def test_no_lfc():
"""Test LFC calculation when there is no LFC in the data."""
levels = np.array([959., 867.9, 779.2, 647.5, 472.5, 321.9, 251.]) * units.mbar
temperatures = np.array([22.2, 17.4, 14.6, 1.4, -17.6, -39.4, -52.5]) * units.celsius
dewpoints = np.array([9., 4.3, -21.2, -26.7, -31., -53.3, -66.7]) * units.celsius
lfc_pressure, lfc_temperature = lfc(levels, temperatures, dewpoints)
assert assert_nan(lfc_pressure, levels.units)
assert assert_nan(lfc_temperature, temperatures.units)
def test_lfc_basic():
"""Test LFC calculation."""
levels = np.array([959., 779.2, 751.3, 724.3, 700., 269.]) * units.mbar
temperatures = np.array([22.2, 14.6, 12., 9.4, 7., -49.]) * units.celsius
dewpoints = np.array([19., -11.2, -10.8, -10.4, -10., -53.2]) * units.celsius
lfc_pressure, lfc_temp = lfc(levels, temperatures, dewpoints)
assert_almost_equal(lfc_pressure, 727.468 * units.mbar, 2)
assert_almost_equal(lfc_temp, 9.705 * units.celsius, 2)
def test_lfc_basic():
"""Test LFC calculation."""
levels = np.array([959., 779.2, 751.3, 724.3, 700., 269.]) * units.mbar
temperatures = np.array([22.2, 14.6, 12., 9.4, 7., -49.]) * units.celsius
dewpoints = np.array([19., -11.2, -10.8, -10.4, -10., -53.2]) * units.celsius
l = lfc(levels, temperatures, dewpoints)
assert_almost_equal(l[0], 727.468 * units.mbar, 2)
assert_almost_equal(l[1], 9.705 * units.celsius, 2)
def test_no_lfc():
"""Test LFC calculation when there is no LFC in the data."""
levels = np.array([959., 867.9, 779.2, 647.5, 472.5, 321.9, 251.]) * units.mbar
temperatures = np.array([22.2, 17.4, 14.6, 1.4, -17.6, -39.4, -52.5]) * units.celsius
dewpoints = np.array([9., 4.3, -21.2, -26.7, -31., -53.3, -66.7]) * units.celsius
lfc_pressure, lfc_temperature = lfc(levels, temperatures, dewpoints)
assert assert_nan(lfc_pressure, levels.units)
assert assert_nan(lfc_temperature, temperatures.units)
def plot_sounding(date, station):
p, T, Td, u, v, windspeed = get_sounding_data(date, station)
lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0])
lfc_pressure, lfc_temperature = mpcalc.lfc(p, T, Td)
parcel_path = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC')
# Create a new figure. The dimensions here give a good aspect ratio
fig = plt.figure(figsize=(8, 8))
skew = SkewT(fig)
# Plot the data
temperature_line, = skew.plot(p, T, color='tab:red')
dewpoint_line, = skew.plot(p, Td, color='blue')
cursor = mplcursors.cursor([temperature_line, dewpoint_line])
# Plot thermodynamic parameters and parcel path
skew.plot(p, parcel_path, color='black')
if lcl_pressure:
skew.ax.axhline(lcl_pressure, color='black')
if lfc_pressure:
skew.ax.axhline(lfc_pressure, color='0.7')
# Add the relevant special lines
skew.ax.axvline(0, color='c', linestyle='--', linewidth=2)
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
# Shade areas representing CAPE and CIN
skew.shade_cin(p, T, parcel_path)
skew.shade_cape(p, T, parcel_path)
# Add wind barbs
skew.plot_barbs(p, u, v)
# Add an axes to the plot
ax_hod = inset_axes(skew.ax, '30%', '30%', loc=1, borderpad=3)
# Plot the hodograph
h = Hodograph(ax_hod, component_range=100.)
# Grid the hodograph
h.add_grid(increment=20)
# Plot the data on the hodograph
mask = (p >= 100 * units.mbar)
h.plot_colormapped(u[mask], v[mask],
windspeed[mask]) # Plot a line colored by wind speed
# Set some sensible axis limits
skew.ax.set_ylim(1000, 100)
skew.ax.set_xlim(-40, 60)
return fig, skew
def plot_sounding(date, station):
p, T, Td, u, v, windspeed = get_sounding_data(date, station)
lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0])
lfc_pressure, lfc_temperature = mpcalc.lfc(p, T, Td)
parcel_path = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC')
# Create a new figure. The dimensions here give a good aspect ratio
fig = plt.figure(figsize=(8, 8))
skew = SkewT(fig)
# Plot the data
temperature_line, = skew.plot(p, T, color='tab:red')
dewpoint_line, = skew.plot(p, Td, color='blue')
cursor = mplcursors.cursor([temperature_line, dewpoint_line])
# Plot thermodynamic parameters and parcel path
skew.plot(p, parcel_path, color='black')
if lcl_pressure:
skew.ax.axhline(lcl_pressure, color='black')
if lfc_pressure:
skew.ax.axhline(lfc_pressure, color='0.7')
# Add the relevant special lines
skew.ax.axvline(0, color='c', linestyle='--', linewidth=2)
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
# Shade areas representing CAPE and CIN
skew.shade_cin(p, T, parcel_path)
skew.shade_cape(p, T, parcel_path)
# Add wind barbs
skew.plot_barbs(p, u, v)
# Add an axes to the plot
ax_hod = inset_axes(skew.ax, '30%', '30%', loc=1, borderpad=3)
# Plot the hodograph
h = Hodograph(ax_hod, component_range=100.)
# Grid the hodograph
h.add_grid(increment=20)
# Plot the data on the hodograph
mask = (p >= 100 * units.mbar)
h.plot_colormapped(u[mask], v[mask], windspeed[mask]) # Plot a line colored by wind speed
# Set some sensible axis limits
skew.ax.set_ylim(1000, 100)
skew.ax.set_xlim(-40, 60)
return fig, skew
def test_lfc_ml():
"""Test Mixed-Layer LFC calculation."""
levels = np.array([959., 779.2, 751.3, 724.3, 700., 269.]) * units.mbar
temperatures = np.array([22.2, 14.6, 12., 9.4, 7., -49.]) * units.celsius
dewpoints = np.array([19., -11.2, -10.8, -10.4, -10., -53.2]) * units.celsius
__, t_mixed, td_mixed = mixed_parcel(levels, temperatures, dewpoints)
mixed_parcel_prof = parcel_profile(levels, t_mixed, td_mixed)
lfc_pressure, lfc_temp = lfc(levels, temperatures, dewpoints, mixed_parcel_prof)
assert_almost_equal(lfc_pressure, 631.794 * units.mbar, 2)
assert_almost_equal(lfc_temp, -1.862 * units.degC, 2)
def test_lfc_sfc_precision():
"""Test LFC when there are precision issues with the parcel path."""
levels = np.array([839., 819.4, 816., 807., 790.7, 763., 736.2,
722., 710.1, 700.]) * units.mbar
temperatures = np.array([20.6, 22.3, 22.6, 22.2, 20.9, 18.7, 16.4,
15.2, 13.9, 12.8]) * units.celsius
dewpoints = np.array([10.6, 8., 7.6, 6.2, 5.7, 4.7, 3.7, 3.2, 3., 2.8]) * units.celsius
lfc_pressure, lfc_temp = lfc(levels, temperatures, dewpoints)
assert assert_nan(lfc_pressure, levels.units)
assert assert_nan(lfc_temp, temperatures.units)
def test_lfc_sfc_precision():
"""Test LFC when there are precision issues with the parcel path."""
levels = np.array([839., 819.4, 816., 807., 790.7, 763., 736.2,
722., 710.1, 700.]) * units.mbar
temperatures = np.array([20.6, 22.3, 22.6, 22.2, 20.9, 18.7, 16.4,
15.2, 13.9, 12.8]) * units.celsius
dewpoints = np.array([10.6, 8., 7.6, 6.2, 5.7, 4.7, 3.7, 3.2, 3., 2.8]) * units.celsius
lfc_pressure, lfc_temp = lfc(levels, temperatures, dewpoints)
assert assert_nan(lfc_pressure, levels.units)
assert assert_nan(lfc_temp, temperatures.units)
def test_lfc_inversion():
"""Test LFC when there is an inversion to be sure we don't pick that."""
