def test_sigtor_scalar():
"""Test significant tornado parameter function with a single value."""
sbcape = 4000 * units('J/kg')
sblcl = 800 * units('meter')
srh1 = 400 * units('m^2/s^2')
shr6 = 35 * units('m/s')
truth = 10.666667
sigtor = significant_tornado(sbcape, sblcl, srh1, shr6)
assert_almost_equal(sigtor, truth, 6)
def test_sigtor():
"""Test significant tornado parameter function."""
sbcape = [2000., 2000., 2000., 2000., 3000, 4000] * units('J/kg')
sblcl = [3000., 1500., 500., 1500., 1500, 800] * units('meter')
srh1 = [200., 200., 200., 200., 300, 400] * units('m^2/s^2')
shr6 = [20., 5., 20., 35., 20., 35] * units('m/s')
truth = [0., 0, 1.777778, 1.333333, 2., 10.666667]
sigtor = significant_tornado(sbcape, sblcl, srh1, shr6)
assert_almost_equal(sigtor, truth, 6)
def test_sigtor_scalar():
"""Test significant tornado parameter function with a single value."""
sbcape = 4000 * units('J/kg')
sblcl = 800 * units('meter')
srh1 = 400 * units('m^2/s^2')
shr6 = 35 * units('m/s')
truth = 10.666667
sigtor = significant_tornado(sbcape, sblcl, srh1, shr6)
assert_almost_equal(sigtor, truth, 6)
def test_sigtor():
"""Test significant tornado parameter function."""
sbcape = [2000., 2000., 2000., 2000., 3000, 4000] * units('J/kg')
sblcl = [3000., 1500., 500., 1500., 1500, 800] * units('meter')
srh1 = [200., 200., 200., 200., 300, 400] * units('m^2/s^2')
shr6 = [20., 5., 20., 35., 20., 35] * units('m/s')
truth = [0., 0, 1.777778, 1.333333, 2., 10.666667]
sigtor = significant_tornado(sbcape, sblcl, srh1, shr6)
assert_almost_equal(sigtor, truth, 6)
def scalardata(field, valid_time, targetdir=".", debug=False):
# Get color map, levels, and netCDF variable name appropriate for requested variable (from fieldinfo module).
info = fieldinfo[field]
if debug:
print("scalardata: found", field, "fieldinfo:", info)
cmap = colors.ListedColormap(info['cmap'])
levels = info['levels']
fvar = info['fname'][0]
# Get narr file and filename.
ifile = get(valid_time, targetdir=targetdir, narrtype=info['filename'])
if debug:
print("About to open " + ifile)
nc = xarray.open_dataset(ifile)
# Tried to rename vars and dimensions so metpy.parse_cf() would not warn "Found latitude/longitude values, assuming latitude_longitude for projection grid_mapping variable"
# It didn't help. Only commenting out the metpy.parse_cf() line helped.
# It didn't help with MetpyDeprecationWarning: Multidimensional coordinate lat assigned for axis "y". This behavior has been deprecated and will be removed in v1.0 (only one-dimensional coordinates will be available for the "y" axis) either
#nc = nc.rename_vars({"gridlat_221": "lat", "gridlon_221" : "lon"})
#nc = nc.rename_dims({"gridx_221": "x", "gridy_221" : "y"})
#nc = nc.metpy.parse_cf() # TODO: figure out why filled contour didn't have .metpy.parse_cf()
if fvar not in nc.variables:
print(fvar, "not in", ifile, '. Try', nc.var())
sys.exit(1)
