def test_no_el():
"""Test equilibrium layer calculation when there is no EL 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([19., 14.3, -11.2, -16.7, -21., -43.3, -56.7]) * units.celsius
el_pressure, el_temperature = el(levels, temperatures, dewpoints)
assert assert_nan(el_pressure, levels.units)
assert assert_nan(el_temperature, temperatures.units)
def test_no_el():
"""Test equilibrium layer calculation when there is no EL 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([19., 14.3, -11.2, -16.7, -21., -43.3, -56.7]) * units.celsius
el_pressure, el_temperature = el(levels, temperatures, dewpoints)
assert assert_nan(el_pressure, levels.units)
assert assert_nan(el_temperature, temperatures.units)
def test_el():
"""Test equilibrium layer 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., -38.]) * units.celsius
dewpoints = np.array([19., -11.2, -10.8, -10.4, -10., -53.2]) * units.celsius
el_pressure, el_temperature = el(levels, temperatures, dewpoints)
assert_almost_equal(el_pressure, 520.8700 * units.mbar, 3)
assert_almost_equal(el_temperature, -11.7027 * units.degC, 3)
def test_el():
"""Test equilibrium layer 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., -38.]) * units.celsius
dewpoints = np.array([19., -11.2, -10.8, -10.4, -10., -53.2]) * units.celsius
el_pressure, el_temperature = el(levels, temperatures, dewpoints)
assert_almost_equal(el_pressure, 520.8700 * units.mbar, 3)
assert_almost_equal(el_temperature, -11.7027 * units.degC, 3)
def test_el_ml():
"""Test equilibrium layer calculation for a mixed parcel."""
levels = np.array([959., 779.2, 751.3, 724.3, 700., 400., 269.]) * units.mbar
temperatures = np.array([22.2, 14.6, 12., 9.4, 7., -25., -35.]) * units.celsius
dewpoints = np.array([19., -11.2, -10.8, -10.4, -10., -35., -53.2]) * units.celsius
__, t_mixed, td_mixed = mixed_parcel(levels, temperatures, dewpoints)
mixed_parcel_prof = parcel_profile(levels, t_mixed, td_mixed)
el_pressure, el_temperature = el(levels, temperatures, dewpoints, mixed_parcel_prof)
assert_almost_equal(el_pressure, 355.834 * units.mbar, 3)
assert_almost_equal(el_temperature, -28.371 * units.degC, 3)
def test_no_el_multi_crossing():
"""Test el calculation with no el and severel parcel path-profile crossings."""
levels = np.array([918., 911., 880., 873.9, 850., 848., 843.5, 818., 813.8, 785.,
773., 763., 757.5, 730.5, 700., 679., 654.4, 645.,
643.9]) * units.mbar
temperatures = np.array([24.2, 22.8, 19.6, 19.1, 17., 16.8, 16.5, 15., 14.9, 14.4, 16.4,
16.2, 15.7, 13.4, 10.6, 8.4, 5.7, 4.6, 4.5]) * units.celsius
dewpoints = np.array([19.5, 17.8, 16.7, 16.5, 15.8, 15.7, 15.3, 13.1, 12.9, 11.9, 6.4,
3.2, 2.6, -0.6, -4.4, -6.6, -9.3, -10.4, -10.5]) * units.celsius
el_pressure, el_temperature = el(levels, temperatures, dewpoints)
assert assert_nan(el_pressure, levels.units)
assert assert_nan(el_temperature, temperatures.units)
def test_no_el_multi_crossing():
"""Test el calculation with no el and severel parcel path-profile crossings."""
levels = np.array([918., 911., 880., 873.9, 850., 848., 843.5, 818., 813.8, 785.,
773., 763., 757.5, 730.5, 700., 679., 654.4, 645.,
643.9]) * units.mbar
temperatures = np.array([24.2, 22.8, 19.6, 19.1, 17., 16.8, 16.5, 15., 14.9, 14.4, 16.4,
16.2, 15.7, 13.4, 10.6, 8.4, 5.7, 4.6, 4.5]) * units.celsius
dewpoints = np.array([19.5, 17.8, 16.7, 16.5, 15.8, 15.7, 15.3, 13.1, 12.9, 11.9, 6.4,
3.2, 2.6, -0.6, -4.4, -6.6, -9.3, -10.4, -10.5]) * units.celsius
el_pressure, el_temperature = el(levels, temperatures, dewpoints)
assert assert_nan(el_pressure, levels.units)
assert assert_nan(el_temperature, temperatures.units)
def test_el_small_surface_instability():
"""Test that no EL is found when there is a small pocket of instability at the sfc."""
