def plot(self, ax):
# Get global u/v wind data
ug, vg = self.chart.get_data(self.gfs_vars, apply_domain=False)
# Get level coordinates for U/V wind components
ug_lv_coord = gfs_utils.get_level_coord(ug, self.level_units)
vg_lv_coord = gfs_utils.get_level_coord(vg, self.level_units)
# Constrain to specified level(s)
ugc = gfs_utils.get_coord_constraint(ug_lv_coord.name(), self.level)
ug = ug.extract(ugc)
vgc = gfs_utils.get_coord_constraint(vg_lv_coord.name(), self.level)
vg = vg.extract(vgc)
# Apply smoothing
ug_data = wrf_smooth2d(ug.data, 6)
ug.data = ug_data.data
vg_data = wrf_smooth2d(vg.data, 6)
vg.data = vg_data.data
# Set up windspharm wind vector object
w = VectorWind(ug, vg)
xi = w.vorticity()
div = w.divergence()
# Constrain to specified domain
domain_constraint = gfs_utils.get_domain_constraint(self.chart.domain)
xi = xi.extract(domain_constraint)
div = div.extract(domain_constraint)
# Calculate strain S following Schielicke et al 2016
u_x, u_y = w.gradient(ug)
v_x, v_y = w.gradient(vg)
D_h = u_x + v_y # horizontal divergence
Def = u_x - v_y # stretching deformation
Def_s = u_y + v_x # shearing deformation
ss = D_h**2 + Def**2 + Def_s**2
ss = ss.extract(domain_constraint)
S = np.sqrt(ss.data) / math.sqrt(2)
# Okubo-Weiss parameter
# vorticity - (div + strain)
okw = (div + S) - xi
# Set mask to inspect O-W parameter in relevant region i.e. < 20N
self.set_coord_mask(lat_max=20)
# Define mask for O-W parameter
mask = self.coord_mask | (okw.data < self.thres)
# Apply mask
okw_masked = iris.util.mask_cube(okw, mask)
ax.contourf(self.lon, self.lat, okw_masked.data, **self.options)
def plot(self, ax):
# Get global u/v wind data
ug, vg = self.chart.get_data(self.gfs_vars, apply_domain=False)
# Get level coordinates for U/V wind components
ug_lv_coord = gfs_utils.get_level_coord(ug, self.level_units)
vg_lv_coord = gfs_utils.get_level_coord(vg, self.level_units)
# Constrain to specified level(s)
ugc = gfs_utils.get_coord_constraint(ug_lv_coord.name(), self.level)
ug = ug.extract(ugc)
vgc = gfs_utils.get_coord_constraint(vg_lv_coord.name(), self.level)
vg = vg.extract(vgc)
# Apply smoothing
ug_data = wrf_smooth2d(ug.data, 6)
ug.data = ug_data.data
vg_data = wrf_smooth2d(vg.data, 6)
vg.data = vg_data.data
# Set up windspharm wind vector object
w = VectorWind(ug, vg)
# Get divergence
div = w.divergence()
# Constrain to specified domain
domain_constraint = gfs_utils.get_domain_constraint(self.chart.domain)
div = div.extract(domain_constraint)
# Mask absolute values below threshold
div_masked = np.ma.masked_where(
np.abs(div.data) < self.thres, div.data)
# Set norm to match centre of colour scale to zero value
self.options['norm'] = mcolors.TwoSlopeNorm(vmin=np.min(div_masked),
vcenter=0,
vmax=np.max(div_masked))
ax.contourf(self.lon, self.lat, div_masked, **self.options)
# The components are in separate files. We catch warnings here because the
# files are not completely CF compliant.
with warnings.catch_warnings():
warnings.simplefilter('ignore', UserWarning)
uwnd = iris.load_cube(example_data_path('uwnd_mean.nc'))
vwnd = iris.load_cube(example_data_path('vwnd_mean.nc'))
uwnd.coord('longitude').circular = True
vwnd.coord('longitude').circular = True
# Create a VectorWind instance to handle the computations.
w = VectorWind(uwnd, vwnd)
# Compute components of rossby wave source: absolute vorticity, divergence,
# irrotational (divergent) wind components, gradients of absolute vorticity.
eta = w.absolutevorticity()
div = w.divergence()
uchi, vchi = w.irrotationalcomponent()
etax, etay = w.gradient(eta)
etax.units = 'm**-1 s**-1'
etay.units = 'm**-1 s**-1'