# Define data array. Speed and shear derived differently.
# Define 'long_name' attribute
#
if field[0:5] == "speed":
u = nc[info['fname'][0]]
v = nc[info['fname'][1]]
data = u # copy metadata/coordinates from u
data.values = wind_speed(u, v)
data.attrs['long_name'] = "wind speed"
elif field[0:3] == 'shr' and '_' in field:
du, dv = shear(field,
valid_time=valid_time,
targetdir=targetdir,
debug=debug)
ws = wind_speed(du, dv)
attrs = {
'long_name': 'wind shear',
'units': str(ws.units),
'verttitle': du.attrs["verttitle"]
}
# Use .m magnitude because you can't transfer units of pint quantity to xarray numpy array (xarray.values)
data = xarray.DataArray(data=ws.m,
dims=du.dims,
coords=du.coords,
name=field,
attrs=attrs)
elif field == 'theta2':
pres = nc[info['fname'][0]]
temp = nc[info['fname'][1]]
data = pres # retain xarray metadata/coordinates
theta = potential_temperature(pres, temp)
data.values = theta
data.attrs['units'] = str(theta.units)
data.attrs['long_name'] = 'potential temperature'
elif field == 'thetae2':
pres = nc[info['fname'][0]]
temp = nc[info['fname'][1]]
dwpt = nc[info['fname'][2]]
data = pres # retain xarray metadata/coordinates
thetae = equivalent_potential_temperature(pres, temp, dwpt)
data.values = thetae
data.attrs['units'] = str(thetae.units)
data.attrs['long_name'] = 'equivalent potential temperature'
elif field == 'scp' or field == 'stp' or field == 'tctp':
cape = nc[info['fname'][0]]
cin = nc[info['fname'][1]]
ifile = get(valid_time, targetdir=targetdir, narrtype=narrFlx)
ncFlx = xarray.open_dataset(ifile).metpy.parse_cf()
srh = ncFlx[info['fname'][2]]
shear_layer = info['fname'][3]
bulk_shear = scalardata(shear_layer,
valid_time,
targetdir=targetdir,
debug=debug)
lifted_condensation_level_height = scalardata('zlcl',
valid_time,
targetdir=targetdir,
debug=debug)
if field == 'scp':
# In SPC help, cin is positive in SCP formulation.
cin_term = -40 / cin
cin_term = cin_term.where(cin < -40, other=1)
scp = supercell_composite(cape, srh,
bulk_shear) * cin_term.metpy.unit_array
attrs = {
'units': str(scp.units),
'long_name': 'supercell composite parameter'
}
data = xarray.DataArray(data=scp,
dims=cape.dims,
coords=cape.coords,
name=field,
attrs=attrs)
if field == 'stp':
cin_term = (200 + cin) / 150
cin_term = cin_term.where(cin <= -50, other=1)
cin_term = cin_term.where(cin >= -200, other=0)
# CAPE, srh, bulk_shear, cin may be one vertical level, but LCL may be multiple heights.
# xarray.broadcast() makes them all multiple heights with same shape, so significant_tornado doesn't
# complain about expecting lat/lon 2 dimensions and getting 3 dimensions..
(cape, lifted_condensation_level_height, srh, bulk_shear,
cin_term) = xarray.broadcast(cape,
lifted_condensation_level_height,
srh, bulk_shear, cin_term)
stp = significant_tornado(cape, lifted_condensation_level_height,
srh,
bulk_shear) * cin_term.metpy.unit_array
attrs = {
'units': str(stp.units),
'long_name': 'significant tornado parameter',
'verttitle':
lifted_condensation_level_height.attrs['verttitle']
}
data = xarray.DataArray(data=stp,
dims=cape.dims,
coords=cape.coords,
name=field,
attrs=attrs)
if field == 'tctp':
tctp = srh / (40 * munits['m**2/s**2']) * bulk_shear / (
12 * munits['m/s']) * (2000 - lifted_condensation_level_height
) / (1400 * munits.m)
