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)
mpl.rcParams['mathtext.default'] = 'regular'
# Read zonal and meridional wind components from file using the iris module.
# 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.
from windspharm.examples import example_data_path
# Read zonal and meridional wind components from file using the iris module.
# 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 computation of streamfunction and
# velocity potential.
w = VectorWind(uwnd, vwnd)
# Compute the streamfunction and velocity potential.
sf, vp = w.sfvp()
# Pick out the field for December.
time_constraint = iris.Constraint(month='Dec')
add_month(sf, 'time', name='month')
add_month(vp, 'time', name='month')
sf_dec = sf.extract(time_constraint)
vp_dec = vp.extract(time_constraint)
# Plot streamfunction.
clevs = [-120, -100, -80, -60, -40, -20, 0, 20, 40, 60, 80, 100, 120]
ax = plt.subplot(111, projection=ccrs.PlateCarree(central_longitude=180))
fill_sf = iplt.contourf(sf_dec * 1e-06, clevs, cmap=plt.cm.RdBu_r,
from windspharm.iris import VectorWind
from windspharm.examples import example_data_path
# Read zonal and meridional wind components from file using the iris module.
# 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.
from windspharm.iris import VectorWind
from windspharm.examples import example_data_path
# Read zonal and meridional wind components from file using the iris module.
# 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 computation of streamfunction and
# velocity potential.
w = VectorWind(uwnd, vwnd)
# Compute the streamfunction and velocity potential.
sf, vp = w.sfvp()
# Pick out the field for December.
time_constraint = iris.Constraint(month='Dec')
add_month(sf, 'time', name='month')
add_month(vp, 'time', name='month')
sf_dec = sf.extract(time_constraint)
vp_dec = vp.extract(time_constraint)
# Plot streamfunction.
clevs = [-120, -100, -80, -60, -40, -20, 0, 20, 40, 60, 80, 100, 120]
ax = plt.subplot(111, projection=ccrs.PlateCarree(central_longitude=180))
fill_sf = iplt.contourf(sf_dec * 1e-06,
mons = 6
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)
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 nondivergent component of wind
upsi, vpsi = w.nondivergentcomponent()
Vpsi = VectorWind(upsi, vpsi)
# Partition streamfunction vorticity into curvature and shear
# components
# Bell and Keyser 1993 Appendix equation A.3b
# Relative curvature vorticity:
# V dalpha/ds = 1/V^2[u^2 dv/dx - v^2 du/dy - uv(du/dx - dv/dy)]
dupsi_dx, dupsi_dy = Vpsi.gradient(upsi)
dvpsi_dx, dvpsi_dy = Vpsi.gradient(vpsi)
ws = Vpsi.magnitude()
vrt = (upsi**2 * dvpsi_dx - vpsi**2 * dupsi_dy - upsi * vpsi *
(dupsi_dx - dvpsi_dy)) / ws**2
# Calculate advection of non-divergent curvature vorticity
dvrt_dx, dvrt_dy = Vpsi.gradient(vrt)
advec = -1 * (upsi * dvrt_dx + vpsi * dvrt_dy)
# Second order advection term needed for masking
dadv_dx, dadv_dy = Vpsi.gradient(advec)
advec2 = upsi * dadv_dx + vpsi * dadv_dy
# Apply domain constraint
domain_constraint = gfs_utils.get_domain_constraint(self.chart.domain)
upsi = upsi.extract(domain_constraint)
vrt = vrt.extract(domain_constraint)
advec = advec.extract(domain_constraint)
advec2 = advec2.extract(domain_constraint)
# Masking rules from Berry & Thorncroft 2007, Table 1
# A1. Mask streamfunction curvature vorticity to exclude AEW
# ridges axes or weak systems (BT2007: K1T = 0.5 * 10^-5 s^-1)
K1T = 1.5 * 10**-5 # s^-1
m1t = vrt.data <= K1T # mask for troughs
m1r = vrt.data >= -K1T # mask for ridges
# A2. Remove "pseudoridge" axes in nondivergent flow that is
# highly cyclonically curved
K2T = 0 # m s^-3
m2 = advec2.data <= K2T
# A3. Removes trough axes in westerly flow
K3T = 0 # m s^-1
m3 = upsi.data >= K3T
trough_mask = m1t | m2 | m3
ridge_mask = m1r | ~m2 | m3
troughs = self.apply_size_mask(trough_mask, advec.data)
ridges = self.apply_size_mask(ridge_mask, advec.data)
# Plot troughs and ridges
ax.contour(self.lon, self.lat, troughs, levels=0, **self.options)
ax.contour(self.lon,
self.lat,
ridges,
levels=0,
**self.options,
linestyles="dotted")