# Combine the components to form the Rossby wave source term.
S = eta * -1. * div - (uchi * etax + vchi * etay)
S.coord('longitude').attributes['circular'] = True
# Pick out the field for December at 200 hPa.
time_constraint = iris.Constraint(month='Dec')
add_month(S, 'time')
S_dec = S.extract(time_constraint)
# Plot Rossby wave source.
# The components are in separate files. We catch warnings here because the
# files are not completely CF compliant.
with warnings.catch_warnings():
warnings.simplefilter('ignore', UserWarning)
uwnd = iris.load_cube(example_data_path('uwnd_mean.nc'))
vwnd = iris.load_cube(example_data_path('vwnd_mean.nc'))
uwnd.coord('longitude').circular = True
vwnd.coord('longitude').circular = True
# Create a VectorWind instance to handle the computations.
w = VectorWind(uwnd, vwnd)
# Compute components of rossby wave source: absolute vorticity, divergence,
# irrotational (divergent) wind components, gradients of absolute vorticity.
eta = w.absolutevorticity()
div = w.divergence()
uchi, vchi = w.irrotationalcomponent()
etax, etay = w.gradient(eta)
etax.units = 'm**-1 s**-1'
etay.units = 'm**-1 s**-1'
# Combine the components to form the Rossby wave source term.
S = eta * -1. * div - (uchi * etax + vchi * etay)
S.coord('longitude').attributes['circular'] = True
# Pick out the field for December at 200 hPa.
time_constraint = iris.Constraint(month='Dec')
add_month(S, 'time')
S_dec = S.extract(time_constraint)
# Plot Rossby wave source.
lag = 0
max_i = 35 + lag
max_f = max_i + mons
min_i = 11 + lag
min_f = min_i + mons
plt.clf()
plt.ion()
lsmask = iris.load_cube(ncfile_path + 'lsmask.nc')[0,0,::]
lsmask.coord('latitude').guess_bounds()
lsmask.coord('longitude').guess_bounds()
# landmask = ~(ma.make_mask(lsmask.data.copy()) + np.zeros(temp_plv.shape)).astype(bool) # mask sea, show land
# seamask = (ma.make_mask(lsmask.data.copy()) + np.zeros(temp_plv.shape)).astype(bool) # mask land, show sea
uv = VectorWind(u,v)
uv_div = uv.divergence()
psi = uv.streamfunction()
plev_b = 300
plev_t = 850
uv_uptrop_max = ta.iristropave(uv_div,plev_bottom=500,plev_top=200)[max_i:max_f,::].collapsed('time',iris.analysis.MEAN)
uv_midtrop_max = ta.iristropave(uv_div,plev_bottom=850,plev_top=500)[max_i:max_f,::].collapsed('time',iris.analysis.MEAN)
uv_lowtrop_max = ta.iristropave(uv_div,plev_bottom=1000,plev_top=850)[max_i:max_f,::].collapsed('time',iris.analysis.MEAN)
u_uptrop_max = ta.iristropave(u,plev_bottom=500,plev_top=200)[max_i:max_f,::].collapsed('time',iris.analysis.MEAN)
u_midtrop_max = ta.iristropave(u,plev_bottom=850,plev_top=500)[max_i:max_f,::].collapsed('time',iris.analysis.MEAN)
u_lowtrop_max = ta.iristropave(u,plev_bottom=1000,plev_top=850)[max_i:max_f,::].collapsed('time',iris.analysis.MEAN)
v_uptrop_max = ta.iristropave(v,plev_bottom=500,plev_top=200)[max_i:max_f,::].collapsed('time',iris.analysis.MEAN)
v_midtrop_max = ta.iristropave(v,plev_bottom=850,plev_top=500)[max_i:max_f,::].collapsed('time',iris.analysis.MEAN)
v_lowtrop_max = ta.iristropave(v,plev_bottom=1000,plev_top=850)[max_i:max_f,::].collapsed('time',iris.analysis.MEAN)