levels = np.array([963., 789., 782.3, 754.8, 728.1, 727., 700.,
571., 450., 300., 248.]) * units.mbar
temperatures = np.array([25.4, 18.4, 17.8, 15.4, 12.9, 12.8,
10., -3.9, -16.3, -41.1, -51.5]) * units.celsius
dewpoints = np.array([20.4, 0.4, -0.5, -4.3, -8., -8.2, -9.,
-23.9, -33.3, -54.1, -63.5]) * units.celsius
lfc_pressure, lfc_temp = lfc(levels, temperatures, dewpoints)
assert_almost_equal(lfc_pressure, 706.0103 * units.mbar, 2)
assert_almost_equal(lfc_temp, 10.6232 * units.celsius, 2)
def test_lfc_equals_lcl():
"""Test LFC when there is no cap and the lfc is equal to the lcl."""
levels = np.array([912., 905.3, 874.4, 850., 815.1, 786.6, 759.1,
748., 732.2, 700., 654.8]) * units.mbar
temperatures = np.array([29.4, 28.7, 25.2, 22.4, 19.4, 16.8,
14.3, 13.2, 12.6, 11.4, 7.1]) * units.celsius
dewpoints = np.array([18.4, 18.1, 16.6, 15.4, 13.2, 11.4, 9.6,
8.8, 0., -18.6, -22.9]) * units.celsius
lfc_pressure, lfc_temp = lfc(levels, temperatures, dewpoints)
assert_almost_equal(lfc_pressure, 777.0333 * units.mbar, 2)
assert_almost_equal(lfc_temp, 15.8714 * units.celsius, 2)
def test_lfc_inversion():
"""Test LFC when there is an inversion to be sure we don't pick that."""
levels = np.array([963., 789., 782.3, 754.8, 728.1, 727., 700.,
571., 450., 300., 248.]) * units.mbar
temperatures = np.array([25.4, 18.4, 17.8, 15.4, 12.9, 12.8,
10., -3.9, -16.3, -41.1, -51.5]) * units.celsius
dewpoints = np.array([20.4, 0.4, -0.5, -4.3, -8., -8.2, -9.,
-23.9, -33.3, -54.1, -63.5]) * units.celsius
lfc_pressure, lfc_temp = lfc(levels, temperatures, dewpoints)
assert_almost_equal(lfc_pressure, 706.0103 * units.mbar, 2)
assert_almost_equal(lfc_temp, 10.6232 * units.celsius, 2)
def test_lfc_equals_lcl():
"""Test LFC when there is no cap and the lfc is equal to the lcl."""
levels = np.array([912., 905.3, 874.4, 850., 815.1, 786.6, 759.1,
748., 732.2, 700., 654.8]) * units.mbar
temperatures = np.array([29.4, 28.7, 25.2, 22.4, 19.4, 16.8,
14.3, 13.2, 12.6, 11.4, 7.1]) * units.celsius
dewpoints = np.array([18.4, 18.1, 16.6, 15.4, 13.2, 11.4, 9.6,
8.8, 0., -18.6, -22.9]) * units.celsius
lfc_pressure, lfc_temp = lfc(levels, temperatures, dewpoints)
assert_almost_equal(lfc_pressure, 777.0333 * units.mbar, 2)
assert_almost_equal(lfc_temp, 15.8714 * units.celsius, 2)
def test_lfc_not_below_lcl():
"""Test sounding where LFC appears to be (but isn't) below LCL."""
levels = np.array([1002.5, 1001.7, 1001., 1000.3, 999.7, 999., 998.2, 977.9,
966.2, 952.3, 940.6, 930.5, 919.8, 909.1, 898.9, 888.4,
878.3, 868.1, 858., 848., 837.2, 827., 816.7, 805.4]) * units.hPa
temperatures = np.array([17.9, 17.9, 17.8, 17.7, 17.7, 17.6, 17.5, 16.,
15.2, 14.5, 13.8, 13., 12.5, 11.9, 11.4, 11.,
10.3, 9.7, 9.2, 8.7, 8., 7.4, 6.8, 6.1]) * units.degC
dewpoints = np.array([13.6, 13.6, 13.5, 13.5, 13.5, 13.5, 13.4, 12.5,
12.1, 11.8, 11.4, 11.3, 11., 9.3, 10., 8.7, 8.9,
8.6, 8.1, 7.6, 7., 6.5, 6., 5.4]) * units.degC
lfc_pressure, lfc_temp = lfc(levels, temperatures, dewpoints)
# Before patch, LFC pressure would show 1000.5912165339967 hPa
assert_almost_equal(lfc_pressure, 811.8456357 * units.mbar, 6)
assert_almost_equal(lfc_temp, 6.4992871 * units.celsius, 6)
def test_sensitive_sounding():
"""Test quantities for a sensitive sounding (#902)."""
# This sounding has a very small positive area in the low level. It's only captured
# properly if the parcel profile includes the LCL, otherwise it breaks LFC and CAPE
p = units.Quantity([
1004., 1000., 943., 928., 925., 850., 839., 749., 700., 699., 603.,
500., 404., 400., 363., 306., 300., 250., 213., 200., 176., 150.
], 'hectopascal')
t = units.Quantity([
24.2, 24., 20.2, 21.6, 21.4, 20.4, 20.2, 14.4, 13.2, 13., 6.8, -3.3,
-13.1, -13.7, -17.9, -25.5, -26.9, -37.9, -46.7, -48.7, -52.1, -58.9
], 'degC')
td = units.Quantity([
21.9, 22.1, 19.2, 20.5, 20.4, 18.4, 17.4, 8.4, -2.8, -3.0, -15.2,
-20.3, -29.1, -27.7, -24.9, -39.5, -41.9, -51.9, -60.7, -62.7, -65.1,
-71.9
], 'degC')
lfc_pressure, lfc_temp = lfc(p, t, td)
assert_almost_equal(lfc_pressure, 947.476 * units.mbar, 2)
assert_almost_equal(lfc_temp, 20.498 * units.degC, 2)
pos, neg = surface_based_cape_cin(p, t, td)
assert_almost_equal(pos, 0.112 * units('J/kg'), 3)
assert_almost_equal(neg, -6.075 * units('J/kg'), 3)
def calculate_stability_indicies(ds, temp_name="temperature",
td_name="dewpoint_temperature",
p_name="pressure",
moving_ave_window=0):
"""
Function for calculating stability indices from sounding data.
Parameters
----------
ds : ACT dataset
The dataset to compute the stability indicies of. Must have
temperature, dewpoint, and pressure in vertical coordinates.
temp_name : str
The name of the temperature field.
td_name : str
The name of the dewpoint field.
p_name : str
The name of the pressure field.
moving_ave_window : int
Number of points to do a moving average on sounding data to reduce
noise. This is useful if noise in the sounding is preventing parcel
ascent.
Returns
-------
ds : ACT dataset
An ACT dataset with additional stability indicies added.
