x = np.arange(0., 116000., 100.)
y = np.arange(0., 31900., 100.)
X, Y = np.meshgrid(x, y)
lsm = Dataset('/work/n02/n02/xb899100/island_masks/lsm50.nc', 'r')
lsm_data = lsm.variables['lsm'][:]
lsm.close()
for hour in ["{0:02d}".format(h) for h in xrange(0, 24, 3)]:
# open the netCDFs
bouy_nc = Dataset('../bouy_' + hour + '.nc', 'r')
fluxes_nc = Dataset('../fluxes_' + hour + '.nc', 'r')
if hour == '00':
theta_e = getThetaE(bouy_nc.variables[theta_key][:, 0, :, :],
bouy_nc.variables[temperature_key][:, 0, :, :],
fluxes_nc.variables[pressure_key][:, 0, :, :],
p_units='Pa')
times = bouy_nc.variables['min10_0'][:]
z = bouy_nc.variables['thlev_zsea_theta'][:]
else:
theta_e = np.concatenate(
(theta_e,
getThetaE(bouy_nc.variables[theta_key][:, 0, :, :],
bouy_nc.variables[temperature_key][:, 0, :, :],
fluxes_nc.variables[pressure_key][:, 0, :, :],
p_units='Pa')),
axis=0)
times = np.concatenate((times, bouy_nc.variables['min10_0'][:]),
axis=0)
bouy_nc.close()
fluxes_nc.close()
time_05 = bouy_05_nc.variables[time05_key][1:]
iz = np.where(
np.abs(bouy_10_nc.variables['thlev_zsea_theta'][:] -
target_z) == np.min(
np.abs(bouy_10_nc.variables['thlev_zsea_theta'][:] -
target_z)))[0][0]
theta_e_10 = np.zeros_like(bouy_10_nc.variables[theta_key][1:,
iz, :, :])
theta_e_05 = np.zeros_like(bouy_05_nc.variables[theta_key][1:,
iz, :, :])
for it in xrange(theta_e_10.shape[0]):
theta_e_10[it, :, :] = getThetaE(
bouy_10_nc.variables[theta_key][1 + it, iz, :, :],
bouy_10_nc.variables[temp_key][1 + it, iz, :, :],
fluxes_10_nc.variables[pthe_key][1 + it, iz, :, :],
t_units='K',
p_units='Pa')
for it in xrange(theta_e_05.shape[0]):
theta_e_05[it, :, :] = getThetaE(
bouy_05_nc.variables[theta_key][1 + it, iz, :, :],
bouy_05_nc.variables[temp_key][1 + it, iz, :, :],
fluxes_05_nc.variables[pthe_key][1 + it, iz, :, :],
t_units='K',
p_units='Pa')
else:
time_10 = np.concatenate(
(time_10, bouy_10_nc.variables[time10_key][:]), axis=0)
time_05 = np.concatenate(
(time_05, bouy_05_nc.variables[time05_key][:]), axis=0)
keys = ['H250E250', 'DX0200', 'DX0400', 'DX0800', 'DX1600']
hours = ["{0:02d}".format(hour) for hour in range(0, 24, 3)]
hours_U05 = ["{0:02d}".format(hour) for hour in range(0, 16, 4)]
thetaE = {}
for key in keys:
print key
# get cheap and dirty temperature using the initial surface pressure and theta
if key == 'U05':
for hour in hours_U05:
bouy_nc = Dataset(paths[key] + 'bouy_' + hour + '.nc', 'r')
if hour == hours_U05[0]:
theta = bouy_nc.variables[theta_key][:, 0, :, :] * 1.
temp = PTtoTemp(theta, p_sfc, t_units='K', p_units='Pa')
thetaE[key] = getThetaE(theta,
temp,
p_sfc,
t_units='K',
p_units='Pa')
else:
theta = bouy_nc.variables[theta_key][:, 0, :, :] * 1.
