lonstr = atm.latlon_str(lon1, lon2, 'lon')
suptitle = ssn + ' ' + lonstr
months = atm.season_months(ssn)
v = data['V'].sel(month=months)
if (lon2 - lon1) < 360:
v = atm.subset(v, {'lon': (lon1, lon2)})
sector_scale = (lon2 - lon1) / 360.0
psbar = atm.dim_mean(ps, 'lon', lon1, lon2)
clev = 10
else:
sector_scale = None
psbar = atm.dim_mean(ps, 'lon')
clev = 20
vssn = v.mean(dim='month')
vssn_bar = atm.dim_mean(vssn, 'lon')
psi1 = atm.streamfunction(vssn, sector_scale=sector_scale)
psi1 = atm.dim_mean(psi1, 'lon')
psi2 = atm.streamfunction(vssn_bar, sector_scale=sector_scale)
plt.figure(figsize=figsize)
plt.suptitle(suptitle)
plt.subplot(2, 1, 1)
atm.contour_latpres(psi1, clev=clev, omitzero=omitzero, topo=psbar)
plt.title('v -> $\psi$ -> [$\psi$]')
plt.xlabel('')
plt.subplot(2, 1, 2)
atm.contour_latpres(psi2, clev=clev, omitzero=omitzero, topo=psbar)
plt.title('[v] -> [$\psi$]')
# ----------------------------------------------------------------------
# Daily data
lonstr = atm.latlon_str(lon1, lon2, 'lon')
suptitle = ssn + ' ' + lonstr
months = atm.season_months(ssn)
v = data['V'].sel(month=months)
if (lon2 - lon1) < 360:
v = atm.subset(v, {'lon' : (lon1, lon2)})
sector_scale = (lon2 - lon1) / 360.0
psbar = atm.dim_mean(ps, 'lon', lon1, lon2)
clev = 10
else:
sector_scale = None
psbar = atm.dim_mean(ps, 'lon')
clev = 20
vssn = v.mean(dim='month')
vssn_bar = atm.dim_mean(vssn, 'lon')
psi1 = atm.streamfunction(vssn, sector_scale=sector_scale)
psi1 = atm.dim_mean(psi1, 'lon')
psi2 = atm.streamfunction(vssn_bar, sector_scale=sector_scale)
plt.figure(figsize=figsize)
plt.suptitle(suptitle)
plt.subplot(2, 1, 1)
atm.contour_latpres(psi1, clev=clev, omitzero=omitzero, topo=psbar)
plt.title('v -> $\psi$ -> [$\psi$]')
plt.xlabel('')
plt.subplot(2, 1, 2)
atm.contour_latpres(psi2, clev=clev, omitzero=omitzero, topo=psbar)
plt.title('[v] -> [$\psi$]')
# ----------------------------------------------------------------------
# Daily data
# Monsoon onset, retreat indices
index = utils.get_onset_indices(onset_nm, indfiles, years)
mfc = atm.rolling_mean(index['ts_daily'], nroll, center=True)
onset = index['onset']
ssn_length=index['length'].mean(dim='year')
data = {}
data['MFC'] = utils.daily_rel2onset(mfc, onset, npre, npost)
data[pcp_nm] = utils.daily_rel2onset(pcp, onset, npre, npost)
data['MFC_ACC'] = utils.daily_rel2onset(index['tseries'], onset, npre, npost)
for nm in varnms:
print('Loading ' + relfiles[nm])
with xray.open_dataset(relfiles[nm]) as ds:
if nm == 'PSI':
data[nm] = atm.streamfunction(ds['V'])
psimid = atm.subset(data[nm], {'plev' : (pmid, pmid)},
squeeze=True)
psimid.name = 'PSI%d' % pmid
data['PSI%d' % pmid] = psimid
elif nm == 'VFLXLQV':
var = atm.dim_mean(ds['VFLXQV'], 'lon', lon1, lon2)
data[nm] = var * atm.constants.Lv.values
elif nm == theta_nm:
theta = ds[nm]
_, _, dtheta = atm.divergence_spherical_2d(theta, theta)
data[nm] = atm.dim_mean(ds[nm], 'lon', lon1, lon2)
data[dtheta_nm] = atm.dim_mean(dtheta, 'lon', lon1, lon2)
elif nm == dtheta_nm:
continue
else: