def theta_plots(pressure, temperature, mixing_ratio):
"""
plots for vertical profiles of potential temperature, equivalent potential temperature,
and saturated equivalent potential temperature
"""
p = pressure * units('mbar')
T = temperature * units('degC')
q = mixing_ratio * units('kilogram/kilogram')
lev = find_nearest(p.magnitude, 100)
Td = mpcalc.dewpoint(mpcalc.vapor_pressure(p, q)) # dewpoint
theta = mpcalc.potential_temperature(p, T)
theta_e = mpcalc.equivalent_potential_temperature(p, T, Td)
theta_es = mpcalc.equivalent_potential_temperature(p, T, T)
plt.figure(figsize=(7, 7))
plt.plot(theta[:lev], p[:lev], '-ok')
plt.plot(theta_e[:lev], p[:lev], '-ob')
plt.plot(theta_es[:lev], p[:lev], '-or')
plt.xlabel('Temperature [K]', fontsize=12)
plt.ylabel('Pressure [hpa]', fontsize=12)
plt.gca().invert_yaxis()
plt.legend(['$\\theta$', '$\\theta_e$', '$\\theta_{es}$'], loc=1)
plt.grid()
return (plt)
def run_calcs(df, ctx):
"""Do our maths."""
# Convert sea level pressure to station pressure
df['pressure'] = mcalc.add_height_to_pressure(
df['slp'].values * units('millibars'),
ctx['nt'].sts[ctx['station']]['elevation'] * units('m')).to(
units('millibar'))
# Compute the mixing ratio
df['mixingratio'] = mcalc.mixing_ratio_from_relative_humidity(
df['relh'].values * units('percent'),
df['tmpf'].values * units('degF'),
df['pressure'].values * units('millibars'))
# Compute the saturation mixing ratio
df['saturation_mixingratio'] = mcalc.saturation_mixing_ratio(
df['pressure'].values * units('millibars'),
df['tmpf'].values * units('degF'))
df['vapor_pressure'] = mcalc.vapor_pressure(
df['pressure'].values * units('millibars'),
df['mixingratio'].values * units('kg/kg')).to(units('kPa'))
df['saturation_vapor_pressure'] = mcalc.vapor_pressure(
df['pressure'].values * units('millibars'),
df['saturation_mixingratio'].values * units('kg/kg')).to(units('kPa'))
df['vpd'] = df['saturation_vapor_pressure'] - df['vapor_pressure']
group = df.groupby('year')
df = group.aggregate(np.average)
df['dwpf'] = mcalc.dewpoint(df['vapor_pressure'].values * units('kPa')).to(
units('degF')).m
return df
def add_entropy(ax,
pressure,
temperature,
mixing_ratio,
ds=100,
linewidth=1.0):
"add entropy curves and rescale values to fit in by 0.5*entropy + ds"
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
# 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, q * 0, p)
sm1 = entropy(T, q, p)
sm2 = entropy(T, qs, p)
ax.plot(sd.magnitude * 0.5 + ds, p, '--k')
ax.plot(sm1.magnitude * 0.5 + ds, p, '--b')
ax.plot(sm2.magnitude * 0.5 + ds, p, '--r')
def run_calcs(df, ctx):
"""Do our maths."""
# Convert sea level pressure to station pressure
df["pressure"] = mcalc.add_height_to_pressure(
df["slp"].values * units("millibars"),
ctx["_nt"].sts[ctx["station"]]["elevation"] * units("m"),
).to(units("millibar"))
# Compute the mixing ratio
df["mixingratio"] = mcalc.mixing_ratio_from_relative_humidity(
df["relh"].values * units("percent"),
df["tmpf"].values * units("degF"),
df["pressure"].values * units("millibars"),
)
# Compute the saturation mixing ratio
df["saturation_mixingratio"] = mcalc.saturation_mixing_ratio(
df["pressure"].values * units("millibars"),
df["tmpf"].values * units("degF"),
)
df["vapor_pressure"] = mcalc.vapor_pressure(
df["pressure"].values * units("millibars"),
df["mixingratio"].values * units("kg/kg"),
).to(units("kPa"))
df["saturation_vapor_pressure"] = mcalc.vapor_pressure(
df["pressure"].values * units("millibars"),
df["saturation_mixingratio"].values * units("kg/kg"),
).to(units("kPa"))
df["vpd"] = df["saturation_vapor_pressure"] - df["vapor_pressure"]
# remove any NaN rows
df = df.dropna()
group = df.groupby("year")
df = group.aggregate(np.average)
df["dwpf"] = (mcalc.dewpoint(df["vapor_pressure"].values *
units("kPa")).to(units("degF")).m)
return df
def plot(variables, prev_vars, pltenv):
cont_int = 10
cont_smooth = 0.5
x = pltenv['x']
y = pltenv['y']
m = pltenv['map']
bbox = dict(boxstyle="square", ec='None', fc=(1, 1, 1, 0.75))
#var = (variables['T2'][0]-273.15) * 1.8 + 32
var = variables['Q2'][0] * 1000.
varp = variables['AFWA_MSLP'][0] * 0.01
var = var * units('g/kg')
vare = mcalc.vapor_pressure(varp * units.mbar, var)
vartd = mcalc.dewpoint(vare)
vartd = vartd.to('degF')
var2 = ndimage.gaussian_filter(vartd, sigma=cont_smooth)
levels = np.arange(-100, 150, cont_int)
levels2 = np.arange(-40, 140, 1)
P = m.contour(x, y, var2, levels=levels, colors='k')
plt.clabel(P, inline=1, fontsize=10, fmt='%1.0f', inline_spacing=1)
P = m.contour(x, y, var2, levels=[32], colors='r')
plt.clabel(P, inline=1, fontsize=10, fmt='%1.0f', inline_spacing=1)
m.contourf(x, y, vartd, cmap='gist_ncar', levels=levels2, extend='both')
def convert_H2O_MR_to_Td(H2O_MR, p):
# input water vapour mass mixing ration in (mWV / mDA) kg/kg
WVMR = H2O_MR * 1000 # convert to grams
WVMR = WVMR * units('g/kg')
e_1 = mpcalc.vapor_pressure(p_NUCAPS_orig, WVMR)
T_d = mpcalc.dewpoint(e_1)
return T_d
def test_dewpoint_weird_units():
"""Test dewpoint using non-standard units.
Revealed from odd dimensionless units and ending up using numpy.ma math
functions instead of numpy ones.
"""
assert_almost_equal(dewpoint(15825.6 * units('g * mbar / kg')),
13.8564 * units.degC, 4)
def test_dewpoint_weird_units():
"""Test dewpoint using non-standard units.
Revealed from odd dimensionless units and ending up using numpy.ma math
functions instead of numpy ones.
"""
assert_almost_equal(dewpoint(15825.6 * units('g * mbar / kg')),
13.8564 * units.degC, 4)
def add_curves(ax,
pressure,
temperature,
mixing_ratio,
altitude,
linewidth=1.0,
LH_Tdepend=False):
"""
overlaying new curves of multiple soundings from profiles
"""
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 energies
if LH_Tdepend == False:
mse = mpcalc.moist_static_energy(altitude, T, q)
mse_s = mpcalc.moist_static_energy(altitude, T, qs)
dse = mpcalc.dry_static_energy(altitude, T)
else:
# A short course in cloud physics, Roger and Yau (1989)
Lvt = (2500.8 - 2.36 * T.magnitude +
0.0016 * T.magnitude**2 - 0.00006 * T.magnitude**3) * units(
'joule/gram') # latent heat of evaporation
#Lf = 2834.1 - 0.29*T - 0.004*T**2 # latent heat of fusion
mse = Cp_d * T + g * altitude + Lvt * q
mse_s = Cp_d * T + g * altitude + Lvt * qs
dse = mpcalc.dry_static_energy(altitude, T)
ax.plot(dse, p, '--k', linewidth=linewidth)
ax.plot(mse, p, '--b', linewidth=linewidth)
ax.plot(mse_s, p, '--r', linewidth=linewidth)
def down_cape(p_start=None):
if p_start not in ls.lev:
raise ValueError(
"Please provide pressure of one level of large-scale dataset to start calculating DCAPE from."
)
# find index of p_start
start_level = int((abs(ls.lev - p_start)).argmin())
# get temperature and humidity from large-scale state
temp = ls.T.sel(lev=slice(None, 990)).metpy.unit_array
mix = ls.r.sel(lev=slice(None, 990)).metpy.unit_array.to('kg/kg')
p_vector = ls.lev.sel(lev=slice(None, 990)).metpy.unit_array
# get dew-point temperature
vap_pres = mpcalc.vapor_pressure(p_vector, mix)
dew_temp = mpcalc.dewpoint(vap_pres)
# pressure levels to integrate over
p_down = ls.lev.sel(lev=slice(p_start, 990))
# find NaNs
l_valid = ls.T[:, start_level].notnull().values
d_cape = xr.full_like(ls.cape, np.nan)
x = p_down.values
temp = temp[l_valid, :]
dew_temp = dew_temp[l_valid, :]
# loop over all non-NaN times in large-scale state
for i, this_time in enumerate(ls.T[l_valid].time):
print(i)
# bug: p_start has to be multiplied with units when given as argument, not beforehand
wb_temp = mpcalc.wet_bulb_temperature(p_start * units['hPa'],
temp[i, start_level],
dew_temp[i, start_level])
# create placeholder for moist adiabat temperature
moist_adiabat = temp[i, start_level:].to('degC')
moist_adiabat_below = mpcalc.moist_lapse(p_vector[start_level + 1:],
wb_temp,
p_start * units['hPa'])
moist_adiabat[0] = wb_temp
moist_adiabat[1:] = moist_adiabat_below
env_temp = temp[i, start_level:]
temp_diff = moist_adiabat - env_temp
y = temp_diff.magnitude
d_cape.loc[this_time] = (mpconsts.Rd *
(np.trapz(y, np.log(x)) * units.degK)).to(
units('J/kg'))
d_cape.attrs['long_name'] = 'Downward CAPE'
return xr.merge([ls, xr.Dataset({'d_cape': d_cape})])
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 main():
img_dir = Path("hail_plots/soundings/")
if not img_dir.exists():
img_dir.mkdir(parents=True)
data_dir = Path("/HOME/huziy/skynet3_rech1/hail/soundings_from_erai/")
# dates = [datetime(1991, 9, 7), datetime(1991, 9, 7, 6), datetime(1991, 9, 7, 12), datetime(1991, 9, 7, 18),
# datetime(1991, 9, 8, 0), datetime(1991, 9, 8, 18)]
#
# dates.extend([datetime(1991, 9, 6, 0), datetime(1991, 9, 6, 6), datetime(1991, 9, 6, 12), datetime(1991, 9, 6, 18)])
#
# dates = [datetime(1990, 7, 7), datetime(2010, 7, 12), datetime(1991, 9, 8, 0)]
dates_s = """
- 07/09/1991 12:00
- 07/09/1991 18:00
- 08/09/1991 00:00
- 08/09/1991 06:00
- 08/09/1991 12:00
- 13/09/1991 12:00
- 13/09/1991 18:00
- 14/09/1991 00:00
- 14/09/1991 06:00
- 14/09/1991 12:00
"""
dates = [
datetime.strptime(line.strip()[1:].strip(), "%d/%m/%Y %H:%M")
for line in dates_s.split("\n") if line.strip() != ""
]
def __date_parser(s):
return pd.datetime.strptime(s, '%Y-%m-%d %H:%M:%S')
tt = pd.read_csv(data_dir.joinpath("TT.csv"),
index_col=0,
parse_dates=['Time'])
uu = pd.read_csv(data_dir.joinpath("UU.csv"),
index_col=0,
parse_dates=['Time'])
vv = pd.read_csv(data_dir.joinpath("VV.csv"),
index_col=0,
parse_dates=['Time'])
hu = pd.read_csv(data_dir.joinpath("HU.csv"),
index_col=0,
parse_dates=['Time'])
print(tt.head())
print([c for c in tt])
print(list(tt.columns.values))
temp_perturbation_degc = 0
for the_date in dates:
p = np.array([float(c) for c in tt])
fig = plt.figure(figsize=(9, 9))
skew = SkewT(fig)
skew.ax.set_ylim(1000, 100)
skew.ax.set_xlim(-40, 60)
tsel = tt.select(lambda d: d == the_date)
usel = uu.select(lambda d: d == the_date)
vsel = vv.select(lambda d: d == the_date)
husel = hu.select(lambda d: d == the_date)
tvals = tsel.values.mean(axis=0)
uvals = usel.values.mean(axis=0) * mul_mpers_per_knot
vvals = vsel.values.mean(axis=0) * mul_mpers_per_knot
huvals = husel.values.mean(axis=0) * units("g/kg")
# ignore the lowest level
all_vars = [p, tvals, uvals, vvals, huvals]
for i in range(len(all_vars)):
all_vars[i] = all_vars[i][:-5]
p, tvals, uvals, vvals, huvals = all_vars
assert len(p) == len(huvals)
tdvals = calc.dewpoint(
calc.vapor_pressure(p * units.mbar, huvals).to(units.mbar))
print(tvals, tdvals)
# Calculate full parcel profile and add to plot as black line
parcel_profile = calc.parcel_profile(
p[::-1] * units.mbar,
(tvals[-1] + temp_perturbation_degc) * units.degC,
tdvals[-1]).to('degC')
parcel_profile = parcel_profile[::-1]
skew.plot(p, parcel_profile, 'k', linewidth=2)
# Example of coloring area between profiles
greater = tvals * units.degC >= parcel_profile
skew.ax.fill_betweenx(p,
tvals,
parcel_profile,
where=greater,
facecolor='blue',
alpha=0.4)
skew.ax.fill_betweenx(p,
tvals,
parcel_profile,
where=~greater,
facecolor='red',
alpha=0.4)
skew.plot(p, tvals, "r")
skew.plot(p, tdvals, "g")
skew.plot_barbs(p, uvals, vvals)
# Plot a zero degree isotherm
l = skew.ax.axvline(0, color='c', linestyle='--', linewidth=2)
# Add the relevant special lines
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
plt.title("{} (dT={})".format(the_date, temp_perturbation_degc))
img_path = "{}_dT={}.png".format(the_date.strftime("%Y%m%d_%H%M%S"),
temp_perturbation_degc)
img_path = img_dir.joinpath(img_path)
fig.savefig(str(img_path), bbox_inches="tight")
plt.close(fig)
from metpy.vis import meteogram
from metpy.constants import C2F
from metpy.calc import dewpoint, windchill
from metpy.cbook import rec_append_fields
fields = ('stid', 'time', 'relh', 'tair', 'wspd', 'wmax', 'wdir', 'pres',
'srad', 'rain')
data = read_mesonet_data('data/20080117nrmn.mts', fields, lookup_stids=False)
#Add a reasonable time range if we're doing current data
end = get_last_time(data)
times = (end - datetime.timedelta(hours=24), end)
#Calculate dewpoint in F from relative humidity and temperature
dewpt = C2F(dewpoint(data['TAIR'], data['RELH'] / 100.))
data = rec_append_fields(data, ('dewpoint', ), (dewpt, ))
#Convert temperature and dewpoint to Farenheit
mod_units = mesonet_units.copy()
mod_units['TAIR'] = 'F'
mod_units['dewpoint'] = 'F'
data['TAIR'] = C2F(data['TAIR'])
#Convert wind speeds to MPH
data['WSPD'] *= sconsts.hour / sconsts.mile
data['WMAX'] *= sconsts.hour / sconsts.mile
mod_units['WSPD'] = 'MPH'
mod_units['WMAX'] = 'MPH'
#Convert rainfall to inches
def main():
img_dir = Path("hail_plots/soundings/")
if not img_dir.exists():
img_dir.mkdir(parents=True)
data_dir = Path("/HOME/huziy/skynet3_rech1/hail/soundings_from_erai/")
# dates = [datetime(1991, 9, 7), datetime(1991, 9, 7, 6), datetime(1991, 9, 7, 12), datetime(1991, 9, 7, 18),
# datetime(1991, 9, 8, 0), datetime(1991, 9, 8, 18)]
#
# dates.extend([datetime(1991, 9, 6, 0), datetime(1991, 9, 6, 6), datetime(1991, 9, 6, 12), datetime(1991, 9, 6, 18)])
#
# dates = [datetime(1990, 7, 7), datetime(2010, 7, 12), datetime(1991, 9, 8, 0)]
dates_s = """
- 07/09/1991 12:00
- 07/09/1991 18:00
- 08/09/1991 00:00
- 08/09/1991 06:00
- 08/09/1991 12:00
- 13/09/1991 12:00
- 13/09/1991 18:00
- 14/09/1991 00:00
- 14/09/1991 06:00
- 14/09/1991 12:00
"""
dates = [datetime.strptime(line.strip()[1:].strip(), "%d/%m/%Y %H:%M") for line in dates_s.split("\n") if line.strip() != ""]
def __date_parser(s):
return pd.datetime.strptime(s, '%Y-%m-%d %H:%M:%S')
tt = pd.read_csv(data_dir.joinpath("TT.csv"), index_col=0, parse_dates=['Time'])
uu = pd.read_csv(data_dir.joinpath("UU.csv"), index_col=0, parse_dates=['Time'])
vv = pd.read_csv(data_dir.joinpath("VV.csv"), index_col=0, parse_dates=['Time'])
hu = pd.read_csv(data_dir.joinpath("HU.csv"), index_col=0, parse_dates=['Time'])
print(tt.head())
print([c for c in tt])
print(list(tt.columns.values))
temp_perturbation_degc = 0
for the_date in dates:
p = np.array([float(c) for c in tt])
fig = plt.figure(figsize=(9, 9))
skew = SkewT(fig)
skew.ax.set_ylim(1000, 100)
skew.ax.set_xlim(-40, 60)
tsel = tt.select(lambda d: d == the_date)
usel = uu.select(lambda d: d == the_date)
vsel = vv.select(lambda d: d == the_date)
husel = hu.select(lambda d: d == the_date)
tvals = tsel.values.mean(axis=0)
uvals = usel.values.mean(axis=0) * mul_mpers_per_knot
vvals = vsel.values.mean(axis=0) * mul_mpers_per_knot
huvals = husel.values.mean(axis=0) * units("g/kg")
# ignore the lowest level
all_vars = [p, tvals, uvals, vvals, huvals]
for i in range(len(all_vars)):
all_vars[i] = all_vars[i][:-5]
p, tvals, uvals, vvals, huvals = all_vars
assert len(p) == len(huvals)
tdvals = calc.dewpoint(calc.vapor_pressure(p * units.mbar, huvals).to(units.mbar))
print(tvals, tdvals)
# Calculate full parcel profile and add to plot as black line
parcel_profile = calc.parcel_profile(p[::-1] * units.mbar, (tvals[-1] + temp_perturbation_degc) * units.degC, tdvals[-1]).to('degC')
parcel_profile = parcel_profile[::-1]
skew.plot(p, parcel_profile, 'k', linewidth=2)
# Example of coloring area between profiles
greater = tvals * units.degC >= parcel_profile
skew.ax.fill_betweenx(p, tvals, parcel_profile, where=greater, facecolor='blue', alpha=0.4)
skew.ax.fill_betweenx(p, tvals, parcel_profile, where=~greater, facecolor='red', alpha=0.4)
skew.plot(p, tvals, "r")
skew.plot(p, tdvals, "g")
skew.plot_barbs(p, uvals, vvals)
# Plot a zero degree isotherm
l = skew.ax.axvline(0, color='c', linestyle='--', linewidth=2)
# Add the relevant special lines
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
plt.title("{} (dT={})".format(the_date, temp_perturbation_degc))
img_path = "{}_dT={}.png".format(the_date.strftime("%Y%m%d_%H%M%S"), temp_perturbation_degc)
img_path = img_dir.joinpath(img_path)
fig.savefig(str(img_path), bbox_inches="tight")
plt.close(fig)
mixing = 10 * units('g/kg')
print(mixing)
###########################################
# Now throw that value with units into the function to calculate
# the corresponding vapor pressure, given a surface pressure of 1000 mb
e = mcalc.vapor_pressure(1000. * units.mbar, mixing)
print(e)
###########################################
# Take the odd units and force them to millibars
print(e.to(units.mbar))
###########################################
# Take the raw vapor pressure and throw into the dewpoint function
td = mcalc.dewpoint(e)
print(td)
###########################################
# Which can of course be converted to Fahrenheit
print(td.to('degF'))
###########################################
# Now do the same thing for 850 mb, approximately the pressure of Denver
e = mcalc.vapor_pressure(850. * units.mbar, mixing)
print(e.to(units.mbar))
###########################################
# And print the corresponding dewpoint
td = mcalc.dewpoint(e)
print(td, td.to('degF'))
def plotter(fdict):
""" Go """
pgconn = get_dbconn('asos')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['zstation']
network = ctx['network']
month = ctx['month']
nt = NetworkTable(network)
if month == 'all':
months = range(1, 13)
elif month == 'fall':
months = [9, 10, 11]
elif month == 'winter':
months = [12, 1, 2]
elif month == 'spring':
months = [3, 4, 5]
elif month == 'summer':
months = [6, 7, 8]
else:
ts = datetime.datetime.strptime("2000-"+month+"-01", '%Y-%b-%d')
# make sure it is length two for the trick below in SQL
months = [ts.month, 999]
df = read_sql("""
SELECT tmpf::int as tmpf, dwpf,
coalesce(mslp, alti * 33.8639, 1013.25) as slp
from alldata where station = %s
and drct is not null and dwpf is not null and dwpf <= tmpf
and sknt > 3 and drct::int %% 10 = 0
and extract(month from valid) in %s
and report_type = 2
""", pgconn, params=(station, tuple(months)))
# Convert sea level pressure to station pressure
df['pressure'] = mcalc.add_height_to_pressure(
df['slp'].values * units('millibars'),
nt.sts[station]['elevation'] * units('m')
).to(units('millibar'))
# compute RH
df['relh'] = mcalc.relative_humidity_from_dewpoint(
df['tmpf'].values * units('degF'),
df['dwpf'].values * units('degF')
)
# compute mixing ratio
df['mixingratio'] = mcalc.mixing_ratio_from_relative_humidity(
df['relh'].values,
df['tmpf'].values * units('degF'),
df['pressure'].values * units('millibars')
)
# compute pressure
df['vapor_pressure'] = mcalc.vapor_pressure(
df['pressure'].values * units('millibars'),
df['mixingratio'].values * units('kg/kg')
).to(units('kPa'))
means = df.groupby('tmpf').mean().copy()
# compute dewpoint now
means['dwpf'] = mcalc.dewpoint(
means['vapor_pressure'].values * units('kPa')
).to(units('degF')).m
means.reset_index(inplace=True)
# compute RH again
means['relh'] = mcalc.relative_humidity_from_dewpoint(
means['tmpf'].values * units('degF'),
means['dwpf'].values * units('degF')
) * 100.
(fig, ax) = plt.subplots(1, 1, figsize=(8, 6))
ax.bar(
means['tmpf'].values - 0.5, means['dwpf'].values - 0.5,
ec='green', fc='green', width=1
)
ax.grid(True, zorder=11)
ax.set_title(("%s [%s]\nAverage Dew Point by Air Temperature (month=%s) "
"(%s-%s)\n"
"(must have 3+ hourly observations at the given temperature)"
) % (nt.sts[station]['name'], station, month.upper(),
nt.sts[station]['archive_begin'].year,
datetime.datetime.now().year), size=10)
ax.plot([0, 140], [0, 140], color='b')
ax.set_ylabel("Dew Point [F]")
y2 = ax.twinx()
y2.plot(means['tmpf'].values, means['relh'].values, color='k')
y2.set_ylabel("Relative Humidity [%] (black line)")
y2.set_yticks([0, 5, 10, 25, 50, 75, 90, 95, 100])
y2.set_ylim(0, 100)
ax.set_ylim(0, means['tmpf'].max() + 2)
ax.set_xlim(0, means['tmpf'].max() + 2)
ax.set_xlabel(r"Air Temperature $^\circ$F")
return fig, means[['tmpf', 'dwpf', 'relh']]
def plotter(fdict):
""" Go """
pgconn = get_dbconn('asos')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['zstation']
network = ctx['network']
month = ctx['month']
nt = NetworkTable(network)
if month == 'all':
months = range(1, 13)
elif month == 'fall':
months = [9, 10, 11]
elif month == 'winter':
months = [12, 1, 2]
elif month == 'spring':
months = [3, 4, 5]
elif month == 'summer':
months = [6, 7, 8]
else:
ts = datetime.datetime.strptime("2000-" + month + "-01", '%Y-%b-%d')
# make sure it is length two for the trick below in SQL
months = [ts.month, 999]
df = read_sql("""
SELECT drct::int as t, dwpf, tmpf, relh,
coalesce(mslp, alti * 33.8639, 1013.25) as slp
from alldata where station = %s
and drct is not null and dwpf is not null and dwpf <= tmpf
and sknt > 3 and drct::int %% 10 = 0
and extract(month from valid) in %s
and report_type = 2
""",
pgconn,
params=(station, tuple(months)))
# Convert sea level pressure to station pressure
df['pressure'] = mcalc.add_height_to_pressure(
df['slp'].values * units('millibars'),
nt.sts[station]['elevation'] * units('m')).to(units('millibar'))
# compute mixing ratio
df['mixingratio'] = mcalc.mixing_ratio_from_relative_humidity(
df['relh'].values * units('percent'),
df['tmpf'].values * units('degF'),
df['pressure'].values * units('millibars'))
# compute pressure
df['vapor_pressure'] = mcalc.vapor_pressure(
df['pressure'].values * units('millibars'),
df['mixingratio'].values * units('kg/kg')).to(units('kPa'))
means = df.groupby('t').mean().copy()
# compute dewpoint now
means['dwpf'] = mcalc.dewpoint(means['vapor_pressure'].values *
units('kPa')).to(units('degF')).m
(fig, ax) = plt.subplots(1, 1)
ax.bar(means.index.values,
means['dwpf'].values,
ec='green',
fc='green',
width=10,
align='center')
ax.grid(True, zorder=11)
ax.set_title(("%s [%s]\nAverage Dew Point by Wind Direction (month=%s) "
"(%s-%s)\n"
"(must have 3+ hourly obs > 3 knots at given direction)") %
(nt.sts[station]['name'], station, month.upper(),
max([1973, nt.sts[station]['archive_begin'].year
]), datetime.datetime.now().year),
size=10)
ax.set_ylabel("Dew Point [F]")
ax.set_ylim(means['dwpf'].min() - 5, means['dwpf'].max() + 5)
ax.set_xlim(-5, 365)
ax.set_xticks([0, 45, 90, 135, 180, 225, 270, 315, 360])
ax.set_xticklabels(['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW', 'N'])
ax.set_xlabel("Wind Direction")
return fig, means['dwpf']
def plotter(fdict):
""" Go """
pgconn = get_dbconn("asos")
ctx = get_autoplot_context(fdict, get_description())
station = ctx["zstation"]
month = ctx["month"]
if month == "all":
months = range(1, 13)
elif month == "fall":
months = [9, 10, 11]
elif month == "winter":
months = [12, 1, 2]
elif month == "spring":
months = [3, 4, 5]
elif month == "summer":
months = [6, 7, 8]
else:
ts = datetime.datetime.strptime("2000-" + month + "-01", "%Y-%b-%d")
# make sure it is length two for the trick below in SQL
months = [ts.month, 999]
df = read_sql(
"""
SELECT drct::int as t, dwpf, tmpf, relh,
coalesce(mslp, alti * 33.8639, 1013.25) as slp
from alldata where station = %s
and drct is not null and dwpf is not null and dwpf <= tmpf
and sknt > 3 and drct::int %% 10 = 0
and extract(month from valid) in %s
and report_type = 2
""",
pgconn,
params=(station, tuple(months)),
)
if df.empty:
raise NoDataFound("No Data Found.")
# Convert sea level pressure to station pressure
df["pressure"] = mcalc.add_height_to_pressure(
df["slp"].values * units("millibars"),
ctx["_nt"].sts[station]["elevation"] * units("m"),
).to(units("millibar"))
# compute mixing ratio
df["mixingratio"] = mcalc.mixing_ratio_from_relative_humidity(
df["relh"].values * units("percent"),
df["tmpf"].values * units("degF"),
df["pressure"].values * units("millibars"),
)
# compute pressure
df["vapor_pressure"] = mcalc.vapor_pressure(
df["pressure"].values * units("millibars"),
df["mixingratio"].values * units("kg/kg"),
).to(units("kPa"))
means = df.groupby("t").mean().copy()
# compute dewpoint now
means["dwpf"] = (mcalc.dewpoint(means["vapor_pressure"].values *
units("kPa")).to(units("degF")).m)
(fig, ax) = plt.subplots(1, 1)
ax.bar(
means.index.values,
means["dwpf"].values,
ec="green",
fc="green",
width=10,
align="center",
)
ax.grid(True, zorder=11)
ab = ctx["_nt"].sts[station]["archive_begin"]
if ab is None:
raise NoDataFound("Unknown station metadata.")
ax.set_title(
("%s [%s]\nAverage Dew Point by Wind Direction (month=%s) "
"(%s-%s)\n"
"(must have 3+ hourly obs > 3 knots at given direction)") % (
ctx["_nt"].sts[station]["name"],
station,
month.upper(),
max([1973, ab.year]),
datetime.datetime.now().year,
),
size=10,
)
ax.set_ylabel("Dew Point [F]")
ax.set_ylim(means["dwpf"].min() - 5, means["dwpf"].max() + 5)
ax.set_xlim(-5, 365)
ax.set_xticks([0, 45, 90, 135, 180, 225, 270, 315, 360])
ax.set_xticklabels(["N", "NE", "E", "SE", "S", "SW", "W", "NW", "N"])
ax.set_xlabel("Wind Direction")
return fig, means["dwpf"]
def dewpoint_from_relative_humidity(temperature, relative_humidity):
"""Similar to `metpy.calc.dewpoint_from_relative_humidity`, but without
a check for values greater than 120%"""
return mpcalc.dewpoint(relative_humidity *
mpcalc.saturation_vapor_pressure(temperature))
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 plotter(fdict):
""" Go """
pgconn = get_dbconn("asos")
ctx = get_autoplot_context(fdict, get_description())
station = ctx["zstation"]
month = ctx["month"]
if month == "all":
months = range(1, 13)
elif month == "fall":
months = [9, 10, 11]
elif month == "winter":
months = [12, 1, 2]
elif month == "spring":
months = [3, 4, 5]
elif month == "summer":
months = [6, 7, 8]
else:
ts = datetime.datetime.strptime("2000-" + month + "-01", "%Y-%b-%d")
# make sure it is length two for the trick below in SQL
months = [ts.month, 999]
df = read_sql(
"""
SELECT tmpf::int as tmpf, dwpf, relh,
coalesce(mslp, alti * 33.8639, 1013.25) as slp
from alldata where station = %s
and drct is not null and dwpf is not null and dwpf <= tmpf
and sknt > 3 and drct::int %% 10 = 0
and extract(month from valid) in %s
and report_type = 2
""",
pgconn,
params=(station, tuple(months)),
)
if df.empty:
raise NoDataFound("No Data Found.")
# Convert sea level pressure to station pressure
df["pressure"] = mcalc.add_height_to_pressure(
df["slp"].values * units("millibars"),
ctx["_nt"].sts[station]["elevation"] * units("m"),
).to(units("millibar"))
# compute mixing ratio
df["mixingratio"] = mcalc.mixing_ratio_from_relative_humidity(
df["relh"].values * units("percent"),
df["tmpf"].values * units("degF"),
df["pressure"].values * units("millibars"),
)
# compute pressure
df["vapor_pressure"] = mcalc.vapor_pressure(
df["pressure"].values * units("millibars"),
df["mixingratio"].values * units("kg/kg"),
).to(units("kPa"))
means = df.groupby("tmpf").mean().copy()
# compute dewpoint now
means["dwpf"] = (
mcalc.dewpoint(means["vapor_pressure"].values * units("kPa"))
.to(units("degF"))
.m
)
means.reset_index(inplace=True)
# compute RH again
means["relh"] = (
mcalc.relative_humidity_from_dewpoint(
means["tmpf"].values * units("degF"),
means["dwpf"].values * units("degF"),
)
* 100.0
)
(fig, ax) = plt.subplots(1, 1, figsize=(8, 6))
ax.bar(
means["tmpf"].values - 0.5,
means["dwpf"].values - 0.5,
ec="green",
fc="green",
width=1,
)
ax.grid(True, zorder=11)
ab = ctx["_nt"].sts[station]["archive_begin"]
if ab is None:
raise NoDataFound("Unknown station metadata.")
ax.set_title(
(
"%s [%s]\nAverage Dew Point by Air Temperature (month=%s) "
"(%s-%s)\n"
"(must have 3+ hourly observations at the given temperature)"
)
% (
ctx["_nt"].sts[station]["name"],
station,
month.upper(),
ab.year,
datetime.datetime.now().year,
),
size=10,
)
ax.plot([0, 140], [0, 140], color="b")
ax.set_ylabel("Dew Point [F]")
y2 = ax.twinx()
y2.plot(means["tmpf"].values, means["relh"].values, color="k")
y2.set_ylabel("Relative Humidity [%] (black line)")
y2.set_yticks([0, 5, 10, 25, 50, 75, 90, 95, 100])
y2.set_ylim(0, 100)
ax.set_ylim(0, means["tmpf"].max() + 2)
ax.set_xlim(0, means["tmpf"].max() + 2)
ax.set_xlabel(r"Air Temperature $^\circ$F")
return fig, means[["tmpf", "dwpf", "relh"]]
def test_dewpoint():
"""Test dewpoint calculation."""
assert_almost_equal(dewpoint(6.112 * units.mbar), 0. * units.degC, 2)
def test_dewpoint():
"""Test dewpoint calculation."""
