0., 510., 100.),
add_labels=False,
colors='0.7',
linewidths=2)
psi.sel(pfull=500).plot.contour(ax=ax,
x='xofyear',
y='lat',
levels=np.arange(-1000., 1010., 1000.),
add_labels=False,
colors='0.5',
linewidths=2)
data.p_cent.plot.line(ax=ax, color='k', linewidth=2)
ax.set_ylabel('')
ax.set_xlabel('')
i = i + 1
for ax in left_column:
ax.set_ylabel('Latitude')
for ax in bottom_row:
ax.set_xlabel('Pentad')
plt.subplots_adjust(left=0.07,
right=0.97,
top=0.97,
bottom=0.05,
hspace=0.2,
wspace=0.1)
if lonin == [-1., 361.]:
if not diff_run == None:
plt.savefig(plot_dir + 'surface_fluxes_' + run + '_' + diff_run +
'.pdf',
format='pdf')
else:
plt.savefig(plot_dir + 'surface_fluxes_' + run + '.pdf',
format='pdf')
else:
figname = plot_dir + 'surface_fluxes_' + run + '_' + str(int(
lonin[0])) + '_' + str(int(lonin[1])) + '.pdf'
plt.savefig(figname, format='pdf')
plt.close()
def evap_parts(run, lonin=[-1., 361.], do_make_sym=True):
rcParams['figure.figsize'] = 6, 9
rcParams['font.size'] = 14
plot_dir = '/scratch/rg419/plots/surface_fluxes/'
mkdir = sh.mkdir.bake('-p')
mkdir(plot_dir)
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' +
run + '.nc')
#Calculate psi to overplot
psi = mass_streamfunction(data, a=6376.0e3, dp_in=50.)
psi /= 1.e9
psi = make_sym(psi, asym=True)
# Make precip symmetric and find the precip centroid
data['precipitation'] = make_sym(data.precipitation)
precip_centroid(data)
lons = pick_lons(data, lonin)
# Density of lowest level air
#rho_a = 95000./Rd/data.temp.sel(pfull=950.) / (1 + (Rv - Rd)/Rd*data.sphum.sel(pfull=950.))
# Look at an average over a zonal region
v_a = data.wind_speed.sel(lon=lons).mean('lon')
q_a = data.q_atm.sel(lon=lons).mean('lon')
q_s = data.q_surf.sel(lon=lons).mean('lon')
qdiff = (q_a - q_s) * 1000.
rho_a = data.rho.sel(lon=lons).mean('lon')
#rho_a.plot.contourf()
#plt.colorbar()
#plt.show()
#(v_a*qdiff).plot.contourf(x='xofyear', y='lat', levels=np.arange(-0.05,0.05,0.005))
#plt.show()
#q_a.plot.contourf(x='xofyear', y='lat', levels=np.arange(0,0.02,0.001))
#plt.figure(2)
#q_s.plot.contourf(x='xofyear', y='lat', levels=np.arange(0,0.02,0.001))
#plt.show()
if do_make_sym:
qdiff = make_sym(qdiff)
rho_a = make_sym(rho_a)
v_a = make_sym(v_a)
f, (ax1, ax2, ax3) = plt.subplots(3, sharex=True)
qdiff.plot.contourf(x='xofyear',
y='lat',
ax=ax1,
extend='both',
add_labels=False,
levels=np.arange(-10., 10.1, 1.))
ax1.set_title('q$_{a}$ - q$_{s}$')
rho_a.plot.contourf(x='xofyear',
y='lat',
ax=ax2,
extend='both',
add_labels=False,
levels=np.arange(1.12, 1.26, 0.01))
ax2.set_title(r'$\rho_{a}$')
v_a.plot.contourf(x='xofyear',
y='lat',
levels=np.arange(0., 20., 2.),
ax=ax3,
extend='both',
add_labels=False)
ax3.set_title('v$_{a}$')
labels = ['a)', 'b)', 'c)']
i = 0
for ax in [ax1, ax2, ax3]:
#psi.sel(pfull=500).plot.contour(ax=ax, x='xofyear', y='lat', levels=np.arange(-500.,0.,100.), add_labels=False, colors='0.7', linewidths=2, linestyles='--')
#psi.sel(pfull=500).plot.contour(ax=ax, x='xofyear', y='lat', levels=np.arange(0.,510.,100.), add_labels=False, colors='0.7', linewidths=2)
#psi.sel(pfull=500).plot.contour(ax=ax, x='xofyear', y='lat', levels=np.arange(-1000.,1010.,1000.), add_labels=False, colors='0.5', linewidths=2)
#data.p_cent.plot.line(ax=ax, color='k', linewidth=2)
#ax.set_xlabel('')
ax.set_ylim([-60, 60])
ax.set_yticks([-60, -30, 0, 30, 60])
ax.set_ylabel('Latitude')
ax.text(-10, 60., labels[i])
ax.grid(True, linestyle=':')
i = i + 1
ax3.set_xticks([12, 24, 36, 48, 60, 72])
ax3.set_xlabel('Pentad')
plt.subplots_adjust(left=0.12, right=0.99, top=0.95, bottom=0.06)
plt.savefig(plot_dir + 'evap_parts_revisions_' + run + '.pdf',
format='pdf')
plt.close()
def p_cent_rate_max(runs,
do_make_sym=True,
months=None,
days=None,
period_fac=1.):