"""
if not METPY_AVAILABLE:
raise ImportError("MetPy need to be installed on your system to " +
"calculate stability indices")
t = ds[temp_name]
td = ds[td_name]
p = ds[p_name]
if not hasattr(t, "units"):
raise AttributeError("Temperature field must have units" +
" for ACT to discern!")
if not hasattr(td, "units"):
raise AttributeError("Dewpoint field must have units" +
" for ACT to discern!")
if not hasattr(p, "units"):
raise AttributeError("Pressure field must have units" +
" for ACT to discern!")
if t.units == "C":
t_units = units.degC
else:
t_units = getattr(units, t.units)
if td.units == "C":
td_units = units.degC
else:
td_units = getattr(units, td.units)
p_units = getattr(units, p.units)
# Sort all values by decreasing pressure
t_sorted = np.array(t.values)
td_sorted = np.array(td.values)
p_sorted = np.array(p.values)
ind_sort = np.argsort(p_sorted)
t_sorted = t_sorted[ind_sort[-1:0:-1]]
td_sorted = td_sorted[ind_sort[-1:0:-1]]
p_sorted = p_sorted[ind_sort[-1:0:-1]]
if moving_ave_window > 0:
t_sorted = np.convolve(
t_sorted, np.ones((moving_ave_window,)) / moving_ave_window)
td_sorted = np.convolve(
td_sorted, np.ones((moving_ave_window,)) / moving_ave_window)
p_sorted = np.convolve(
p_sorted, np.ones((moving_ave_window,)) / moving_ave_window)
t_sorted = t_sorted * t_units
td_sorted = td_sorted * td_units
p_sorted = p_sorted * p_units
t_profile = mpcalc.parcel_profile(
p_sorted, t_sorted[0], td_sorted[0])
# Calculate parcel trajectory
ds["parcel_temperature"] = t_profile.magnitude
ds["parcel_temperature"].attrs['units'] = t_profile.units
# Calculate CAPE, CIN, LCL
sbcape, sbcin = mpcalc.surface_based_cape_cin(
p_sorted, t_sorted, td_sorted)
lcl = mpcalc.lcl(
p_sorted[0], t_sorted[0], td_sorted[0])
try:
lfc = mpcalc.lfc(
p_sorted[0], t_sorted[0], td_sorted[0])
except IndexError:
lfc = np.nan * p_sorted.units
mucape, mucin = mpcalc.most_unstable_cape_cin(
p_sorted, t_sorted, td_sorted)
where_500 = np.argmin(np.abs(p_sorted - 500 * units.hPa))
li = t_sorted[where_500] - t_profile[where_500]
ds["surface_based_cape"] = sbcape.magnitude
ds["surface_based_cape"].attrs['units'] = "J/kg"
ds["surface_based_cape"].attrs['long_name'] = "Surface-based CAPE"
ds["surface_based_cin"] = sbcin.magnitude
ds["surface_based_cin"].attrs['units'] = "J/kg"
ds["surface_based_cin"].attrs['long_name'] = "Surface-based CIN"
ds["most_unstable_cape"] = mucape.magnitude
ds["most_unstable_cape"].attrs['units'] = "J/kg"
ds["most_unstable_cape"].attrs['long_name'] = "Most unstable CAPE"
ds["most_unstable_cin"] = mucin.magnitude
ds["most_unstable_cin"].attrs['units'] = "J/kg"
ds["most_unstable_cin"].attrs['long_name'] = "Most unstable CIN"
ds["lifted_index"] = li.magnitude
ds["lifted_index"].attrs['units'] = t_profile.units
ds["lifted_index"].attrs['long_name'] = "Lifted index"
ds["level_of_free_convection"] = lfc.magnitude
ds["level_of_free_convection"].attrs['units'] = lfc.units
ds["level_of_free_convection"].attrs['long_name'] = "Level of free convection"
ds["lifted_condensation_level_temperature"] = lcl[1].magnitude
ds["lifted_condensation_level_temperature"].attrs['units'] = lcl[1].units
ds["lifted_condensation_level_temperature"].attrs['long_name'] = "Lifted condensation level temperature"
ds["lifted_condensation_level_pressure"] = lcl[0].magnitude
ds["lifted_condensation_level_pressure"].attrs['units'] = lcl[0].units
ds["lifted_condensation_level_pressure"].attrs['long_name'] = "Lifted condensation level pressure"
return ds
'pressure': p,
'temperature': T,
'dewpoint': Td,
'speed': ws,
'direction': wd
})
p = df['pressure'].values * units.hPa
T = df['temperature'].values * units.degC
Td = df['dewpoint'].values * units.degC
wind_speed = df['speed'].values * units.meter / (units.second)
wind_dir = df['direction'].values * units.degrees
u, v = mpcalc.wind_components(wind_speed, wind_dir)
lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0])
lfc_pressure, lfc_temperature = mpcalc.lfc(p, T, Td)
parcel_prof = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC')
cape, cin = mpcalc.cape_cin(p, T, Td, parcel_prof)
fig = plt.figure(figsize=(12., 9.))
fig.subplots_adjust(top=0.9,
bottom=0.1,
left=0.05,
right=0.96,
wspace=0.08,
hspace=0.25)
gs = gridspec.GridSpec(21, 5)
skew = SkewT(fig, subplot=gs[:, :4], rotation=45)
# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dictated by the typical meteorological plot
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
def entropy_plots(pressure,
temperature,
mixing_ratio,
altitude,
h0_std=2000,
ensemble_size=20,
ent_rate=np.arange(0, 2, 0.05),
entrain=False):
"""
plotting the summarized entropy diagram with annotations and thermodynamic parameters
"""
p = pressure * units('mbar')
T = temperature * units('degC')
q = mixing_ratio * units('kilogram/kilogram')
qs = mpcalc.mixing_ratio(mpcalc.saturation_vapor_pressure(T), p)
Td = mpcalc.dewpoint(mpcalc.vapor_pressure(p, q)) # dewpoint
Tp = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC') # parcel profile
# Altitude based on the hydrostatic eq.
if len(altitude) == len(pressure): # (1) altitudes for whole levels
altitude = altitude * units('meter')
elif len(altitude
) == 1: # (2) known altitude where the soundings was launched
z_surf = altitude.copy() * units('meter')
# given altitude
altitude = np.zeros((np.size(T))) * units('meter')
for i in range(np.size(T)):
altitude[i] = mpcalc.thickness_hydrostatic(
p[:i + 1], T[:i + 1]) + z_surf # Hypsometric Eq. for height
else:
print(
'***NOTE***: the altitude at the surface is assumed 0 meter, and altitudes are derived based on the hypsometric equation'
)
altitude = np.zeros(
(np.size(T))) * units('meter') # surface is 0 meter
for i in range(np.size(T)):
altitude[i] = mpcalc.thickness_hydrostatic(
p[:i + 1], T[:i + 1]) # Hypsometric Eq. for height
# specific entropy [joule/(kg*K)]
# sd : specific entropy of dry air
# sm1 : specific entropy of airborne mositure in state 1 (water vapor)
# sm2 : specific entropy of airborne mositure in state 2 (saturated water vapor)
sd = entropy(T.magnitude, q.magnitude * 1e-6, p.magnitude)
sm1 = entropy(T.magnitude, q.magnitude, p.magnitude)
sm2 = entropy(T.magnitude, qs.magnitude, p.magnitude)
###############################
# Water vapor calculations
p_PWtop = min(p)
#p_PWtop = max(200*units.mbar, min(p) + 1*units.mbar) # integrating until 200mb
cwv = mpcalc.precipitable_water(Td, p,
top=p_PWtop) # column water vapor [mm]
cwvs = mpcalc.precipitable_water(
T, p, top=p_PWtop) # saturated column water vapor [mm]
crh = (cwv / cwvs) * 100. # column relative humidity [%]
#================================================
# plotting MSE vertical profiles
fig = plt.figure(figsize=[12, 8])
ax = fig.add_axes([0.1, 0.1, 0.6, 0.8])
ax.plot(sd, p, '-k', linewidth=2)
ax.plot(sm1, p, '-b', linewidth=2)
ax.plot(sm2, p, '-r', linewidth=2)
# mse based on different percentages of relative humidity
qr = np.zeros((9, np.size(qs))) * units('kilogram/kilogram')
sm1_r = qr # container
for i in range(9):
qr[i, :] = qs * 0.1 * (i + 1)
sm1_r[i, :] = entropy(T.magnitude, qr[i, :].magnitude, p.magnitude)
for i in range(9):
ax.plot(sm1_r[i, :], p[:], '-', color='grey', linewidth=0.7)
ax.text(sm1_r[i, 3].magnitude - 2, p[3].magnitude, str((i + 1) * 10))
# drawing LCL and LFC levels
[lcl_pressure, lcl_temperature] = mpcalc.lcl(p[0], T[0], Td[0])
lcl_idx = np.argmin(np.abs(p.magnitude - lcl_pressure.magnitude))
[lfc_pressure, lfc_temperature] = mpcalc.lfc(p, T, Td)
lfc_idx = np.argmin(np.abs(p.magnitude - lfc_pressure.magnitude))
# conserved mse of air parcel arising from 1000 hpa
sm1_p = np.squeeze(np.ones((1, np.size(T))) * sm1[0])
# illustration of CAPE
el_pressure, el_temperature = mpcalc.el(p, T, Td) # equilibrium level
el_idx = np.argmin(np.abs(p.magnitude - el_pressure.magnitude))
ELps = [el_pressure.magnitude
] # Initialize an array of EL pressures for detrainment profile
[CAPE, CIN] = mpcalc.cape_cin(p[:el_idx], T[:el_idx], Td[:el_idx],
Tp[:el_idx])
plt.plot(sm1_p, p, color='green', linewidth=2)
#ax.fill_betweenx(p[lcl_idx:el_idx+1],sm1_p[lcl_idx:el_idx+1],sm2[lcl_idx:el_idx+1],interpolate=True
# ,color='green',alpha='0.3')
ax.fill_betweenx(p, sd, sm1, color='deepskyblue', alpha='0.5')
ax.set_xlabel('Specific entropies: sd, sm, sm_sat [J K$^{-1}$ kg$^{-1}$]',
fontsize=14)
ax.set_ylabel('Pressure [hPa]', fontsize=14)
ax.set_xticks([0, 50, 100, 150, 200, 250, 300, 350])
ax.set_xlim([0, 440])
ax.set_ylim(1030, 120)
if entrain is True:
# Depict Entraining parcels
# Parcel mass solves dM/dz = eps*M, solution is M = exp(eps*Z)
# M=1 at ground without loss of generality
# Distribution of surface parcel h offsets
h0offsets = np.sort(np.random.normal(
0, h0_std, ensemble_size)) * units('joule/kilogram')
# Distribution of entrainment rates
entrainment_rates = ent_rate / (units('km'))
for h0offset in h0offsets:
h4ent = sm1.copy()
h4ent[0] += h0offset
for eps in entrainment_rates:
M = np.exp(eps * (altitude - altitude[0])).to('dimensionless')
