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 main():
"""Go Main Go"""
pgconn = get_dbconn('scan')
for station in ['S2004', 'S2196', 'S2002', 'S2072', 'S2068',
'S2031', 'S2001', 'S2047']:
df = read_sql("""
select extract(year from valid + '2 months'::interval) as wy,
tmpf, dwpf from alldata where station = %s and tmpf is not null
and dwpf is not null
""", pgconn, params=(station, ), index_col=None)
df['mixingratio'] = meteorology.mixing_ratio(
temperature(df['dwpf'].values, 'F')).value('KG/KG')
df['vapor_pressure'] = mcalc.vapor_pressure(
1000. * units.mbar,
df['mixingratio'].values * units('kg/kg')).to(units('kPa'))
df['saturation_mixingratio'] = (
meteorology.mixing_ratio(
temperature(df['tmpf'].values, 'F')).value('KG/KG'))
df['saturation_vapor_pressure'] = mcalc.vapor_pressure(
1000. * units.mbar,
df['saturation_mixingratio'].values * units('kg/kg')).to(units('kPa'))
df['vpd'] = df['saturation_vapor_pressure'] - df['vapor_pressure']
means = df.groupby('wy').mean()
counts = df.groupby('wy').count()
for yr, row in means.iterrows():
print(("%s,%s,%.0f,%.3f"
) % (yr, station, counts.at[yr, 'vpd'], row['vpd']))
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 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 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 mixingratio_to_relativehumidity(pressure, temperature, mixing_ratio):
actual_vapour_pressure = mpcalc.vapor_pressure(
pressure * units['hPa'], mixing_ratio * units['g/kg']).to_base_units()
sat_vapour_pressure = mpcalc.saturation_vapor_pressure(
temperature * units['degC']).to_base_units()
rh = actual_vapour_pressure / sat_vapour_pressure
return rh
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 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 run_calcs(df):
"""Do our maths"""
df['mixingratio'] = meteorology.mixing_ratio(
temperature(df['dwpf'].values, 'F')).value('KG/KG')
df['vapor_pressure'] = mcalc.vapor_pressure(
1000. * units.mbar,
df['mixingratio'].values * units('kg/kg')).to(units('kPa'))
df['saturation_mixingratio'] = (meteorology.mixing_ratio(
temperature(df['tmpf'].values, 'F')).value('KG/KG'))
df['saturation_vapor_pressure'] = mcalc.vapor_pressure(
1000. * units.mbar,
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'] = meteorology.dewpoint_from_pq(
pressure(1000, 'MB'), mixingratio(df['mixingratio'].values,
'KG/KG')).value('F')
return df
def plot(T, p, r, f_in, dir_out):
T = T * units.K
p = p * units.millibar
vp = mpcalc.vapor_pressure(p, r)
svp = mpcalc.saturation_vapor_pressure(T)
Td = mpcalc.dewpoint_from_relative_humidity(T, vp / svp)
Td[np.isnan(
Td)] = -99. * units.degree_Celsius # fill nan with very low temp
f_in_basename = os.path.basename(f_in)
prof.core(p,
T,
Td,
u,
v,
saveto=dir_out + 'prof_' + f_in_basename + '.png',
title=f_in_basename)
def plot_profile(f, fig, skew):
ds = xr.open_dataset(f)
ds = ds.isel(Time=0)
Theta_m = ds.T.mean(areadims) + basetemp
pm = (ds.P + ds.PB).mean(areadims)
Qvm = (ds.QVAPOR).mean(areadims)
U = wrf.destagger(ds.U, 2, meta=True)
V = wrf.destagger(ds.V, 1, meta=True)
um = U.mean(areadims)
vm = V.mean(areadims)
Tm = Theta_m * (pm / 1e5)**(2 / 7)
p = pm.values / 100. * units.millibar
Tm = Tm.values * units.K
Qv = Qvm.values
vp = mpcalc.vapor_pressure(p, Qv)
svp = mpcalc.saturation_vapor_pressure(Tm)
#svp[svp<1e-16*units.millibar] = 1e-16 * units.millibar
Td = mpcalc.dewpoint_from_relative_humidity(Tm, vp / svp)
Td[np.isnan(
Td)] = -99. * units.degree_Celsius # fill nan with very low temp
# print('Dewpoints: ', Td)
# print('Temperatures: ', Tm)
u = um.values
v = vm.values
skew.plot(p, Tm, 'r.-', ms=1, lw=.5, label='mean T')
skew.plot(p, Td, 'g.-', ms=5, lw=2, label='mean Td')
#skew.plot_barbs(p, u, v)
return Tm, Td, p
def plotter(fdict):
""" Go """
pgconn = get_dbconn("asos")
ctx = get_autoplot_context(fdict, get_description())
station = ctx["zstation"]
year = ctx["year"]
varname = ctx["var"]
df = read_sql(
"""
SELECT extract(year from valid) as year,
coalesce(mslp, alti * 33.8639, 1013.25) as slp,
extract(doy from valid) as doy, tmpf, dwpf, relh from alldata
where station = %s and dwpf > -50 and dwpf < 90 and
tmpf > -50 and tmpf < 120 and valid > '1950-01-01'
and report_type = 2
""",
pgconn,
params=(station, ),
index_col=None,
)
if df.empty:
raise NoDataFound("No Data was found.")
# saturation vapor pressure
# 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 the mixing ratio
df["mixing_ratio"] = 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["mixing_ratio"].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"]
dailymeans = df[["year", "doy", varname]].groupby(["year", "doy"]).mean()
dailymeans = dailymeans.reset_index()
df2 = dailymeans[["doy", varname]].groupby("doy").describe()
dyear = df[df["year"] == year]
df3 = dyear[["doy", varname]].groupby("doy").describe()
df3[(varname, "diff")] = df3[(varname, "mean")] - df2[(varname, "mean")]
(fig, ax) = plt.subplots(2, 1, figsize=(8, 6))
multiplier = 1000.0 if varname == "mixing_ratio" else 10.0
ax[0].fill_between(
df2[(varname, "min")].index.values,
df2[(varname, "min")].values * multiplier,
df2[(varname, "max")].values * multiplier,
color="gray",
)
ax[0].plot(
df2[(varname, "mean")].index.values,
df2[(varname, "mean")].values * multiplier,
label="Climatology",
)
ax[0].plot(
df3[(varname, "mean")].index.values,
df3[(varname, "mean")].values * multiplier,
color="r",
label="%s" % (year, ),
)
ax[0].set_title(("%s [%s]\nDaily Mean Surface %s") %
(station, ctx["_nt"].sts[station]["name"], PDICT[varname]))
lbl = ("Mixing Ratio ($g/kg$)"
if varname == "mixing_ratio" else PDICT[varname])
ax[0].set_ylabel(lbl)
ax[0].set_xlim(0, 366)
ax[0].set_ylim(bottom=0)
ax[0].set_xticks((1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335))
ax[0].set_xticklabels(calendar.month_abbr[1:])
ax[0].grid(True)
ax[0].legend(loc=2, fontsize=10)
cabove = "b" if varname == "mixing_ratio" else "r"
cbelow = "r" if cabove == "b" else "b"
rects = ax[1].bar(
df3[(varname, "diff")].index.values,
df3[(varname, "diff")].values * multiplier,
facecolor=cabove,
edgecolor=cabove,
)
for rect in rects:
if rect.get_height() < 0.0:
rect.set_facecolor(cbelow)
rect.set_edgecolor(cbelow)
plunits = "$g/kg$" if varname == "mixing_ratio" else "hPa"
ax[1].set_ylabel("%.0f Departure (%s)" % (year, plunits))
ax[1].set_xlim(0, 366)
ax[1].set_xticks((1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335))
ax[1].set_xticklabels(calendar.month_abbr[1:])
ax[1].grid(True)
return fig, df3
e = 6.112 * np.exp((17.67 * T) / (T + 243.5)) * (RH / 100)
r = 0.622 * (e) / (p - e) * 1000.
# e = mpcalc.vapor_pressure(1000. * units.mbar, mixing)
df = pd.DataFrame({
'pressure': p,
'temperature': T,
'r': r,
'speed': ws,
'direction': wd
})
p = df['pressure'].values * units.hPa
T = df['temperature'].values * units.degC
r = df['r'].values * units('g/kg')
e = mpcalc.vapor_pressure(p, r)
Td = mpcalc.dewpoint(e)
wind_speed = df['speed'].values * units.meter / (units.second)
wind_dir = df['direction'].values * units.degrees
u, v = mpcalc.wind_components(wind_speed, wind_dir)
lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0])
lfc_pressure, lfc_temperature = mpcalc.lfc(p, T, Td)
parcel_prof = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC')
cape, cin = mpcalc.cape_cin(p, T, Td, parcel_prof)
fig = plt.figure(figsize=(12., 9.))