# Get the maximum rate of change of the precipitation centroid latitude, and the latitude at which this occurs.
# Ensure runs is a list not a string
if not isinstance(runs, list):
runs = [runs]
if not isinstance(period_fac, list):
period_fac = [period_fac] * len(runs)
# Set up empty lists
max_rate = []
max_rate_lat = []
max_lat = []
if not do_make_sym: # If we're not averaging both hemispheres, get southern peak magnitude and location too
max_rate_s = []
max_rate_lat_s = []
max_lat_s = []
if days == None:
days = [False] * len(runs)
j = 0
for run in runs:
if months == None: # If no months provided, look for a climatology
# Open dataset
data = xr.open_dataset(
'/disca/share/rg419/Data_moist/climatologies/' + run + '.nc')
# Get total precip
try:
data[
'precipitation'] = data.condensation_rain + data.convection_rain
except:
data['precipitation'] = data.precipitation
else: # If months are provided, load the data for these, and reshape to years/day of year
try: # First make sure months is a list of lists
len(months[0])
except:
months = [months]
data = precip_load_and_reshape(run,
months=months[j],
period_fac=period_fac[j])
if do_make_sym: # If symmetric data is wanted, average over both hemispheres (NB currently only set up for a climatology 2/05/18)
data['precipitation'] = make_sym(data.precipitation)
# Locate precipitation centroid
precip_centroid(data)
# Get rate of movement of precip centroid
if days[j]:
dpcentdt = gr.ddt(data.p_cent, secperunit=86400.) * 86400.
else:
dpcentdt = gr.ddt(data.p_cent) * 86400.
def get_max(
dpcentdt_pmask,
sh=False
): # Function to find the maximum ratea and it's latitude and the maximum latitude.
dpcentdt_max = dpcentdt_pmask.where(
dpcentdt_pmask == dpcentdt_pmask.max('xofyear'),
drop=True) # Find the maximum rate
numdims = len(
dpcentdt_max.shape
) # Check if this is just 1 year or if it's many years
if numdims != 1: # If it's many years, evaluate the metrics for each year
dpdt_max_temp = [] #First set up empty lists
pcent_dtmax = []
pcent_max = []
for y in range(dpcentdt_max.shape[0]): # Loop over years
if sh: #If it's the southern hemisphere, look for the min latitude of pcent, and make it positive
#data.p_cent.sel(year_no=y).plot()
#plt.show()
pcent_max.append(
-1. * data.p_cent.sel(year_no=y).min('xofyear'))
else: # Otherwise look for the maximum latitude
pcent_max.append(
data.p_cent.sel(year_no=y).max('xofyear'))
# Find the maximum rate for the given year, remove any nans that have been added. If there are multiple values, use the smallest
dpdt_max_temp.append(
dpcentdt_max[y, :].dropna('xofyear')[0])
# Find the location of the max rate for that year
pcent_dtmax.append(
data.p_cent.sel(
year_no=y,
xofyear=dpdt_max_temp[y].xofyear.values))
# Convert all results to datarrays
pcent_dtmax = xr.DataArray(np.asarray(pcent_dtmax),
coords=[data.year_no.values],
dims=['year_no'])
dpcentdt_max = xr.DataArray(np.asarray(dpdt_max_temp),
coords=[data.year_no.values],
dims=['year_no'])
pcent_max = xr.DataArray(np.asarray(pcent_max),
coords=[data.year_no.values],
dims=['year_no'])
else: # If it's only 1D
if len(
dpcentdt_max
) > 1: # If there are multiple values for the maximum take the smallest
dpcentdt_max = dpcentdt_max[0].expand_dims('xofyear',
axis=0)
if sh: # If it's the southern hemisphere find the min lat of pcent, and make it positive
pcent_max = -1. * data.p_cent.min('xofyear')
else: # Otherwise find the maximum latitude
pcent_max = data.p_cent.max('xofyear')
pcent_max = np.expand_dims(pcent_max, axis=1)
pcent_dtmax = data.p_cent.sel(
xofyear=dpcentdt_max.xofyear
) # Find the location of the preciptiation when the rate is maximum
return dpcentdt_max, pcent_dtmax, pcent_max
dpcentdt_ppos = dpcentdt.where(
data.p_cent >= 0.
