def sf_spinup(run, months_list, filenames=['plev_pentad']):
# Function to open files for a specfied month range and filename.
def open_files(run, months, filename):
name_temp = '/disca/share/rg419/Data_moist/' + run + '/run%04d/' + filename + '.nc'
names = [name_temp % m for m in range(months[0], months[1])]
data = xr.open_mfdataset(names,
decode_times=False,
chunks={'time': 30})
# Reduce dataset so that only vcomp is retained
data = xr.Dataset(
{
'vcomp': data.vcomp,
'precipitation': data.precipitation
},
coords=data.vcomp.coords)
#data.coords['month'] = data.time//30 + 1
#data = data.groupby('month').mean(('time'))
return data
arrays = []
i = 0
for filename in filenames:
data = open_files(run, months_list[i], filename)
arrays.append(data)
i = i + 1
data = xr.concat(arrays, dim='time')
precip_centroid(data)
adj_time = np.min(
data.p_cent.time.where(data.p_cent >= 15., drop=True).values)
return data.p_cent, adj_time
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_centroid_rate(run, ax_in, ylim_in=1.):
data = xr.open_dataset('/scratch/rg419/Data_moist/climatologies/' + run + '.nc')
precip_centroid(data)
dpcentdt = gr.ddt(data.p_cent) * 86400.
dpcentdt_ppos = dpcentdt.where(data.p_cent>=0.)
dpcentdt_max = dpcentdt_ppos.where(dpcentdt_ppos==dpcentdt_ppos.max(),drop=True)
pcent_dtmax = data.p_cent.sel(xofyear=dpcentdt_max.xofyear)
print((dpcentdt_max.values, pcent_dtmax.values))
ax_twin = ax_in.twinx()
data.p_cent.plot(ax=ax_twin, color='b')
dpcentdt.plot(ax=ax_in, color='k')
ax_in.set_xlabel('')
ax_in.set_ylabel('')
ax_twin.set_ylabel('')
ax_twin.set_ylim([-30,30])
ax_in.set_ylim([-1.*ylim_in, ylim_in])
ax_in.set_title(run, fontsize=14)
#ax_in.set_yticks([-8.,-4.,0.,4.,8.])
ax_twin.spines['right'].set_color('blue')
plt.tight_layout()
return ax_twin
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 pcent_vs_cell_edge(data, symbol='xk'):
precip_centroid(data)
x = np.linspace(0., 360., 72.)
sinewave = -1.*np.sin(x*np.pi/180.)
coswave = 3.*np.cos(3.*x*np.pi/180.) + 20.*np.cos(3. + x*np.pi/180.)
plt.plot(sinewave, data.p_cent, symbol)
plt.plot(sinewave, coswave)
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 pcent_egrad_scatter(run, ax_in, lonin=[-1.,361.]):
data = xr.open_dataset('/scratch/rg419/Data_moist/climatologies/' + run + '.nc')
precip_centroid(data)
dsdy = 100000.* subcloud_entropy_gradient(data, lonin=lonin)
ax_in.plot(dsdy[:,31:33].mean('lat'), data.p_cent, 'xk')
ax_in.set_xlabel('')
ax_in.set_ylabel('')
ax_in.set_ylim([-30,30])
ax_in.set_xlim([-8,8])
def pcent_grad_scatter(run, ax_in, ylim_in=1.):
data = xr.open_dataset('/scratch/rg419/Data_moist/climatologies/' + run + '.nc')
precip_centroid(data)
dpcentdt = gr.ddt(data.p_cent) * 86400.
ax_in.plot(data.p_cent, dpcentdt, 'xk')
ax_in.set_xlabel('')
ax_in.set_ylabel('')
ax_in.set_xlim([-30,30])
ax_in.set_ylim([-1.*ylim_in, ylim_in])
ax_in.set_title(run, fontsize=14)
def abs_vort_mean(run, rot_fac=1., lev=150.):
data = xr.open_dataset('/scratch/rg419/Data_moist/climatologies/' + run +
'.nc')
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)
# Take gradients of vorticity
dvordx = gr.ddx(vor)
dvordy = gr.ddy(vor, vector=False)
# Horizontal material derivative
horiz_md_mean = (-86400.**2. *
(data.ucomp.sel(pfull=lev) * dvordx +
data.vcomp.sel(pfull=lev) * dvordy)).mean('lon')
# Calculate divergence and stretching term
div = gr.ddx(data.ucomp.sel(pfull=lev)) + gr.ddy(data.vcomp.sel(pfull=lev))
stretching_mean = (-86400.**2. * vor * div).mean('lon')
vor = vor.mean('lon')
vor_mag = np.abs(vor)
stretching_mag = stretching_mean
stretching_mag[:, 0:32] = stretching_mag[:, 0:32] * -1.