fig.subplots_adjust(top=0.9,
bottom=0.1,
left=0.05,
right=0.96,
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 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)
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)
def test_vapor_pressure():
"""Test vapor pressure calculation."""
assert_almost_equal(vapor_pressure(998. * units.mbar, 0.04963),
73.74925 * units.mbar, 5)
vgrd = v[j, 2:29, :, lugar]
q = q[j, 2:29, :, lugar]
t = t[j, 2:29, :, lugar]
omega = w[j, 2:29, :, lugar]
## hay que transformar omega a w:
rgas = 287.058
g = 9.80665
vvel = np.zeros([int((omega.shape)[0]), int((omega.shape)[1])])
for ii in range(0, int((omega.shape)[0])):
rho = niveles[ii] / (rgas * t[ii, :])
a = -omega[ii, :] / (rho * g)
vvel[ii, :] = a
## calculo la temperatura potencial equivalente
e = mpcalc.vapor_pressure(1000. * units.mbar, q)
td = mpcalc.dewpoint(e)
titae = np.zeros([int((omega.shape)[0]), int((omega.shape)[1])])
for iii in range(0, int((omega.shape)[0])):
titae[iii, :] = mpcalc.equivalent_potential_temperature(
(niveles[iii] * 100) * units.pascal, t[iii, :] * units.kelvin,
td[iii, :])
titae = ndimage.gaussian_filter(titae, sigma=1, order=0)
## paso el geopotencial del terreno a presion para poder graficarlo junto a lo demas
z_sup = z_sup[0, :, lugar_z] / 9.80665
press = (100000 - (1.2) * 9.81 * (z_sup)) / 100
####### GRAFICADO ########
if i == 0:
fig = plt.figure(figsize=(18, 12))
def plotter(fdict):
""" Go """
pgconn = get_dbconn('asos')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['zstation']
network = ctx['network']
nt = NetworkTable(network)
year = ctx['year']
varname = ctx['var']
df = read_sql("""
SELECT extract(year from valid) as year,
coalesce(mslp, alti * 33.8639, 1013.25) as slp,
extract(doy from valid) as doy, tmpf, dwpf from alldata
where station = %s and dwpf > -50 and dwpf < 90 and
tmpf > -50 and tmpf < 120 and valid > '1950-01-01'
and report_type = 2
""",
pgconn,
params=(station, ),
index_col=None)
# compute RH
df['relh'] = mcalc.relative_humidity_from_dewpoint(
df['tmpf'].values * units('degF'), df['dwpf'].values * units('degF'))
# saturation vapor pressure
# 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 the relative humidity
df['relh'] = mcalc.relative_humidity_from_dewpoint(
df['tmpf'].values * units('degF'), df['dwpf'].values * units('degF'))
# Compute the mixing ratio
df['mixing_ratio'] = mcalc.mixing_ratio_from_relative_humidity(
df['relh'].values, 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['mixing_ratio'].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']
dailymeans = df[['year', 'doy', varname]].groupby(['year', 'doy']).mean()
dailymeans = dailymeans.reset_index()
df2 = dailymeans[['doy', varname]].groupby('doy').describe()
dyear = df[df['year'] == year]
df3 = dyear[['doy', varname]].groupby('doy').describe()
df3[(varname, 'diff')] = df3[(varname, 'mean')] - df2[(varname, 'mean')]
(fig, ax) = plt.subplots(2, 1, figsize=(8, 6))
multiplier = 1000. if varname == 'mixing_ratio' else 10.
ax[0].fill_between(df2[(varname, 'min')].index.values,
df2[(varname, 'min')].values * multiplier,
df2[(varname, 'max')].values * multiplier,
color='gray')
ax[0].plot(df2[(varname, 'mean')].index.values,
df2[(varname, 'mean')].values * multiplier,
label="Climatology")
ax[0].plot(df3[(varname, 'mean')].index.values,
df3[(varname, 'mean')].values * multiplier,
color='r',
label="%s" % (year, ))
ax[0].set_title(("%s [%s]\nDaily Mean Surface %s") %
(station, nt.sts[station]['name'], PDICT[varname]))
lbl = ("Mixing Ratio ($g/kg$)"
if varname == 'mixing_ratio' else PDICT[varname])
ax[0].set_ylabel(lbl)
ax[0].set_xlim(0, 366)
ax[0].set_ylim(bottom=0)
ax[0].set_xticks(
(1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335, 365))
ax[0].set_xticklabels(calendar.month_abbr[1:])
ax[0].grid(True)
ax[0].legend(loc=2, fontsize=10)
cabove = 'b' if varname == 'mixing_ratio' else 'r'
cbelow = 'r' if cabove == 'b' else 'b'
rects = ax[1].bar(df3[(varname, 'diff')].index.values,
df3[(varname, 'diff')].values * multiplier,
facecolor=cabove,
edgecolor=cabove)
for rect in rects:
if rect.get_height() < 0.:
rect.set_facecolor(cbelow)
rect.set_edgecolor(cbelow)
plunits = '$g/kg$' if varname == 'mixing_ratio' else 'hPa'
ax[1].set_ylabel("%.0f Departure (%s)" % (year, plunits))
ax[1].set_xlim(0, 366)
ax[1].set_xticks(
(1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335, 365))
ax[1].set_xticklabels(calendar.month_abbr[1:])
ax[1].grid(True)
return fig, df3
def test_vapor_pressure():
"""Test vapor pressure calculation."""