) # Find precip centroid rate where precip centroid is in the northern hemisphere
dpcentdt_max, pcent_dtmax, pcent_max = get_max(
dpcentdt_ppos) # Get the maximum etc
max_rate.append(dpcentdt_max) # Add values to lists for different runs
max_rate_lat.append(pcent_dtmax)
max_lat.append(pcent_max)
if not do_make_sym:
dpcentdt_pneg = -1. * dpcentdt.where(
data.p_cent <= 0.
) # Find precip centroid rate where precip centroid is in the southern hemisphere, and make poleward positive
dpcentdt_max_s, pcent_dtmax_s, pcent_max_s = get_max(
dpcentdt_pneg, sh=True) # Get the maximum etc
max_rate_s.append(
dpcentdt_max_s) # Add values to lists for different runs
max_rate_lat_s.append(-1. * pcent_dtmax_s)
max_lat_s.append(pcent_max_s)
j = j + 1 # Add 1 to counter to get values for next run
# Convert all output to xarrays
if do_make_sym:
if months == None:
coords_yrno = ['clim']
else:
coords_yrno = data.year_no.values
max_rate = xr.DataArray(np.asarray(max_rate),
coords=[runs, coords_yrno],
dims=['run', 'year_no'])
max_rate_lat = xr.DataArray(np.asarray(max_rate_lat),
coords=[runs, coords_yrno],
dims=['run', 'year_no'])
max_lat = xr.DataArray(np.asarray(max_lat),
coords=[runs, coords_yrno],
dims=['run', 'year_no'])
else:
if months == None:
coords_yrno = ['clim']
else:
coords_yrno = data.year_no.values
max_rate = (xr.DataArray(np.asarray([max_rate, max_rate_s]),
coords=[['n', 's'], runs, coords_yrno],
dims=['hemisphere', 'run', 'year_no'
])).transpose('run', 'year_no',
'hemisphere')
max_rate_lat = (xr.DataArray(np.asarray([max_rate_lat,
max_rate_lat_s]),
coords=[['n', 's'], runs, coords_yrno],
dims=['hemisphere', 'run',
'year_no'])).transpose(
'run', 'year_no', 'hemisphere')
max_lat = (xr.DataArray(np.asarray([max_lat, max_lat_s]),
coords=[['n', 's'], runs, coords_yrno],
dims=['hemisphere', 'run', 'year_no'
])).transpose('run', 'year_no',
'hemisphere')
return max_rate, max_rate_lat, max_lat
def vdtdy_plot(run, lev=850.):
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' + run + '.nc')
psi = mass_streamfunction(data, a=6376.0e3, dp_in=50.)
psi /= 1.e9
try:
data['precipitation'] = make_sym(data.precipitation)
except:
data['precipitation'] = data.convection_rain + data.condensation_rain
data['precipitation'] = make_sym(data.precipitation)
precip_centroid(data)
convTtotheta=(1000./data.pfull)**(2./7.)
theta = data.temp*convTtotheta
#rcParams['figure.figsize'] = 5.5, 4.3
#theta.mean('lon').sel(xofyear=40).plot.contourf(x='lat', y='pfull', yincrease=False)
#plt.show()
dvtdy = -1.*(gr.ddy(data.vcomp * theta)).mean('lon').sel(pfull=lev)*86400.
dwtdp = -1.*(gr.ddp(data.omega * theta)).mean('lon').sel(pfull=lev)*86400.
adv_tend_grad = gr.ddy(dvtdy+dwtdp, vector=False)*10.**5.