lat_itcz, lat_nh, lat_sh = get_streamfun_zeros(data, lev=lev)
def take_cell_average(var, lats_min, lats_max):
cell_av = np.zeros(len(data.xofyear.values))
for i in range(len(data.xofyear.values)):
lats = [
data.lat[j] for j in range(len(data.lat))
if data.lat[j] > lats_min[i] and data.lat[j] < lats_max[i]
]
cell_av[i] = var[i].sel(lat=lats).mean('lat')
return cell_av
vor_av_sh = take_cell_average(vor_mag, lat_sh, lat_itcz)
stretching_av_sh = take_cell_average(stretching_mag, lat_sh, lat_itcz)
plt.plot(stretching_av_sh, 'k')
plt.figure(2)
plt.plot(data.p_cent, stretching_av_sh, 'kx')
plt.show()
def p_cent_rate(data, days=False):
# Locate precipitation centroid
precip_centroid(data)
# If units in are days rather than pentads (e.g. for shorter years) convert to pentads
if days:
data['xofyear'] = data['xofyear'] / 5.
# Get rate of movement of precip centroid
dpcentdt = gr.ddt(data.p_cent) * 86400.
dpcentdt2 = gr.ddt(dpcentdt, secperunit=86400.) * 86400.
return dpcentdt, dpcentdt2
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 bootstrap_method(data, days=False):
max_rate = []
max_rate_lat = []
max_lat = []
eq_rate = []
for boot in range(1000):
print(boot)
sample = bootstrap_sample(data.year_no.size)
precip_sample = data.sel(year_no=sample).mean('year_no')
precip_centroid(precip_sample)
# Next step is to unpick pcent_rate_max function so that you can just hand it the data for the sample mean
# Get the numbers for 1000 or so samples, find 5 and 95 percentile
if days:
dpcentdt = gr.ddt(precip_sample.p_cent, secperunit=86400.) * 86400.
else:
dpcentdt = gr.ddt(precip_sample.p_cent) * 86400.
dpcentdt_ppos = dpcentdt.where(
precip_sample.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('xofyear'), drop=True)
dpcentdt_max = dpcentdt_max[0]
pcent_max = precip_sample.p_cent.max('xofyear')
pcent_dtmax = precip_sample.p_cent.sel(
xofyear=dpcentdt_max.xofyear
) # Find the location of the preciptiation when the rate is maximum
p_cent_abs = np.abs(precip_sample.p_cent.where(dpcentdt >= 0.))
dpdt_eq = (dpcentdt.where(p_cent_abs == p_cent_abs.min('xofyear'),
drop=True)).values[0]
#dpdt_eq = dpcentdt.where(precip_sample.p_cent == precip_sample.p_cent.min('xofyear'), drop=True).max()
max_rate.append(float(dpcentdt_max.values))
max_rate_lat.append(float(pcent_dtmax.values))
max_lat.append(float(pcent_max.values))
eq_rate.append(float(dpdt_eq))
return max_rate, max_rate_lat, max_lat, eq_rate
def load_pcent_find_zero(data, lat_bound=45, lonin=[-1., 361.]):
precip_centroid(data)
pcent_sign = np.ones(data.p_cent.values.shape)
pcent_sign[data.p_cent.values <= 0.] = 0.
pcent_diff = np.diff(pcent_sign)
loc = np.argmax(pcent_diff)
len1 = np.max(pcent_sign.shape) - loc
pcent_repos = np.zeros(data.p_cent.values.shape)
pcent_repos[:len1] = data.p_cent[loc:]
pcent_repos[len1:] = data.p_cent[:loc]
pcent_repos = xr.DataArray(pcent_repos,
coords=[data.xofyear.values],
dims=['xofyear'])
return pcent_repos
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
precip_temp = np.zeros(data.precipitation.values.shape)
n = len(data.xofyear.values) // 2
for i in range(0, n):
precip_temp[i, :, :] = (data.precipitation[i, :, :].values +
data.precipitation[i + n, ::-1, :].values) / 2.
precip_temp[i + n, :, :] = precip_temp[i, ::-1, :]
precip_temp = xr.DataArray(
precip_temp,
coords=[data.xofyear.values, data.lat, data.lon],
dims=['xofyear', 'lat', 'lon'])
data['precipitation'] = precip_temp
# 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 set_vars(data, plottype=None, lonin=[-1.,361.], thresh=0.):
# Load up variables to plot, depending on plot type
edge_loc, psi_max, psi_max_loc = get_edge_psi(data, thresh=thresh, lev=lev, lonin=lonin, nh=True, sanity_check=True)
if plottype == 'mse':
data_mse = peak_mse(data, lonin=lonin)
vars_out = (data_mse.mse_max_loc, psi_max)
elif plottype == 'pcent':
data = precip_centroid(data,lonin=lonin)
vars_out = (data.p_cent, psi_max)
elif plottype == None:
vars_out = (edge_loc, psi_max)
return vars_out
def set_vars(data, type=None, lonin=[-1., 361.], thresh=0.):
edge_loc, psi_max, psi_max_loc = get_edge_psi(data,
thresh=thresh,
lev=lev,
lonin=lonin)
if type == 'mse':
data_mse = peak_mse(data, lonin=lonin)
vars_out = (data_mse.mse_max_loc, psi_max)
elif type == 'pcent':
data = precip_centroid(data, lonin=lonin)
vars_out = (data.p_cent, psi_max)
elif type == None:
vars_out = (edge_loc, psi_max)
return vars_out
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 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 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 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 precip_mse_plot(data,
ax_in,
lonin=[-1., 361.],
do_xlabels=False,
plot_type=None,
precip_contour=8.,
p_cent=True,
mse_max=True,
month_labels=True,
lat_bound=45.):
lons = pick_lons(data, lonin)
try:
precip_plot = (data.precipitation * 86400.).sel(lon=lons).mean('lon')
except:
precip_plot = ((data.convection_rain + data.condensation_rain) *
86400.).sel(lon=lons).mean('lon')
mse_plot = (data.temp * cp + data.sphum * L +
data.height * g).mean('lon') / 1000.