# dM is the mass contribution at each level, with 1 at the origin level.
M[0] = 0
dM = np.gradient(M)
# parcel mass is a sum of all the dM's at each level
# conserved linearly-mixed variables like h are weighted averages
if eps.magnitude == 0.0:
hent = np.ones(len(h4ent)) * h4ent[0] # no mixing
else:
hent = np.cumsum(dM * h4ent) / np.cumsum(dM)
# Boolean for positive buoyancy, and its topmost altitude (index) where curve is clippes
posboy = (hent > sm2)
posboy[0] = True # so there is always a detrainment level
# defining the first EL by posboy as the detrainment layer, swiching from positive buoyancy to
# negative buoyancy (0 to 1) and skipping the surface
ELindex_ent = 0
for idx in range(len(posboy) - 1):
if posboy[idx + 1] == 0 and posboy[idx] == 1 and idx > 0:
ELindex_ent = idx
break
# Plot the curve
plt.plot(hent[0:ELindex_ent + 2],
p[0:ELindex_ent + 2],
linewidth=0.6,
color='g')
#plt.plot( hent[0:], p[0:], linewidth=0.6, color='g')
# Keep a list for a histogram plot (detrainment profile)
if p[ELindex_ent].magnitude < lfc_pressure.magnitude: # buoyant parcels only
ELps.append(p[ELindex_ent].magnitude)
# Plot a crude histogram of parcel detrainment levels
NBINS = 20
pbins = np.linspace(1000, 150,
num=NBINS) # pbins for detrainment levels
hist = np.zeros((len(pbins) - 1))
for x in ELps:
for i in range(len(pbins) - 1):
if (x < pbins[i]) & (x >= pbins[i + 1]):
hist[i] += 1
break
det_per = hist / sum(hist) * 100
# percentages of detrainment ensumbles at levels
ax2 = fig.add_axes([0.705, 0.1, 0.1, 0.8], facecolor=None)
ax2.barh(pbins[1:],
det_per,
color='lightgrey',
edgecolor='k',
height=15 * (20 / NBINS))
ax2.set_xlim([0, 100])
ax2.set_xticks([0, 20, 40, 60, 80, 100])
ax2.set_ylim([1030, 120])
ax2.set_xlabel('Detrainment [%]')
ax2.grid()
ax2.set_zorder(2)
ax.plot([400, 400], [1100, 0])
ax.annotate('Detrainment', xy=(362, 320), color='dimgrey')
ax.annotate('ensemble: ' + str(ensemble_size * len(entrainment_rates)),
xy=(364, 340),
color='dimgrey')
ax.annotate('Detrainment', xy=(362, 380), color='dimgrey')
ax.annotate(' scale: 0 - 2 km', xy=(365, 400), color='dimgrey')
# Overplots on the mess: undilute parcel and CAPE, etc.
ax.plot((1, 1) * sm1[0], (1, 0) * (p[0]), color='g', linewidth=2)
# Replot the sounding on top of all that mess
ax.plot(sm2, p, color='r', linewidth=1.5)
ax.plot(sm1, p, color='b', linewidth=1.5)
# label LCL and LCF
ax.plot((sm2[lcl_idx] + (-2000, 2000) * units('joule/kilogram')),
lcl_pressure + (0, 0) * units('mbar'),
color='orange',
linewidth=3)
ax.plot((sm2[lfc_idx] + (-2000, 2000) * units('joule/kilogram')),
lfc_pressure + (0, 0) * units('mbar'),
color='magenta',
linewidth=3)
# Plot a crude histogram of parcel detrainment levels
# Text parts
ax.text(30, pressure[3], 'RH (%)', fontsize=11, color='k')
ax.text(20,
200,
'CAPE = ' + str(np.around(CAPE.magnitude, decimals=2)) + ' [J/kg]',
fontsize=12,
color='green')
ax.text(20,
250,
'CIN = ' + str(np.around(CIN.magnitude, decimals=2)) + ' [J/kg]',
fontsize=12,
color='green')
ax.text(20,
300,
'LCL = ' + str(np.around(lcl_pressure.magnitude, decimals=2)) +
' [hpa]',
fontsize=12,
color='darkorange')
ax.text(20,
350,
'LFC = ' + str(np.around(lfc_pressure.magnitude, decimals=2)) +
' [hpa]',
fontsize=12,
color='magenta')
ax.text(20,
400,
'CWV = ' + str(np.around(cwv.magnitude, decimals=2)) + ' [mm]',
fontsize=12,
color='deepskyblue')
ax.text(20,
450,
'CRH = ' + str(np.around(crh.magnitude, decimals=2)) + ' [%]',
fontsize=12,
color='blue')
ax.legend(['DEnt', 'MEnt', 'SMEnt'], fontsize=12, loc=1)
ax.set_zorder(3)
return (ax)
def do_profile(cursor, fid, gdf, nt):
"""Process this profile."""
# The inbound profile may contain mandatory level data that is below
# the surface. It seems the best we can do here is to ensure both
# temperature and dewpoint are valid and call that the bottom.
td_profile = gdf[pd.notnull(gdf["tmpc"]) & pd.notnull(gdf["dwpc"])]
wind_profile = gdf[pd.notnull(gdf["u"])]
# Presently we are all or nothing here. The length is arb
if len(td_profile.index) < 5 or len(wind_profile.index) < 5:
msg = ("quorum fail td: %s wind: %s, skipping") % (
len(td_profile.index),
len(wind_profile.index),
)
raise ValueError(msg)
if gdf["pressure"].min() > 500:
raise ValueError("Profile only up to %s mb" %
(gdf["pressure"].min(), ))
# Does a crude check that our metadata station elevation is within 50m
# of the profile bottom, otherwise we ABORT
station_elevation_m = get_station_elevation(td_profile, nt)
# get surface wind
u_sfc, v_sfc = get_surface_winds(wind_profile)
u_1km, v_1km = get_aloft_winds(wind_profile, station_elevation_m + 1000.0)
u_3km, v_3km = get_aloft_winds(wind_profile, station_elevation_m + 3000.0)
u_6km, v_6km = get_aloft_winds(wind_profile, station_elevation_m + 6000.0)
shear_sfc_1km_smps = np.sqrt((u_1km - u_sfc)**2 + (v_1km - v_sfc)**2)
shear_sfc_3km_smps = np.sqrt((u_3km - u_sfc)**2 + (v_3km - v_sfc)**2)
shear_sfc_6km_smps = np.sqrt((u_6km - u_sfc)**2 + (v_6km - v_sfc)**2)
total_totals = compute_total_totals(td_profile)
sweat_index = compute_sweat_index(td_profile, total_totals)
try:
bunkers_rm, bunkers_lm, mean0_6_wind = bunkers_storm_motion(
wind_profile["pressure"].values * units.hPa,
wind_profile["u"].values * units("m/s"),
wind_profile["v"].values * units("m/s"),
wind_profile["height"].values * units("m"),
)
except ValueError:
# Profile may not go up high enough
bunkers_rm = [np.nan * units("m/s"), np.nan * units("m/s")]
bunkers_lm = [np.nan * units("m/s"), np.nan * units("m/s")]
mean0_6_wind = [np.nan * units("m/s"), np.nan * units("m/s")]
bunkers_rm_smps = wind_speed(bunkers_rm[0], bunkers_rm[1])
bunkers_rm_drct = wind_direction(bunkers_rm[0], bunkers_rm[1])
bunkers_lm_smps = wind_speed(bunkers_lm[0], bunkers_lm[1])
bunkers_lm_drct = wind_direction(bunkers_lm[0], bunkers_lm[1])
mean0_6_wind_smps = wind_speed(mean0_6_wind[0], mean0_6_wind[1])
mean0_6_wind_drct = wind_direction(mean0_6_wind[0], mean0_6_wind[1])
try:
(
srh_sfc_1km_pos,
srh_sfc_1km_neg,
srh_sfc_1km_total,
) = storm_relative_helicity(
wind_profile["height"].values * units("m"),
wind_profile["u"].values * units("m/s"),
wind_profile["v"].values * units("m/s"),
1000.0 * units("m"),
)
except ValueError:
srh_sfc_1km_pos = np.nan * units("m") # blah
srh_sfc_1km_neg = np.nan * units("m") # blah
srh_sfc_1km_total = np.nan * units("m") # blah
try:
(
srh_sfc_3km_pos,
srh_sfc_3km_neg,
srh_sfc_3km_total,
) = storm_relative_helicity(
wind_profile["height"].values * units("m"),
wind_profile["u"].values * units("m/s"),
wind_profile["v"].values * units("m/s"),
3000.0 * units("m"),
)
except ValueError:
srh_sfc_3km_pos = np.nan * units("m") # blah
srh_sfc_3km_neg = np.nan * units("m") # blah
srh_sfc_3km_total = np.nan * units("m") # blah
pwater = precipitable_water(
td_profile["pressure"].values * units.hPa,
td_profile["dwpc"].values * units.degC,
)
(sbcape, sbcin) = surface_based_cape_cin(
td_profile["pressure"].values * units.hPa,
td_profile["tmpc"].values * units.degC,
td_profile["dwpc"].values * units.degC,
)
(mucape, mucin) = most_unstable_cape_cin(
td_profile["pressure"].values * units.hPa,
td_profile["tmpc"].values * units.degC,
td_profile["dwpc"].values * units.degC,
)
(mlcape, mlcin) = mixed_layer_cape_cin(
td_profile["pressure"].values * units.hPa,
td_profile["tmpc"].values * units.degC,
td_profile["dwpc"].values * units.degC,
)
el_p, el_t = el(
td_profile["pressure"].values * units.hPa,
td_profile["tmpc"].values * units.degC,
td_profile["dwpc"].values * units.degC,
)
lfc_p, lfc_t = lfc(
td_profile["pressure"].values * units.hPa,
td_profile["tmpc"].values * units.degC,
td_profile["dwpc"].values * units.degC,
)
(lcl_p, lcl_t) = lcl(
td_profile["pressure"].values[0] * units.hPa,
td_profile["tmpc"].values[0] * units.degC,
td_profile["dwpc"].values[0] * units.degC,
)
vals = [
el_p.to(units("hPa")).m,
lfc_p.to(units("hPa")).m,
lcl_p.to(units("hPa")).m,
]
[el_hght, lfc_hght, lcl_hght] = log_interp(
np.array(vals, dtype="f"),
td_profile["pressure"].values[::-1],
td_profile["height"].values[::-1],
)
el_agl = gt1(el_hght - station_elevation_m)
lcl_agl = gt1(lcl_hght - station_elevation_m)
lfc_agl = gt1(lfc_hght - station_elevation_m)
args = (
nonull(sbcape.to(units("joules / kilogram")).m),
nonull(sbcin.to(units("joules / kilogram")).m),
nonull(mucape.to(units("joules / kilogram")).m),
nonull(mucin.to(units("joules / kilogram")).m),
nonull(mlcape.to(units("joules / kilogram")).m),
nonull(mlcin.to(units("joules / kilogram")).m),
nonull(pwater.to(units("mm")).m),
nonull(el_agl),
nonull(el_p.to(units("hPa")).m),
nonull(el_t.to(units.degC).m),
nonull(lfc_agl),
nonull(lfc_p.to(units("hPa")).m),
nonull(lfc_t.to(units.degC).m),
nonull(lcl_agl),
nonull(lcl_p.to(units("hPa")).m),
nonull(lcl_t.to(units.degC).m),
nonull(total_totals),
nonull(sweat_index),
nonull(bunkers_rm_smps.m),
nonull(bunkers_rm_drct.m),
nonull(bunkers_lm_smps.m),
nonull(bunkers_lm_drct.m),
nonull(mean0_6_wind_smps.m),
nonull(mean0_6_wind_drct.m),
nonull(srh_sfc_1km_pos.m),
nonull(srh_sfc_1km_neg.m),
nonull(srh_sfc_1km_total.m),
nonull(srh_sfc_3km_pos.m),
nonull(srh_sfc_3km_neg.m),
nonull(srh_sfc_3km_total.m),
nonull(shear_sfc_1km_smps),
nonull(shear_sfc_3km_smps),
nonull(shear_sfc_6km_smps),
fid,
)
cursor.execute(
"""
UPDATE raob_flights SET sbcape_jkg = %s, sbcin_jkg = %s,
mucape_jkg = %s, mucin_jkg = %s,
mlcape_jkg = %s, mlcin_jkg = %s, pwater_mm = %s,
el_agl_m = %s, el_pressure_hpa = %s, el_tmpc = %s,
lfc_agl_m = %s, lfc_pressure_hpa = %s, lfc_tmpc = %s,
lcl_agl_m = %s, lcl_pressure_hpa = %s, lcl_tmpc = %s,
total_totals = %s, sweat_index = %s,
bunkers_rm_smps = %s, bunkers_rm_drct = %s,
bunkers_lm_smps = %s, bunkers_lm_drct = %s,
mean_sfc_6km_smps = %s, mean_sfc_6km_drct = %s,
srh_sfc_1km_pos = %s, srh_sfc_1km_neg = %s, srh_sfc_1km_total = %s,