levels = np.array([959., 931.3, 925., 899.3, 892., 867.9, 850., 814.,
807.9, 790., 779.2, 751.3, 724.3, 700., 655., 647.5,
599.4, 554.7, 550., 500.]) * units.mbar
temperatures = np.array([22.2, 20.2, 19.8, 18.4, 18., 17.4, 17., 15.4, 15.4,
15.6, 14.6, 12., 9.4, 7., 2.2, 1.4, -4.2, -9.7,
-10.3, -14.9]) * units.degC
dewpoints = np.array([20., 18.5, 18.1, 17.9, 17.8, 15.3, 13.5, 6.4, 2.2,
-10.4, -10.2, -9.8, -9.4, -9., -15.8, -15.7, -14.8, -14.,
-13.9, -17.9]) * units.degC
el_pressure, el_temperature = el(levels, temperatures, dewpoints)
assert assert_nan(el_pressure, levels.units)
assert assert_nan(el_temperature, temperatures.units)
def test_el_small_surface_instability():
"""Test that no EL is found when there is a small pocket of instability at the sfc."""
levels = np.array([959., 931.3, 925., 899.3, 892., 867.9, 850., 814.,
807.9, 790., 779.2, 751.3, 724.3, 700., 655., 647.5,
599.4, 554.7, 550., 500.]) * units.mbar
temperatures = np.array([22.2, 20.2, 19.8, 18.4, 18., 17.4, 17., 15.4, 15.4,
15.6, 14.6, 12., 9.4, 7., 2.2, 1.4, -4.2, -9.7,
-10.3, -14.9]) * units.degC
dewpoints = np.array([20., 18.5, 18.1, 17.9, 17.8, 15.3, 13.5, 6.4, 2.2,
-10.4, -10.2, -9.8, -9.4, -9., -15.8, -15.7, -14.8, -14.,
-13.9, -17.9]) * units.degC
el_pressure, el_temperature = el(levels, temperatures, dewpoints)
assert assert_nan(el_pressure, levels.units)
assert assert_nan(el_temperature, temperatures.units)
def test_no_el_parcel_colder():
"""Test no EL when parcel stays colder than environment. INL 20170925-12Z."""
levels = np.array([974., 946., 925., 877.2, 866., 850., 814.6, 785.,
756.6, 739., 729.1, 700., 686., 671., 641., 613.,
603., 586., 571., 559.3, 539., 533., 500., 491.,
477.9, 413., 390., 378., 345., 336.]) * units.mbar
temperatures = np.array([10., 8.4, 7.6, 5.9, 7.2, 7.6, 6.8, 7.1, 7.7,
7.8, 7.7, 5.6, 4.6, 3.4, 0.6, -0.9, -1.1, -3.1,
-4.7, -4.7, -6.9, -7.5, -11.1, -10.9, -12.1, -20.5, -23.5,
-24.7, -30.5, -31.7]) * units.celsius
dewpoints = np.array([8.9, 8.4, 7.6, 5.9, 7.2, 7., 5., 3.6, 0.3,
-4.2, -12.8, -12.4, -8.4, -8.6, -6.4, -7.9, -11.1, -14.1,
-8.8, -28.1, -18.9, -14.5, -15.2, -15.1, -21.6, -41.5, -45.5,
-29.6, -30.6, -32.1]) * units.celsius
el_pressure, el_temperature = el(levels, temperatures, dewpoints)
assert assert_nan(el_pressure, levels.units)
assert assert_nan(el_temperature, temperatures.units)
def test_no_el_parcel_colder():
"""Tests no EL when parcel stays colder than environment. INL 20170925-12Z."""
levels = np.array([974., 946., 925., 877.2, 866., 850., 814.6, 785.,
756.6, 739., 729.1, 700., 686., 671., 641., 613.,
603., 586., 571., 559.3, 539., 533., 500., 491.,
477.9, 413., 390., 378., 345., 336.]) * units.mbar
temperatures = np.array([10., 8.4, 7.6, 5.9, 7.2, 7.6, 6.8, 7.1, 7.7,
7.8, 7.7, 5.6, 4.6, 3.4, 0.6, -0.9, -1.1, -3.1,
-4.7, -4.7, -6.9, -7.5, -11.1, -10.9, -12.1, -20.5, -23.5,
-24.7, -30.5, -31.7]) * units.celsius
dewpoints = np.array([8.9, 8.4, 7.6, 5.9, 7.2, 7., 5., 3.6, 0.3,
-4.2, -12.8, -12.4, -8.4, -8.6, -6.4, -7.9, -11.1, -14.1,
-8.8, -28.1, -18.9, -14.5, -15.2, -15.1, -21.6, -41.5, -45.5,
-29.6, -30.6, -32.1]) * units.celsius
el_pressure, el_temperature = el(levels, temperatures, dewpoints)
assert assert_nan(el_pressure, levels.units)
assert assert_nan(el_temperature, temperatures.units)
def test_el_lfc_equals_lcl():
"""Test equilibrium layer calculation when the lfc equals the lcl."""