# But NARR storm relative helicity (srh) is 0-3 km AGL, while original TCTP expects 0-1 km AGL.
# So the shear term is too large using the NARR srh. Normalize the srh term with a larger denominator.
# In STP, srh is normalized by 150 m**2/s**2. Use that.
tctp_0_3kmsrh = srh / (150 * munits['m**2/s**2']) * bulk_shear / (
12 * munits['m/s']) * (2000 - lifted_condensation_level_height
) / (1400 * munits.m)
attrs = {
'units': 'dimensionless',
'long_name': 'TC tornado parameter'
}
data = xarray.DataArray(data=tctp_0_3kmsrh,
dims=cape.dims,
coords=cape.coords,
name=field,
attrs=attrs)
elif field == 'lcl':
pres = nc[info['fname'][0]]
temp = nc[info['fname'][1]]
dwpt = nc[info['fname'][2]]
LCL_pressure, LCL_temperature = lcl(pres.fillna(pres.mean()),
temp.fillna(temp.mean()),
dwpt.fillna(dwpt.mean()))
# convert units to string or xarray.DataArray.metpy.unit_array dies with ttributeError: 'NoneType' object has no attribute 'evaluate'
attrs = {
"long_name": "lifted condensation level",
"units": str(LCL_pressure.units),
"from": "metpy.calc.lcl"
}
data = xarray.DataArray(data=LCL_pressure,
coords=pres.coords,
dims=pres.dims,
name='LCL',
attrs=attrs)
elif field == 'zlcl':
LCL_pressure = scalardata('lcl',
valid_time,
targetdir=targetdir,
debug=debug)
ifile = get(valid_time, targetdir=targetdir, narrtype=narr3D)
nc3D = xarray.open_dataset(ifile).metpy.parse_cf()
hgt3D = nc3D["HGT_221_ISBL"]
data = pressure_to_height(LCL_pressure, hgt3D, targetdir=targetdir)
else:
data = nc[fvar]
data = units(data, info, debug=debug)
data = vertical(data, info, debug=debug)
data = temporal(data, info, debug=debug)
data.attrs['field'] = field
data.attrs['ifile'] = os.path.realpath(ifile)
data.attrs['levels'] = levels
data.attrs['cmap'] = cmap
if data.min() > levels[-1] or data.max() < levels[0]:
print('levels', levels, 'out of range of data', data.min(), data.max())
sys.exit(2)
return data
def significant_tornado(self):
u, v = self.bulk_shear()
return mpcalc.significant_tornado(
self.cape_cin(0)[0],
mpint.log_interpolate_1d(self.lcl(0)[0], self.p, self.z),
self.storm_relative_helicity()[2], (u**2 + v**2)**0.5)
def process_skewt(self):
# Calculation
index_p100 = get_pressure_level_index(self.p_i, 100)
lcl_p, lcl_t = mpcalc.lcl(self.p_i[0], self.t_i[0], self.td_i[0])
lfc_p, lfc_t = mpcalc.lfc(self.p_i, self.t_i, self.td_i)
el_p, el_t = mpcalc.el(self.p_i, self.t_i, self.td_i)
prof = mpcalc.parcel_profile(self.p_i, self.t_i[0], self.td_i[0]).to('degC')
cape, cin = mpcalc.cape_cin(self.p_i, self.t_i, self.td_i, prof)
mucape, mucin = mpcalc.most_unstable_cape_cin(self.p_i, self.t_i, self.td_i)
pwat = mpcalc.precipitable_water(self.td_i, self.p_i)
i8 = get_pressure_level_index(self.p_i, 850)
i7 = get_pressure_level_index(self.p_i, 700)
i5 = get_pressure_level_index(self.p_i, 500)
theta850 = mpcalc.equivalent_potential_temperature(850 * units('hPa'), self.t_i[i8], self.td_i[i5])
theta500 = mpcalc.equivalent_potential_temperature(500 * units('hPa'), self.t_i[i5], self.td_i[i5])