srh_sfc_3km_pos = %s, srh_sfc_3km_neg = %s, srh_sfc_3km_total = %s,
shear_sfc_1km_smps = %s, shear_sfc_3km_smps = %s,
shear_sfc_6km_smps = %s,
computed = 't' WHERE fid = %s
""",
args,
)
def SkewT_plot(p,
T,
Td,
heights,
u=0,
v=0,
wind_barb=0,
p_lims=[1000, 100],
T_lims=[-50, 35],
metpy_logo=0,
plt_lfc=0,
plt_lcl=0,
plt_el=0,
title=None):
#plotting
fig = plt.figure(figsize=(10, 10))
skew = plots.SkewT(fig)
skew.plot(p, T, 'red') #Virtual Temperature
skew.plot(p, Td, 'green') #Dewpoint
skew.ax.set_ylim(p_lims)
skew.ax.set_xlim(T_lims)
if wind_barb == 1:
# resampling wind barbs
interval = np.logspace(2, 3) * units.hPa
idx = mpcalc.resample_nn_1d(p, interval)
skew.plot_barbs(p[idx], u[idx], v[idx])
#Showing Adiabasts and Mixing Ratio Lines
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
parcel_path = mpcalc.parcel_profile(p, T[0], Td[0])
skew.plot(p, parcel_path, color='k')
# CAPE and CIN
skew.shade_cape(p, T, parcel_path)
skew.shade_cin(p, T, parcel_path)
# Isotherms and Isobars
# skew.ax.axhline(500 * units.hPa, color='k')
# skew.ax.axvline(0 * units.degC, color='c')
# LCL, LFC, EL
lcl_p, lcl_T = mpcalc.lcl(p[0], T[0], Td[0])
lfc_p, lfc_T = mpcalc.lfc(p, T, Td)
el_p, el_T = mpcalc.el(p, T, Td)
if plt_lfc == 1:
skew.ax.axhline(lfc_p, color='k')
if plt_lcl == 1:
skew.ax.axhline(lcl_p, color='k')
# skew.ax.text(lcl_p, )
if plt_el == 1:
skew.ax.axhline(el_p, color='k')
if metpy_logo == 1: plots.add_metpy_logo(fig, x=55, y=50)
decimate = 3
for p, T, heights in zip(df['pressure'][::decimate],
df['temperature'][::decimate],
df['height'][::decimate]):
if p >= 700: skew.ax.text(T + 1, p, round(heights, 0))
plt.title(title)
plt.show()
return
'''
skew.shade_cin(p, T, prof_0)
skew.shade_cape(p, T, prof_0)
# Calculate LCL height and plot as black dot
lcl_pressure, lcl_temperature = mpcalc.lcl(
p[0], T[0], Td[0])
skew.plot(lcl_pressure,
lcl_temperature,
'ko',
markersize=8,
fillstyle='none',
label='LCL')
# Calculate LCF height and plot as purple dot
LCF_pressure, LCF_temperature = mpcalc.lfc(
p, T, Td, prof_0)
skew.plot(LCF_pressure,
LCF_temperature,
'rx',
markersize=8,
fillstyle='none',
label='LCF')
# Calculate EL height and plot as blue dot
EL_pressure, EL_temperature = mpcalc.el(p, T, Td, prof_0)
skew.plot(EL_pressure,
EL_temperature,
'bo',
markersize=8,
fillstyle='none',
label='EL')
def msed_plots(pressure,
temperature,
mixing_ratio,
h0_std=2000,
ensemble_size=20,
ent_rate=np.arange(0, 2, 0.05),
entrain=False):
"""
plotting the summarized static energy diagram with annotations and thermodynamic parameters
"""
p = pressure * units('mbar')
T = temperature * units('degC')
q = mixing_ratio * units('kilogram/kilogram')
qs = mpcalc.mixing_ratio(mpcalc.saturation_vapor_pressure(T), p)
Td = mpcalc.dewpoint(mpcalc.vapor_pressure(p, q)) # dewpoint
Tp = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC') # parcel profile
# Altitude based on the hydrostatic eq.
altitude = np.zeros((np.size(T))) * units('meter') # surface is 0 meter
for i in range(np.size(T)):
altitude[i] = mpcalc.thickness_hydrostatic(
p[:i + 1], T[:i + 1]) # Hypsometric Eq. for height
# Static energy calculations
mse = mpcalc.moist_static_energy(altitude, T, q)
mse_s = mpcalc.moist_static_energy(altitude, T, qs)
dse = mpcalc.dry_static_energy(altitude, T)
# Water vapor calculations
p_PWtop = max(200 * units.mbar,
min(p) + 1 * units.mbar) # integrating until 200mb
cwv = mpcalc.precipitable_water(Td, p,
top=p_PWtop) # column water vapor [mm]
cwvs = mpcalc.precipitable_water(
T, p, top=p_PWtop) # saturated column water vapor [mm]
crh = (cwv / cwvs) * 100. # column relative humidity [%]
#================================================
# plotting MSE vertical profiles
fig = plt.figure(figsize=[12, 8])
ax = fig.add_axes([0.1, 0.1, 0.6, 0.8])
ax.plot(dse, p, '-k', linewidth=2)
ax.plot(mse, p, '-b', linewidth=2)
ax.plot(mse_s, p, '-r', linewidth=2)
# mse based on different percentages of relative humidity
qr = np.zeros((9, np.size(qs))) * units('kilogram/kilogram')
mse_r = qr * units('joule/kilogram') # container
for i in range(9):
qr[i, :] = qs * 0.1 * (i + 1)
mse_r[i, :] = mpcalc.moist_static_energy(altitude, T, qr[i, :])
for i in range(9):
ax.plot(mse_r[i, :], p[:], '-', color='grey', linewidth=0.7)
ax.text(mse_r[i, 3].magnitude / 1000 - 1, p[3].magnitude,
str((i + 1) * 10))
# drawing LCL and LFC levels
[lcl_pressure, lcl_temperature] = mpcalc.lcl(p[0], T[0], Td[0])
lcl_idx = np.argmin(np.abs(p.magnitude - lcl_pressure.magnitude))
[lfc_pressure, lfc_temperature] = mpcalc.lfc(p, T, Td)
lfc_idx = np.argmin(np.abs(p.magnitude - lfc_pressure.magnitude))
# conserved mse of air parcel arising from 1000 hpa
mse_p = np.squeeze(np.ones((1, np.size(T))) * mse[0].magnitude)
# illustration of CAPE
el_pressure, el_temperature = mpcalc.el(p, T, Td) # equilibrium level
el_idx = np.argmin(np.abs(p.magnitude - el_pressure.magnitude))
ELps = [el_pressure.magnitude
] # Initialize an array of EL pressures for detrainment profile
[CAPE, CIN] = mpcalc.cape_cin(p[:el_idx], T[:el_idx], Td[:el_idx],
Tp[:el_idx])
plt.plot(mse_p, p, color='green', linewidth=2)
ax.fill_betweenx(p[lcl_idx:el_idx + 1],
mse_p[lcl_idx:el_idx + 1],
mse_s[lcl_idx:el_idx + 1],
interpolate=True,
color='green',
alpha='0.3')
ax.fill_betweenx(p, dse, mse, color='deepskyblue', alpha='0.5')
ax.set_xlabel('Specific static energies: s, h, hs [kJ kg$^{-1}$]',
fontsize=14)
ax.set_ylabel('Pressure [hpa]', fontsize=14)
ax.set_xticks([280, 300, 320, 340, 360, 380])
ax.set_xlim([280, 390])
ax.set_ylim(1030, 120)
if entrain is True:
# Depict Entraining parcels
# Parcel mass solves dM/dz = eps*M, solution is M = exp(eps*Z)
# M=1 at ground without loss of generality
# Distribution of surface parcel h offsets
H0STDEV = h0_std # J/kg
h0offsets = np.sort(np.random.normal(
0, H0STDEV, ensemble_size)) * units('joule/kilogram')
# Distribution of entrainment rates
entrainment_rates = ent_rate / (units('km'))
for h0offset in h0offsets:
h4ent = mse.copy()
h4ent[0] += h0offset
for eps in entrainment_rates:
M = np.exp(eps * (altitude - altitude[0])).to('dimensionless')