assert_almost_equal(vapor_pressure(998. * units.mbar, 0.04963),
73.74925 * units.mbar, 5)
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 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 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)
a value for vapor pressure assuming both 1000mb and 850mb ambient air
pressure values. It also demonstrates converting the resulting dewpoint
temperature to degrees Fahrenheit.
"""
import metpy.calc as mcalc
from metpy.units import units
###########################################
# Create a test value of mixing ratio in grams per kilogram
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'))
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
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[
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"]
a value for vapor pressure assuming both 1000mb and 850mb ambient air
pressure values. It also demonstrates converting the resulting dewpoint
temperature to degrees Fahrenheit.
"""
import metpy.calc as mpcalc
from metpy.units import units
###########################################
# Create a test value of mixing ratio in grams per kilogram
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'))
temp = data.variables['air_temperature'][:] * units('degC')
dewp = data.variables['dew_point_temperature'][:] * units('degC')
slp = data.variables['inches_ALTIM'][:] * units('inHg')
wspd = data.variables['wind_speed'][:] * units('m/s')
wdir = data.variables['wind_from_direction'][:] * units('degree')
########################################
# Use MetPy Calculations to calculate RH
# --------------------------------------
# Get ambient partial pressure, use to calculate mixing ratio
es = mpcalc.saturation_vapor_pressure(dewp)
mixr = mpcalc.mixing_ratio(es, slp)
# Calculate vapor pressure
vp = mpcalc.vapor_pressure(slp, mixr)
# Calculate saturation vapor pressure
svp = mpcalc.saturation_vapor_pressure(temp)
# Calculate relative humidity as a percentage
rh = (vp / svp) * 100
########################################
# Make Meteogram Plot
# -------------------
# Create the plots
fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, sharex=True, figsize=(12, 10))
ax1.plot(time, temp, ls='solid', marker='o', color='tab:red', ms=5)
df_Temp = pd.DataFrame(T)
df_Temp = df_Temp.loc[[index_CP],:].T
df_Temp.columns = ['FG_Temperature']
df_Temp = df_Temp[0:int(index_min_pressure)]
FG_T_NUCAPS_1 = df_Temp['FG_Temperature'].values -273.15
FG_T_NUCAPS = (df_Temp['FG_Temperature'].values * units.kelvin).to(units.degC)
### MOISTURE
# H2O MR
var = "H2O_MR"
global_scene.load([var], pressure_levels=True)
WVMR = global_scene[var].values # mass mixing ratio (mWV / mDA) kg/kg
WVMR = WVMR * 1000 # convert to grams
WVMR = WVMR * units('g/kg')
e_1 = mpcalc.vapor_pressure(p_NUCAPS_orig, WVMR)
T_d = mpcalc.dewpoint(e_1)
df_Temp_D = pd.DataFrame(T_d)
df_Temp_D = df_Temp_D.loc[[index_CP],:].T
df_Temp_D.columns = ['Temperature_D']
df_Temp_D = df_Temp_D[0:int(index_min_pressure)]
#T_d_NUCAPS_1 = df_Temp_D['Temperature_D'].values -273.15
T_d_NUCAPS = (df_Temp_D['Temperature_D'].values)
df_Temp_D = pd.DataFrame(T_d.magnitude)
df_Temp_D = df_Temp_D.loc[[index_CP],:].T
df_Temp_D.columns = ['Temperature_D']
df_Temp_D = df_Temp_D[0:int(index_min_pressure)]
#T_d_NUCAPS_1 = df_Temp_D['Temperature_D'].values -273.15
T_d_NUCAPS_1 = (df_Temp_D['Temperature_D'].values)