levels = np.array([912., 905.3, 874.4, 850., 815.1, 786.6, 759.1, 748.,
732.3, 700., 654.8, 606.8, 562.4, 501.8, 500., 482.,
400., 393.3, 317.1, 307., 300., 252.7, 250., 200.,
199.3, 197., 190., 172., 156.6, 150., 122.9, 112.,
106.2, 100.]) * 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, 2.2, -2.7, -10.1,
-10.3, -12.4, -23.3, -24.4, -38., -40.1, -41.1,
-49.8, -50.3, -59.1, -59.1, -59.3, -59.7, -56.3,
-56.9, -57.1, -59.1, -60.1, -58.6, -56.9]) * 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, -27.8, -32.7, -40.1, -40.3, -42.4, -53.3,
-54.4, -68., -70.1, -70., -70., -70., -70., -70., -70.,
-70., -70., -70., -70., -70., -70., -70., -70.]) * units.celsius
el_pressure, el_temperature = el(levels, temperatures, dewpoints)
assert_almost_equal(el_pressure, 175.8684 * units.mbar, 3)
assert_almost_equal(el_temperature, -57.0307 * units.degC, 3)
def test_el_lfc_equals_lcl():
"""Test equilibrium layer calculation when the lfc equals the lcl."""
levels = np.array([912., 905.3, 874.4, 850., 815.1, 786.6, 759.1, 748.,
732.3, 700., 654.8, 606.8, 562.4, 501.8, 500., 482.,
400., 393.3, 317.1, 307., 300., 252.7, 250., 200.,
199.3, 197., 190., 172., 156.6, 150., 122.9, 112.,
106.2, 100.]) * 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, 2.2, -2.7, -10.1,
-10.3, -12.4, -23.3, -24.4, -38., -40.1, -41.1,
-49.8, -50.3, -59.1, -59.1, -59.3, -59.7, -56.3,
-56.9, -57.1, -59.1, -60.1, -58.6, -56.9]) * 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, -27.8, -32.7, -40.1, -40.3, -42.4, -53.3,
-54.4, -68., -70.1, -70., -70., -70., -70., -70., -70.,
-70., -70., -70., -70., -70., -70., -70., -70.]) * units.celsius
el_pressure, el_temperature = el(levels, temperatures, dewpoints)
assert_almost_equal(el_pressure, 175.8684 * units.mbar, 3)
assert_almost_equal(el_temperature, -57.0307 * units.degC, 3)
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 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 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 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
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')
# Calculate most_unstable_parcel
mup_pressure, mup_temperature, mup_dewTemperature, mup_index = mpcalc.most_unstable_parcel(
p,
T,
Td,
heights=prof_0,
bottom=1050 * units.hPa,
depth=650 * units.hPa)
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 EnergyMassPlot(pressure, temperature, dewpoint,
height, uwind, vwind, sphum=None, rh=None,
label='', size=(12,10), return_fig=False):
p=pressure
Z=height
T=temperature
Td=dewpoint
if isinstance(sphum,np.ndarray) and isinstance(rh,np.ndarray):
q=sphum
qs=q/rh
else:
q = mpcalc.mixing_ratio(mpcalc.saturation_vapor_pressure(Td),p)
qs= mpcalc.mixing_ratio(mpcalc.saturation_vapor_pressure(T),p)
s = g*Z + Cp_d*T
sv= g*Z + Cp_d*mpcalc.virtual_temperature(T,q)
h = s + Lv*q
hs= s + Lv*qs
parcel_Tprofile = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC')
CAPE,CIN = mpcalc.cape_cin(p,T,Td,parcel_Tprofile)
ELp,ELT = mpcalc.el(p,T,Td)
ELindex = np.argmin(np.abs(p - ELp))
lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0])
p_PWtop = max(200*units.mbar, min(p) +1*units.mbar)
PW = mpcalc.precipitable_water(Td,p, top=p_PWtop)
PWs = mpcalc.precipitable_water(T,p, top=p_PWtop)
CRH = (PW/PWs).magnitude *100.
fig,ax=setup_fig(size=size,label=label)
ax.plot(s /1000, p, color='r', linewidth=1.5) ### /1000 for kJ/kg
ax.plot(sv /1000, p, color='r', linestyle='-.')