# dM is the mass contribution at each level, with 1 at the origin level.
M[0] = 0
dM = np.gradient(M)
# parcel mass is a sum of all the dM's at each level
# conserved linearly-mixed variables like h are weighted averages
hent = np.cumsum(dM * h4ent) / np.cumsum(dM)
# Boolean for positive buoyancy, and its topmost altitude (index) where curve is clippes
posboy = (hent > mse_s)
posboy[0] = True # so there is always a detrainment level
ELindex_ent = np.max(np.where(posboy))
# Plot the curve
plt.plot(hent[0:ELindex_ent + 2],
p[0:ELindex_ent + 2],
linewidth=0.25,
color='g')
# Keep a list for a histogram plot (detrainment profile)
if p[ELindex_ent].magnitude < lfc_pressure.magnitude: # buoyant parcels only
ELps.append(p[ELindex_ent].magnitude)
# Plot a crude histogram of parcel detrainment levels
NBINS = 20
pbins = np.linspace(1000, 150,
num=NBINS) # pbins for detrainment levels
hist = np.zeros((len(pbins) - 1))
for x in ELps:
for i in range(len(pbins) - 1):
if (x < pbins[i]) & (x >= pbins[i + 1]):
hist[i] += 1
break
det_per = hist / sum(hist) * 100
# percentages of detrainment ensumbles at levels
ax2 = fig.add_axes([0.705, 0.1, 0.1, 0.8], facecolor=None)
ax2.barh(pbins[1:],
det_per,
color='lightgrey',
edgecolor='k',
height=15 * (20 / NBINS))
ax2.set_xlim([0, max(det_per)])
ax2.set_ylim([1030, 120])
ax2.set_xlabel('Detrainment [%]')
ax2.grid()
ax2.set_zorder(2)
ax.plot([400, 400], [1100, 0])
ax.annotate('Detrainment', xy=(362, 320), color='dimgrey')
ax.annotate('ensemble: ' + str(ensemble_size * len(entrainment_rates)),
xy=(364, 340),
color='dimgrey')
ax.annotate('Detrainment', xy=(362, 380), color='dimgrey')
ax.annotate(' scale: 0 - 2 km', xy=(365, 400), color='dimgrey')
# Overplots on the mess: undilute parcel and CAPE, etc.
ax.plot((1, 1) * mse[0], (1, 0) * (p[0]), color='g', linewidth=2)
# Replot the sounding on top of all that mess
ax.plot(mse_s, p, color='r', linewidth=1.5)
ax.plot(mse, p, color='b', linewidth=1.5)
# label LCL and LCF
ax.plot((mse_s[lcl_idx] + (-2000, 2000) * units('joule/kilogram')),
lcl_pressure + (0, 0) * units('mbar'),
color='orange',
linewidth=3)
ax.plot((mse_s[lfc_idx] + (-2000, 2000) * units('joule/kilogram')),
lfc_pressure + (0, 0) * units('mbar'),
color='magenta',
linewidth=3)
### Internal waves (100m adiabatic displacements, assumed adiabatic: conserves s, sv, h).
#dZ = 100 *mpunits.units.meter
dp = 1000 * units.pascal
# depict displacements at sounding levels nearest these target levels
targetlevels = [900, 800, 700, 600, 500, 400, 300, 200] * units.hPa
for ilev in targetlevels:
idx = np.argmin(np.abs(p - ilev))
# dp: hydrostatic
rho = (p[idx]) / Rd / (T[idx])
dZ = -dp / rho / g
# dT: Dry lapse rate dT/dz_dry is -g/Cp
dT = (-g / Cp_d * dZ).to('kelvin')
Tdisp = T[idx].to('kelvin') + dT
# dhsat
dqs = mpcalc.mixing_ratio(mpcalc.saturation_vapor_pressure(Tdisp),
p[idx] + dp) - qs[idx]
dhs = g * dZ + Cp_d * dT + Lv * dqs
# Whiskers on the data plots
ax.plot((mse_s[idx] + dhs * (-1, 1)),
p[idx] + dp * (-1, 1),
linewidth=3,
color='r')
ax.plot((dse[idx] * (1, 1)),
p[idx] + dp * (-1, 1),
linewidth=3,
color='k')
ax.plot((mse[idx] * (1, 1)),
p[idx] + dp * (-1, 1),
linewidth=3,
color='b')
# annotation to explain it
if ilev == 400 * ilev.units:
ax.plot(360 * mse_s.units + dhs * (-1, 1) / 1000,
440 * units('mbar') + dp * (-1, 1),
linewidth=3,
color='r')
ax.annotate('+/- 10mb', xy=(362, 440), fontsize=8)
ax.annotate(' adiabatic displacement', xy=(362, 460), fontsize=8)
# Plot a crude histogram of parcel detrainment levels
# Text parts
ax.text(290, pressure[3], 'RH (%)', fontsize=11, color='k')
ax.text(285,
200,
'CAPE = ' + str(np.around(CAPE.magnitude, decimals=2)) + ' [J/kg]',
fontsize=12,
color='green')
ax.text(285,
250,
'CIN = ' + str(np.around(CIN.magnitude, decimals=2)) + ' [J/kg]',
fontsize=12,
color='green')
ax.text(285,
300,
'LCL = ' + str(np.around(lcl_pressure.magnitude, decimals=2)) +
' [hpa]',
fontsize=12,
color='darkorange')
ax.text(285,
350,
'LFC = ' + str(np.around(lfc_pressure.magnitude, decimals=2)) +
' [hpa]',
fontsize=12,
color='magenta')
ax.text(285,
400,
'CWV = ' + str(np.around(cwv.magnitude, decimals=2)) + ' [mm]',
fontsize=12,
color='deepskyblue')
ax.text(285,
450,
'CRH = ' + str(np.around(crh.magnitude, decimals=2)) + ' [%]',
fontsize=12,
color='blue')
ax.legend(['DSE', 'MSE', 'SMSE'], fontsize=12, loc=1)
ax.set_zorder(3)
return (ax)
def calculate_stability_indicies(
ds,
temp_name='temperature',
td_name='dewpoint_temperature',
p_name='pressure',
rh_name='relative_humidity',
moving_ave_window=0,
):
"""
Function for calculating stability indices from sounding data.
Parameters
----------
ds : ACT dataset
The dataset to compute the stability indicies of. Must have
temperature, dewpoint, and pressure in vertical coordinates.
temp_name : str
The name of the temperature field.
td_name : str
The name of the dewpoint field.
p_name : str
The name of the pressure field.
rh_name : str
The name of the relative humidity field.
moving_ave_window : int
Number of points to do a moving average on sounding data to reduce
noise. This is useful if noise in the sounding is preventing parcel
ascent.
Returns
-------
ds : ACT dataset
An ACT dataset with additional stability indicies added.
"""
if not METPY_AVAILABLE:
raise ImportError(
'MetPy need to be installed on your system to ' + 'calculate stability indices'
)
t = ds[temp_name]
td = ds[td_name]
p = ds[p_name]
rh = ds[rh_name]
if not hasattr(t, 'units'):
raise AttributeError('Temperature field must have units' + ' for ACT to discern!')
if not hasattr(td, 'units'):
raise AttributeError('Dewpoint field must have units' + ' for ACT to discern!')
if not hasattr(p, 'units'):
raise AttributeError('Pressure field must have units' + ' for ACT to discern!')