temp = PTtoTemp(theta, p_sfc, t_units='K', p_units='Pa')
thetaE[key] = np.concatenate(
(thetaE[key],
getThetaE(theta, temp, p_sfc, t_units='K', p_units='Pa')),
axis=0)
else:
for hour in hours:
bouy_nc = Dataset(paths[key] + 'bouy_' + hour + '.nc', 'r')
if hour == hours[0]:
theta = bouy_nc.variables[theta_key][:, 0, :, :] * 1.
temp = PTtoTemp(theta, p_sfc, t_units='K', p_units='Pa')
T_data[exp]['time'] = bouy_nc.variables[T_time_key][:]*1.
T_data[exp]['x'], T_data[exp]['y'] = np.meshgrid(np.arange(T_data[exp]['theta'].shape[3])*dx, np.arange(T_data[exp]['theta'].shape[2])*dx)
z_theta = bouy_nc.variables['thlev_zsea_theta'][:]*1.
R[exp] = np.sqrt((T_data[exp]['x'] - x_c)**2 + (T_data[exp]['y'] - y_c)**2)
with Dataset(path_start + exp + path_end + 'fluxes_09.nc', 'r') as fluxes_nc:
T_data[exp]['pthe'] = fluxes_nc.variables[pthe_key][:]*1.
<<<<<<< HEAD
# define plotting parameters
my_levels = np.array([-2.0, -1.0, -0.5, -0.25, -0.1, 0.1, 0.25, 0.5, 1.0, 2.0])
n_levels = float(len(my_levels))
my_cmap = mpl.cm.get_cmap('bwr')
my_colors = my_cmap((np.arange(n_levels)+1.0)/(n_levels))
=======
# compute the equivalent potential temperature
T_data[exp]['thetae'] = getThetaE(T_data[exp]['theta'], T_data[exp]['temp'], T_data[exp]['pthe'], t_units = 'K', p_units = 'Pa')
# define plotting parameters
my_levels = np.array([level for level in np.arange(-5., 5.1, 0.5) if level != 0.0])
>>>>>>> 62279a4ff074ba2a906de6d583e07e7c7ce0c696
# Panel labels
labels = ['a)', 'b)', 'c)', 'd)']
iz = np.where(np.abs(z_theta - 2.0) == np.min(np.abs(z_theta - 2.0)))[0][0]
### Make the plot ###
<<<<<<< HEAD
fig = plt.figure(figsize = (10, 5))
=======
fig = plt.figure(figsize = (8.5, 4))
>>>>>>> 62279a4ff074ba2a906de6d583e07e7c7ce0c696
for exp in experiments:
lindex_t = np.where((np.abs(lwp_times - target_time) == np.min(
np.abs(lwp_times - target_time))))[0][0]
# Choose some vertical levels to do the plots:
z = wind_nc.variables['thlev_zsea_theta'][:]
iz10 = np.where(np.abs(z - 10.0) == np.min(np.abs(z - 10.0)))[0][0]
# Create coordinate system
X, Y = np.meshgrid(
np.arange(0., 116000., 100.) / 1000.,
np.arange(0., 31900., 100.) / 1000.)
# Compute equivalent potential temperature
theta_e = getThetaE(bouy_nc.variables[theta_key][bindex_t, 0, :, :],
bouy_nc.variables[temp_key][bindex_t, 0, :, :],
fluxes_nc.variables[pthe_key][bindex_t, 0, :, :],
t_units='K',
p_units='Pa')
# Read land-sea mask
lsm = Dataset('/work/n02/n02/xb899100/island_masks/lsm50.nc', 'r')
my_lsm = lsm.variables['lsm'][0, 0, :, :]
lsm.close()
# Create the plot
fig = plt.figure(tight_layout=True, figsize=(14, 9))
axa = fig.add_subplot(2, 2, 1, adjustable='box', aspect=1)
axa.contour(X, Y, my_lsm, colors=['k'], linewidths=[2])
axa.set_xticklabels([])
axa.set_ylabel('y (km)')