if plot_type == None:
# Default case, plot precip with mse overlaid. Choice of whether or not to highlight a specific precip contour
f1 = precip_plot.plot.contourf(ax=ax_in,
x='xofyear',
y='lat',
levels=np.arange(2., 15., 2.),
add_colorbar=False,
add_labels=False,
extend='max',
cmap='Blues')
if not precip_contour == None:
precip_plot.plot.contour(ax=ax_in,
x='xofyear',
y='lat',
levels=np.arange(precip_contour - 100.,
200., 100.),
add_labels=False,
add_colorbar=False,
colors='k',
linewidth=2)
cs = mse_plot.sel(pfull=850.).plot.contour(ax=ax_in,
x='xofyear',
y='lat',
levels=np.arange(
200., 401., 10.),
add_labels=False,
colors='0.7',
add_colorbar=False,
linewidths=2)
plt.clabel(cs, fontsize=15, inline_spacing=-1, fmt='%1.0f')
if p_cent:
data = precip_centroid(data, lonin=lonin, lat_bound=lat_bound)
data.p_cent.plot.line(color='w', ax=ax_in)
ax_in.set_xlabel('')
elif plot_type == 'precip':
# No mse, plot precip and precip centroid
f1 = precip_plot.plot.contourf(ax=ax_in,
x='xofyear',
y='lat',
levels=np.arange(3., 16., 3.),
add_colorbar=False,
add_labels=False,
extend='max',
cmap='Blues',
linewidth=2)
if p_cent:
data = precip_centroid(data, lonin=lonin, lat_bound=lat_bound)
data.p_cent.plot.line(color='k', ax=ax_in, linewidth=2)
ax_in.set_xlabel('')
elif plot_type == 'mse':
# Plot mse in colour, overplot max mse
f1 = mse_plot.sel(pfull=850.).plot.contourf(ax=ax_in,
x='xofyear',
y='lat',
levels=np.arange(
200., 401., 10.),
add_labels=False,
extend='both',
add_colorbar=False)
if mse_max:
data_mse = peak_mse(data, lonin=lonin)
data_mse.mse_max_loc.plot.line('k', ax=ax_in)
ax_in.set_xlabel('')
ax_in.set_ylabel('Latitude')
ax_in.set_ylim(-60, 60)
ax_in.set_yticks(np.arange(-60., 61., 30.))
ax_in.grid(True, linestyle=':')
if month_labels:
mn_dic = month_dic(1)
tickspace = range(13, 72, 18)
labels = [mn_dic[(k + 5) / 6] for k in tickspace]
ax_in.set_xlim((1, 72))
ax_in.set_xticks(tickspace)
if do_xlabels:
ax_in.set_xlabel('')
ax_in.set_xticklabels(labels, rotation=25)
return f1
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()
lhe_temp[i, :, :] = (data.flux_lhe[i, :, :].values +
data.flux_lhe[i + n, ::-1, :].values) / 2.
lhe_temp[i + n, :, :] = lhe_temp[i, ::-1, :]
precip_temp = xr.DataArray(precip_temp,
coords=[data.xofyear.values, data.lat, data.lon],
dims=['xofyear', 'lat', 'lon'])
lhe_temp = xr.DataArray(lhe_temp,
coords=[data.xofyear.values, data.lat, data.lon],
dims=['xofyear', 'lat', 'lon'])
data['precipitation'] = precip_temp
data['flux_lhe'] = lhe_temp
# Locate precipitation centroid
precip_centroid(data)
data = data.mean('lon')
levels_flux = np.arange(-300., 305., 20.)
fig = plt.figure()
ax1 = plt.subplot(111)
f1 = (-1. * data.flux_lhe).plot.contourf(x='xofyear',
y='lat',
ax=ax1,
levels=levels_flux,
extend='both',
add_labels=False,
cmap='RdBu_r',
add_colorbar=False)
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 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 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')