ax.plot(h /1000, p, color='b', linewidth=1.5)
ax.plot(hs /1000, p, color='r', linewidth=1.5)
### RH rulings between s and h lines: annotate near 800 hPa level
annot_level = 800 #hPa
idx = np.argmin(np.abs(p - annot_level *units.hPa))
right_annot_loc = 380
for iRH in np.arange(10,100,10):
ax.plot( (s+ Lv*qs*iRH/100.)/1000, p, linewidth=0.5, linestyle=':', color='k')
ax.annotate(str(iRH), xy=( (s[idx]+Lv*qs[idx]*iRH/100.)/1000, annot_level),
horizontalalignment='center',fontsize=6)
ax.annotate('RH (%)', xy=(right_annot_loc, annot_level), fontsize=10)
if not np.isnan(CAPE.magnitude) and CAPE.magnitude >10:
parcelh = h [0] # for a layer mean: np.mean(h[idx1:idx2])
parcelsv = sv[0]
parcelp0 = p[0]
# Undilute parcel
ax.plot( (1,1)*parcelh/1000., (1,0)*parcelp0, linewidth=0.5, color='g')
maxbindex = np.argmax(parcel_Tprofile - T)
ax.annotate('CAPE='+str(int(CAPE.magnitude)),
xy=(parcelh/1000., p[maxbindex]), color='g')
# Plot LCL at saturation point, above the lifted sv of the surface parcel
lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0])
ax.annotate('LCL', xy=(sv[0]/1000., lcl_pressure), fontsize=10, color='g', horizontalalignment='right')
# Purple fill for negative buoyancy below LCL:
ax.fill_betweenx(p, sv/1000., parcelsv/1000., where=p>lcl_pressure, facecolor='purple', alpha=0.4)
# Positive moist convective buoyancy in green
# Above LCL:
ax.fill_betweenx(p, hs/1000., parcelh/1000., where= parcelh>hs, facecolor='g', alpha=0.4)
# Depict Entraining parcels
# Parcel mass solves dM/dz = eps*M, solution is M = exp(eps*Z)
# M=1 at ground without loss of generality
entrainment_distance = 10000., 5000., 2000.
ax.annotate('entrain: 10,5,2 km', xy=(parcelh/1000, 140), color='g',
fontsize=8, horizontalalignment='right')
ax.annotate('parcel h', xy=(parcelh/1000, 120), color='g',
fontsize=10, horizontalalignment='right')
for ED in entrainment_distance:
eps = 1.0 / (ED*units.meter)
M = np.exp(eps * (Z-Z[0]).to('m')).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*h) / np.cumsum(dM)
ax.plot( hent[0:ELindex+3]/1000., p[0:ELindex+3], linewidth=0.5, color='g')
### Internal waves (100m adiabatic displacements, assumed adiabatic: conserves s, sv, h).
dZ = 100 *units.meter
# 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))
# dT: Dry lapse rate dT/dz_dry is -g/Cp
dT = (-g/Cp_d *dZ).to('kelvin')
Tdisp = T[idx].to('kelvin') + dT
# dp: hydrostatic
rho = (p[idx]/Rd/T[idx])
dp = -rho*g*dZ
# dhsat
#qs = mpcalc.mixing_ratio(mpcalc.saturation_vapor_pressure(T) ,p)
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( (hs[idx]+dhs*(-1,1))/1000, p[idx]+dp*(-1,1), linewidth=3, color='r')
ax.plot( (s [idx] *( 1,1))/1000, p[idx]+dp*(-1,1), linewidth=3, color='r')
ax.plot( (h [idx] *( 1,1))/1000, p[idx]+dp*(-1,1), linewidth=3, color='b')
# annotation to explain it
if ilev == 600*ilev.units:
ax.plot(right_annot_loc*hs.units +dhs*(-1,1)/1000, p[idx]+dp*(-1,1), linewidth=3, color='r')
ax.annotate('+/- 100m', xy=(right_annot_loc,600), fontsize=8)
ax.annotate(' internal', xy=(right_annot_loc,630), fontsize=8)
ax.annotate(' waves', xy=(right_annot_loc,660), fontsize=8)
### Blue fill proportional to precipitable water, and blue annotation
ax.fill_betweenx(p, s/1000., h/1000., where=h > s, facecolor='b', alpha=0.4)
# Have to specify the top of the PW integral.
# I want whole atmosphere of course, but 200 hPa captures it all really.
#import metpy.calc as mpcalc
p_PWtop = max(200*units.mbar, min(p) +1*units.mbar)
PW = mpcalc.precipitable_water(Td,p, top=p_PWtop)
PWs = mpcalc.precipitable_water(T,p, top=p_PWtop)
CRH = (PW/PWs).magnitude *100.