if t.units == 'C':
t_units = units.degC
else:
t_units = getattr(units, t.units)
if td.units == 'C':
td_units = units.degC
else:
td_units = getattr(units, td.units)
p_units = getattr(units, p.units)
rh_units = getattr(units, rh.units)
# Sort all values by decreasing pressure
t_sorted = np.array(t.values)
td_sorted = np.array(td.values)
p_sorted = np.array(p.values)
rh_sorted = np.array(rh.values)
ind_sort = np.argsort(p_sorted)
t_sorted = t_sorted[ind_sort[-1:0:-1]]
td_sorted = td_sorted[ind_sort[-1:0:-1]]
p_sorted = p_sorted[ind_sort[-1:0:-1]]
rh_sorted = rh_sorted[ind_sort[-1:0:-1]]
if moving_ave_window > 0:
t_sorted = np.convolve(t_sorted, np.ones((moving_ave_window,)) / moving_ave_window)
td_sorted = np.convolve(td_sorted, np.ones((moving_ave_window,)) / moving_ave_window)
p_sorted = np.convolve(p_sorted, np.ones((moving_ave_window,)) / moving_ave_window)
rh_sorted = np.convolve(rh_sorted, np.ones((moving_ave_window,)) / moving_ave_window)
t_sorted = t_sorted * t_units
td_sorted = td_sorted * td_units
p_sorted = p_sorted * p_units
rh_sorted = rh_sorted * rh_units
# Calculate mixing ratio
mr = mpcalc.mixing_ratio_from_relative_humidity(p_sorted, t_sorted, rh_sorted)
# Discussion of issue #361 use virtual temperature.
vt = mpcalc.virtual_temperature(t_sorted, mr)
t_profile = mpcalc.parcel_profile(p_sorted, t_sorted[0], td_sorted[0])
# Calculate parcel trajectory
ds['parcel_temperature'] = t_profile.magnitude
ds['parcel_temperature'].attrs['units'] = t_profile.units
# Calculate CAPE, CIN, LCL
sbcape, sbcin = mpcalc.surface_based_cape_cin(p_sorted, vt, td_sorted)
lcl = mpcalc.lcl(p_sorted[0], t_sorted[0], td_sorted[0])
try:
lfc = mpcalc.lfc(p_sorted[0], t_sorted[0], td_sorted[0])
except IndexError:
lfc = np.nan * p_sorted.units
mucape, mucin = mpcalc.most_unstable_cape_cin(p_sorted, vt, td_sorted)
where_500 = np.argmin(np.abs(p_sorted - 500 * units.hPa))
li = t_sorted[where_500] - t_profile[where_500]
ds['surface_based_cape'] = sbcape.magnitude
ds['surface_based_cape'].attrs['units'] = 'J/kg'
ds['surface_based_cape'].attrs['long_name'] = 'Surface-based CAPE'
ds['surface_based_cin'] = sbcin.magnitude
ds['surface_based_cin'].attrs['units'] = 'J/kg'
ds['surface_based_cin'].attrs['long_name'] = 'Surface-based CIN'
ds['most_unstable_cape'] = mucape.magnitude
ds['most_unstable_cape'].attrs['units'] = 'J/kg'
ds['most_unstable_cape'].attrs['long_name'] = 'Most unstable CAPE'
ds['most_unstable_cin'] = mucin.magnitude
ds['most_unstable_cin'].attrs['units'] = 'J/kg'
ds['most_unstable_cin'].attrs['long_name'] = 'Most unstable CIN'
ds['lifted_index'] = li.magnitude
ds['lifted_index'].attrs['units'] = t_profile.units
ds['lifted_index'].attrs['long_name'] = 'Lifted index'
ds['level_of_free_convection'] = lfc.magnitude
ds['level_of_free_convection'].attrs['units'] = lfc.units
ds['level_of_free_convection'].attrs['long_name'] = 'Level of free convection'
ds['lifted_condensation_level_temperature'] = lcl[1].magnitude
ds['lifted_condensation_level_temperature'].attrs['units'] = lcl[1].units
ds['lifted_condensation_level_temperature'].attrs[
'long_name'
] = 'Lifted condensation level temperature'
ds['lifted_condensation_level_pressure'] = lcl[0].magnitude
ds['lifted_condensation_level_pressure'].attrs['units'] = lcl[0].units
ds['lifted_condensation_level_pressure'].attrs[
'long_name'
] = 'Lifted condensation level pressure'
return ds
def cape_cin(pressure, temperature, dewpt, parcel_profile, dz, temp):
r"""Calculate CAPE and CIN.
This script is originally from Metpy module but it was not avaialble in Python 3.6.3 Anaconda version. JLGF.
Calculate the convective available potential energy (CAPE) and convective inhibition (CIN)
of a given upper air profile and parcel path. CIN is integrated between the surface and
LFC, CAPE is integrated between the LFC and EL (or top of sounding). Intersection points of
the measured temperature profile and parcel profile are linearly interpolated.
Especifically this script has been adapted from :cite:`montearl` and :cite:`molinari2010` which use a very particular function for CAPE.
CAPE is not trivially computed from dropsonde measurements and several cautions are extended:
1. Vertical profiles usually do not reach the equilibrium level (EL) but instead are cut-off at 8-9 km.
2. Typical CAPE formula estimates the area of the difference between parcel and environmental profiles, however, this method uses a more robust approach :cite:`bogner2000`.
3. Several corrections would need to be in place for this script to be comparable to other studies (see above). It is then a simple approximation and by no means a complete and thorough algorithm.
Parameters
----------
pressure : `pint.Quantity`
The atmospheric pressure level(s) of interest. The first entry should be the starting
point pressure.
temperature : `pint.Quantity`
The atmospheric temperature corresponding to pressure.
dewpt : `pint.Quantity`
The atmospheric dew point corresponding to pressure.
parcel_profile : `pint.Quantity`
The temperature profile of the parcel
Returns
-------
`pint.Quantity`
Convective available potential energy (CAPE).
`pint.Quantity`
Convective inhibition (CIN).
Notes
-----
Formula adopted from :cite:`montearl`
.. math:: \text{CAPE} = \int_{LFC}^{EL} g\frac{(T_{v} - T_{ve})}{\overline{T_{ve}}} dz
.. math:: \text{CIN} = \int_{SFC}^{LFC} g\frac{(T_{v} - T_{ve})}{\overline{T_{ve}}} dz
* :math:`CAPE` Convective available potential energy
* :math:`CIN` Convective inhibition
* :math:`LFC` Pressure of the level of free convection
* :math:`EL` Pressure of the equilibrium level
* :math:`SFC` Level of the surface or beginning of parcel path
* :math:`g` Gravitational acceleration
* :math:`T_{v}` Parcel potential temperature.
* :math:`T_{ve}` Environmental potential temperature.
* :math:`\overline{T_{ve}}` Mean environmental potential temperature.
* :math:`dz` Height array differential.
See Also
:meth:`toolbox._find_append_zero_crossings`, :meth:`toolbox.parcel_profile`
"""
# Calculate LFC limit of integration
lfc_pressure = mpcalc.lfc(pressure, temperature, dewpt)[0]
g = 9.806 * units.m / units.s**2
# If there is no LFC, no need to proceed.
if np.isnan(lfc_pressure):
return 0 * units('J/kg'), 0 * units('J/kg')
else:
lfc_pressure = lfc_pressure.magnitude
# Calculate the EL limit of integration
el_pressure = mpcalc.el(pressure, temperature, dewpt)[0]
# No EL and we use the top reading of the sounding.
if np.isnan(el_pressure):
el_pressure = pressure[-1].magnitude
else:
el_pressure = el_pressure.magnitude
# Difference between the parcel path and measured temperature profiles
y = (parcel_profile - temperature).to(units.degK)
dzx, yz = _find_append_zero_crossings(np.copy(dz), y)
# Estimate zero crossings
x, y = _find_append_zero_crossings(np.copy(pressure), y)
x = np.flip(x, 0)
lfc_height = np.nanmean(dzx[np.isclose(x, lfc_pressure, atol=1.5)])
el_height = np.nanmean(dzx[np.isclose(x, el_pressure, atol=1.5)])
Tv_env = np.nanmean(temperature[np.where((dz > lfc_height)
& (dz < el_height))]) * units.degK
# CAPE
# Only use data between the LFC and EL for calculation
p_mask = _less_or_close(x, lfc_pressure) & _greater_or_close(
x, el_pressure)
z_mask = _less_or_close(dzx, lfc_height) & _greater_or_close(
dzx, el_height)
x_clipped = dzx[p_mask]
y_clipped = yz[p_mask]
cape = ((g / Tv_env) * (np.trapz(y_clipped, x_clipped, np.diff(dz)) *
units.degK * units.m)).to(units('J/kg'))
# CIN
# Only use data between the surface and LFC for calculation
p_mask = _greater_or_close(x, lfc_pressure)
x_clipped = x[p_mask]
y_clipped = y[p_mask]
cin = ((g / Tv_env) * (np.trapz(y_clipped, x_clipped, np.diff(dz)) *
units.degK * units.m)).to(units('J/kg'))
return cape, cin