dTdt = (gr.ddt(theta)).mean('lon').sel(pfull=lev)*86400./theta.mean('lon').sel(pfull=lev)
dvtdy_colav = -1.*gr.ddy((data.vcomp * theta).mean('lon').mean('pfull')*5000./9.8)*1004.64
#dwtdp = -1.*gr.ddp((data.omega * theta).mean('lon').sum('pfull')*50./9.8)
dTdt_colav = gr.ddt(theta.mean('lon').sum('pfull')*5000./9.8)*1004.64
plot_dir = '/scratch/rg419/plots/paper_2_figs/dvtdy/'
mkdir = sh.mkdir.bake('-p')
mkdir(plot_dir)
#rcParams['figure.figsize'] = 5.5, 4.3
rcParams['figure.figsize'] = 5, 10.5
rcParams['font.size'] = 14
#rcParams['font.size'] = 16
#fig = plt.figure()
#ax1 = fig.add_subplot(111)
fig, ((ax1), (ax2), (ax3)) = plt.subplots(3, 1)
f1 = (dvtdy+dwtdp).plot.contourf(ax=ax1, x='xofyear', y='lat', add_labels=False, levels=np.arange(-50.,55.,5.), extend='both')
psi.sel(pfull=500).plot.contour(ax=ax1, x='xofyear', y='lat', levels=np.arange(-500.,0.,100.), add_labels=False, colors='0.7', linewidths=2, linestyles='--')
psi.sel(pfull=500).plot.contour(ax=ax1, x='xofyear', y='lat', levels=np.arange(0.,510.,100.), add_labels=False, colors='0.7', linewidths=2)
psi.sel(pfull=500).plot.contour(ax=ax1, x='xofyear', y='lat', levels=np.arange(-1000.,1010.,1000.), add_labels=False, colors='0.5', linewidths=2)
data.p_cent.plot.line(ax=ax1,color='k', linewidth=2)
ax1.set_ylim([-60,60])
ax1.set_ylabel('Latitude')
ax1.set_xticks([12,24,36,48,60,72])
ax1.set_yticks([-60,-30,0,30,60])
ax1.set_xlabel('Pentad')
ax1.grid(True,linestyle=':')
f1 = adv_tend_grad.plot.contourf(ax=ax2, x='xofyear', y='lat', add_labels=False, levels=np.arange(-10.,11.,1.), extend='both')
psi.sel(pfull=500).plot.contour(ax=ax2, x='xofyear', y='lat', levels=np.arange(-500.,0.,100.), add_labels=False, colors='0.7', linewidths=2, linestyles='--')
psi.sel(pfull=500).plot.contour(ax=ax2, x='xofyear', y='lat', levels=np.arange(0.,510.,100.), add_labels=False, colors='0.7', linewidths=2)
psi.sel(pfull=500).plot.contour(ax=ax2, x='xofyear', y='lat', levels=np.arange(-1000.,1010.,1000.), add_labels=False, colors='0.5', linewidths=2)
data.p_cent.plot.line(ax=ax2,color='k', linewidth=2)
ax2.set_ylim([-60,60])
ax2.set_ylabel('Latitude')
ax2.set_xticks([12,24,36,48,60,72])
ax2.set_yticks([-60,-30,0,30,60])
ax2.set_xlabel('Pentad')
ax2.grid(True,linestyle=':')
#f1 = dvtdy_colav.plot.contourf(ax=ax3, x='xofyear', y='lat', add_labels=False, levels=np.arange(-50.,51.,5.), extend='both')
#psi.sel(pfull=500).plot.contour(ax=ax3, x='xofyear', y='lat', levels=np.arange(-500.,0.,100.), add_labels=False, colors='0.7', linewidths=2, linestyles='--')
#psi.sel(pfull=500).plot.contour(ax=ax3, x='xofyear', y='lat', levels=np.arange(0.,510.,100.), add_labels=False, colors='0.7', linewidths=2)
#psi.sel(pfull=500).plot.contour(ax=ax3, x='xofyear', y='lat', levels=np.arange(-1000.,1010.,1000.), add_labels=False, colors='0.5', linewidths=2)
#data.p_cent.plot.line(ax=ax3,color='k', linewidth=2)
#ax3.set_ylim([-60,60])
#ax3.set_ylabel('Latitude')
#ax3.set_xticks([12,24,36,48,60,72])
#ax3.set_yticks([-60,-30,0,30,60])
#ax3.set_xlabel('Pentad')
#ax3.grid(True,linestyle=':')
plt.subplots_adjust(left=0.15, right=0.95, top=0.95, bottom=0.05)