thetadiff = theta850 - theta500
k = self.t_i[i8] - self.t_i[i5] + self.td_i[i8] - (self.t_i[i7] - self.td_i[i7])
a = ((self.t_i[i8] - self.t_i[i5]) - (self.t_i[i8] - self.td_i[i5]) -
(self.t_i[i7] - self.td_i[i7]) - (self.t_i[i5] - self.td_i[i5]))
sw = c_sweat(np.array(self.t_i[i8].magnitude), np.array(self.td_i[i8].magnitude),
np.array(self.t_i[i5].magnitude), np.array(self.u_i[i8].magnitude),
np.array(self.v_i[i8].magnitude), np.array(self.u_i[i5].magnitude),
np.array(self.v_i[i5].magnitude))
si = showalter_index(self.t_i[i8], self.td_i[i8], self.t_i[i5])
li = lifted_index(self.t_i[0], self.td_i[0], self.p_i[0], self.t_i[i5])
srh_pos, srh_neg, srh_tot = mpcalc.storm_relative_helicity(self.u_i, self.v_i, self.alt, 1000 * units('m'))
sbcape, sbcin = mpcalc.surface_based_cape_cin(self.p_i, self.t_i, self.td_i)
shr6km = mpcalc.bulk_shear(self.p_i, self.u_i, self.v_i, heights=self.alt, depth=6000 * units('m'))
wshr6km = mpcalc.wind_speed(*shr6km)
sigtor = mpcalc.significant_tornado(sbcape, delta_height(self.p_i[0], lcl_p), srh_tot, wshr6km)[0]
# Plotting
self.ax.set_ylim(1050, 100)
self.ax.set_xlim(-40, 50)
self.plot(self.p_i, self.t_i, 'r', linewidth=1)
self.plot(self.p_i[:self.dp_idx], self.td_i[:self.dp_idx], 'g', linewidth=1)
self.plot_barbs(self.p_i[:index_p100], self.u_i[:index_p100] * 1.94, self.v_i[:index_p100] * 1.94)
self.plot(lcl_p, lcl_t, 'ko', markerfacecolor='black')
self.plot(self.p_i, prof, 'k', linewidth=2)
if cin.magnitude < 0:
chi = -1 * cin.magnitude
self.shade_cin(self.p_i, self.t_i, prof)
elif cin.magnitude > 0:
chi = cin.magnitude
self.shade_cin(self.p_i, self.t_i, prof)
else:
chi = 0.
self.shade_cape(self.p_i, self.t_i, prof)
self.plot_dry_adiabats(linewidth=0.5)
self.plot_moist_adiabats(linewidth=0.5)
self.plot_mixing_lines(linewidth=0.5)
plt.title('Skew-T Plot \nStation: {} Time: {}'.format(self.st, self.time.strftime('%Y.%m.%d %H:%M')), fontsize=14, loc='left')
# Add hodograph
ax = self._fig.add_axes([0.95, 0.71, 0.17, 0.17])
h = Hodograph(ax, component_range=50)
h.add_grid(increment=20)
h.plot_colormapped(self.u_i[:index_p100], self.v_i[:index_p100], self.alt[:index_p100], linewidth=1.2)
# Annotate parameters
# Annotate names
namelist = ['CAPE', 'CIN', 'MUCAPE', 'PWAT', 'K', 'A', 'SWEAT', 'LCL', 'LFC', 'EL', 'SI', 'LI', 'T850-500',
'θse850-500', 'SRH', 'STP']
xcor = -50
ycor = -90
spacing = -9
for nm in namelist:
ax.text(xcor, ycor, '{}: '.format(nm), fontsize=10)
ycor += spacing
# Annotate values
varlist = [cape, chi, mucape, pwat, k, a, sw, lcl_p, lfc_p, el_p, si, li, self.t_i[i8] - self.t_i[i5], thetadiff,
srh_tot, sigtor]
xcor = 10
ycor = -90
for v in varlist:
if hasattr(v, 'magnitude'):
v = v.magnitude
ax.text(xcor, ycor, str(np.round_(v, 2)), fontsize=10)
ycor += spacing
# Annotate units
unitlist = ['J/kg', 'J/kg', 'J/kg', 'mm', '°C', '°C', '', 'hPa', 'hPa', 'hPa', '°C', '°C', '°C', '°C']
xcor = 45
ycor = -90
for u in unitlist:
ax.text(xcor, ycor, ' {}'.format(u), fontsize=10)
ycor += spacing