assert_almost_equal(dewpoint(6.112 * units.mbar), 0. * units.degC, 2)
T_NUCAPS = NUCAPS_data.Temperature.values - 273.15
T_NUCAPS = pd.DataFrame({
'temperature_degC':
np.reshape(T_NUCAPS, (T_NUCAPS.shape[0] * T_NUCAPS.shape[1]))
})
T_NUCAPS_1 = (NUCAPS_data.Temperature.values - 273.15) * units.degC
H2O_MR = np.reshape(NUCAPS_data['H2O_MR'].values,
(T_NUCAPS.shape[0] * T_NUCAPS.shape[1])) * units('g/kg')
H2O_MR_1 = NUCAPS_data['H2O_MR'].values
H2O_MR = pd.DataFrame({'mixing_ratio': H2O_MR})
p_NUCAPS_2 = NUCAPS_data.Pressure.values * units.hPa
WVMR = (H2O_MR_1 * 1000) * units('g/kg')
e_1 = mpcalc.vapor_pressure(p_NUCAPS_2, WVMR)
T_d = mpcalc.dewpoint(e_1)
T_d_NUCAPS = pd.DataFrame({
'dew_point_degC':
np.reshape(T_d, (T_NUCAPS.shape[0] * T_NUCAPS.shape[1]))
})
datetime_NUCAPS = NUCAPS_data.datetime.values
datetime_NUCAPS = pd.DataFrame(
{'time_YMDHMS_1': (np.repeat(datetime_NUCAPS, 100))})
# round datime to next RS time (0000 or 1200)
datetime_NUCAPS_round = pd.DataFrame(
datetime_NUCAPS.time_YMDHMS_1.dt.ceil('60 min').values,
columns=['time_YMDHMS'])
datetime_NUCAPS_round.time_YMDHMS[
datetime_NUCAPS_round.time_YMDHMS.dt.hour ==
print('Time length: ', len(time))
# From dimension to variable.
press = np.zeros_like(temp)
for cnt in range(temp.shape[0]):
press[cnt, :] = lev
pressure = press * units.hPa
temperature = temp * units.K
mixing_ratio = wvmr * units('kg/kg')
# get dew point
relative_humidity = mpcalc.relative_humidity_from_mixing_ratio(
mixing_ratio, temperature, pressure)
e = mpcalc.vapor_pressure(pressure, mixing_ratio)
dew_point = mpcalc.dewpoint(e)
def get_cape(inargs, return_parcel_profile=False):
pres_prof, temp_prof, dp_prof = inargs
try:
prof = mpcalc.parcel_profile(pres_prof, temp_prof[0], dp_prof[0])
cape, cin = mpcalc.cape_cin(pres_prof, temp_prof, dp_prof, prof)
except Exception:
cape, cin, prof = np.NaN, np.NaN, np.NaN
print('Problem during CAPE-calculation. Likely NaN-related.')
if return_parcel_profile:
return cape, cin, prof
else:
return cape, cin
mixing = 10 * units('g/kg')
print(mixing)
###########################################
# Now throw that value with units into the function to calculate
# the corresponding vapor pressure, given a surface pressure of 1000 mb
e = mpcalc.vapor_pressure(1000. * units.mbar, mixing)
print(e)
###########################################
# Take the odd units and force them to millibars
print(e.to(units.mbar))
###########################################
# Take the raw vapor pressure and throw into the dewpoint function
td = mpcalc.dewpoint(e)
print(td)
###########################################
# Which can of course be converted to Fahrenheit
print(td.to('degF'))
###########################################
# Now do the same thing for 850 mb, approximately the pressure of Denver
e = mpcalc.vapor_pressure(850. * units.mbar, mixing)
print(e.to(units.mbar))
###########################################
# And print the corresponding dewpoint
td = mpcalc.dewpoint(e)
print(td, td.to('degF'))
from metpy.vis import meteogram
from metpy.constants import C2F
from metpy.calc import dewpoint, windchill
from metpy.cbook import rec_append_fields
fields = ('stid', 'time', 'relh', 'tair', 'wspd', 'wmax', 'wdir', 'pres',
'srad', 'rain')
data = read_mesonet_data('data/20080117nrmn.mts', fields, lookup_stids=False)
#Add a reasonable time range if we're doing current data
end = get_last_time(data)
times = (end - datetime.timedelta(hours=24), end)
#Calculate dewpoint in F from relative humidity and temperature
dewpt = C2F(dewpoint(data['TAIR'], data['RELH']/100.))
data = rec_append_fields(data, ('dewpoint',), (dewpt,))
#Convert temperature and dewpoint to Farenheit
mod_units = mesonet_units.copy()
mod_units['TAIR'] = 'F'
mod_units['dewpoint'] = 'F'
data['TAIR'] = C2F(data['TAIR'])
#Convert wind speeds to MPH
data['WSPD'] *= sconsts.hour / sconsts.mile
data['WMAX'] *= sconsts.hour / sconsts.mile
mod_units['WSPD'] = 'MPH'
mod_units['WMAX'] = 'MPH'
#Convert rainfall to inches
mixing = 10 * units('g/kg')
print(mixing)
###########################################
# Now throw that value with units into the function to calculate
# the corresponding vapor pressure, given a surface pressure of 1000 mb
e = mpcalc.vapor_pressure(1000. * units.mbar, mixing)
print(e)
###########################################
# Take the odd units and force them to millibars
print(e.to(units.mbar))
###########################################
# Take the raw vapor pressure and throw into the dewpoint function
td = mpcalc.dewpoint(e)
print(td)
###########################################
# Which can of course be converted to Fahrenheit
print(td.to('degF'))
###########################################
# Now do the same thing for 850 mb, approximately the pressure of Denver
e = mpcalc.vapor_pressure(850. * units.mbar, mixing)
print(e.to(units.mbar))
###########################################
# And print the corresponding dewpoint
td = mpcalc.dewpoint(e)
print(td, td.to('degF'))