# cb1=fig.colorbar(f1, ax=ax1, use_gridspec=True, orientation = 'horizontal',fraction=0.15, pad=0.2, aspect=40)
# cb1.set_label('Advective temperature tendency, Kday$^{-1}$')
plt.savefig(plot_dir+'adv_tend_' + run + '.pdf', format='pdf')
plt.close()
def surface_plot(run,
lonin=[-1., 361.],
diff_run=None,
scale_fac=1.,
do_make_sym=True):
rcParams['figure.figsize'] = 12, 10
rcParams['font.size'] = 16
plot_dir = '/scratch/rg419/plots/surface_fluxes/'
mkdir = sh.mkdir.bake('-p')
mkdir(plot_dir)
data = xr.open_dataset('/scratch/rg419/Data_moist/climatologies/' + run +
'.nc')
data['flux_lw_up'] = data.t_surf**4. * mc.stefan
data['dTdt'] = gr.ddt(data.t_surf) * 86400. * scale_fac
if do_make_sym:
for field in [
't_surf', 'dTdt', 'flux_sw', 'flux_lw', 'flux_lw_up', 'flux_t',
'flux_lhe'
]:
data[field] = make_sym(data[field])
lons = pick_lons(data, lonin)
if not diff_run == None:
diff_data = xr.open_dataset(
'/scratch/rg419/Data_moist/climatologies/' + diff_run + '.nc')
data = (data - diff_data).sel(lon=lons).mean('lon')
levels_t = np.arange(-10., 11., 1.)
levels_dt = np.arange(-0.5, 0.51, 0.05)
levels_flux = np.arange(-80., 81., 5.)
else:
data = data.sel(lon=lons).mean('lon')
levels_t = np.arange(250., 311., 1.)
levels_dt = np.arange(-0.5, 0.51, 0.01)
levels_flux = np.arange(-300., 305., 10.)
f, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots(3,
2,
sharex='col',
sharey='row')
left_column = [ax1, ax3, ax5]
bottom_row = [ax5, ax6]
all_plots = [ax1, ax2, ax3, ax4, ax5, ax6]
#check = data.flux_sw + (data.flux_lw - flux_lw_up) - data.flux_t - data.flux_lhe
#check = data.flux_sw - data.flux_lhe
data.t_surf.plot.contourf(x='xofyear',
y='lat',
levels=levels_t,
ax=ax1,
extend='both',
add_labels=False)
ax1.set_title('SST')
data.dTdt.plot.contourf(x='xofyear',
y='lat',
ax=ax2,
levels=levels_dt,
extend='both',
add_labels=False,
cmap='RdBu_r')
ax2.set_title('d(SST)/dt')
data.flux_sw.plot.contourf(x='xofyear',
y='lat',
levels=levels_flux,
ax=ax3,
extend='both',
add_labels=False,
cmap='RdBu_r')
ax3.set_title('Net downward SW flux')
(data.flux_lw - data.flux_lw_up).plot.contourf(x='xofyear',
y='lat',
levels=levels_flux,
ax=ax4,
extend='both',
add_labels=False,
cmap='RdBu_r')
ax4.set_title('Net downward LW flux')
(-1. * data.flux_t).plot.contourf(x='xofyear',
y='lat',
levels=levels_flux,
ax=ax5,
extend='both',
add_labels=False,
cmap='RdBu_r')
#check.plot.contourf(x='xofyear', y='lat', levels=np.arange(-300.,305.,20.), ax=ax5, extend = 'both', add_labels=False, cmap='RdBu_r')
ax5.set_title('Downward sensible heat flux')
(-1. * data.flux_lhe).plot.contourf(x='xofyear',
y='lat',
levels=levels_flux,
ax=ax6,
extend='both',
add_labels=False,
cmap='RdBu_r')
ax6.set_title('Downward latent heat flux')
for ax in left_column:
ax.set_ylabel('Latitude')
for ax in bottom_row:
ax.set_xlabel('Pentad')
for ax in all_plots:
ax.grid(True, linestyle=':')
ax.set_xticks([12, 24, 36, 48, 60, 72])
ax.set_ylim([-60, 60])
if not diff_run == None:
plt.savefig(plot_dir + 'surface_fluxes_' + run + '_' + diff_run +
'.pdf',
format='pdf')
else:
plt.savefig(plot_dir + 'surface_fluxes_' + run + '.pdf', format='pdf')
plt.close()