# PW annotation arrow tip at 700 mb
idx = np.argmin(np.abs(p - 700*p.units))
centerblue = (s[idx]+h[idx])/2.0 /1000.
ax.annotate('CWV='+str(round(PW.to('mm').magnitude, 1))+'mm',
xy=(centerblue, 700), xytext=(285, 200),
color='blue', fontsize=15,
arrowprops=dict(width = 1, edgecolor='blue', shrink=0.02),
)
ax.annotate('(' + str(round(CRH,1)) +'% of sat)',
xy=(285, 230), color='blue', fontsize=12)
### Surface water values at 1C intervals, for eyeballing surface fluxes
sT = np.trunc(T[0].to('degC'))
sTint = int(sT.magnitude)
for idT in [-2,0,2,4]:
ssTint = sTint + idT # UNITLESS degC integers, for labels
# Kelvin values for computations
ssTC = ssTint * units.degC
ssTK = ssTC.to('kelvin')
ss = g*Z[0] + Cp_d*ssTK
hs = ss + Lv*mpcalc.mixing_ratio(mpcalc.saturation_vapor_pressure(ssTK) ,p[0])
ax.annotate(str(ssTint), xy=(ss/1000., p[0]+0*p.units),
verticalalignment='top', horizontalalignment='center',
color='red', fontsize=7)
ax.annotate(str(ssTint), xy=(hs/1000., p[0]+0*p.units),
verticalalignment='top', horizontalalignment='center',
color='red', fontsize=9)
ax.annotate('\u00b0C water', xy=(right_annot_loc, p[0]), verticalalignment='top',
fontsize=10, color='r')
if return_fig:
return ax,fig
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
#plots the barbs
my_interval = np.arange(100, 1000, 20) * units('mbar')
ix = resample_nn_1d(p, my_interval)
skew.plot_barbs(p[ix], u[ix], v[ix])
skew.ax.set_ylim(1075, 100)
skew.ax.set_ylabel('Pressure (hPa)')
lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0]) #LCL
pwat = mpcalc.precipitable_water(Td, p, 500 * units.hectopascal).to('in') #PWAT
cape, cin = mpcalc.most_unstable_cape_cin(p[:], T[:], Td[:]) #MUCAPE
cape_sfc, cin_sfc = mpcalc.surface_based_cape_cin(p, T, Td) #SBCAPE
prof = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC') #parcel profile
equiv_pot_temp = mpcalc.equivalent_potential_temperature(p, T, Td) #equivalent potential temperature
el_pressure, el_temperature = mpcalc.el(p, T, Td) #elevated level
lfc_pressure, lfc_temperature = mpcalc.lfc(p, T, Td) #LFC
#calculates shear
u_threekm_bulk_shear, v_threekm_bulk_shear = mpcalc.bulk_shear(p, u, v, hgt, bottom = min(hgt), depth = 3000 * units.meter)
threekm_bulk_shear = mpcalc.get_wind_speed(u_threekm_bulk_shear, v_threekm_bulk_shear)
u_onekm_bulk_shear, v_onekm_bulk_shear = mpcalc.bulk_shear(p, u, v, hgt, bottom = min(hgt), depth = 1000 * units.meter)
onekm_bulk_shear = mpcalc.get_wind_speed(u_onekm_bulk_shear, v_onekm_bulk_shear)
#shows the level of the LCL, LFC, and EL.
skew.ax.text(T[0].magnitude, p[0].magnitude + 5, str(int(np.round(T[0].to('degF').magnitude))), fontsize = 'medium', horizontalalignment = 'left', verticalalignment = 'top', color = 'red')
skew.ax.text(Td[0].magnitude, p[0].magnitude + 5, str(int(np.round(Td[0].to('degF').magnitude))), fontsize = 'medium', horizontalalignment = 'right', verticalalignment = 'top', color = 'green')
skew.ax.text(lcl_temperature.magnitude + 5, lcl_pressure.magnitude, "---- LCL", fontsize = 'medium', verticalalignment = 'center')
skew.ax.text(Td[0].magnitude - 10, p[0].magnitude, 'SFC: {}hPa ----'.format(p[0].magnitude), fontsize = 'medium', horizontalalignment = 'right', verticalalignment = 'center', color = 'black')
if str(lfc_temperature.magnitude) != 'nan': #checks to see if LFC/EL exists. If not, skip.