def add_curves_Wyoming(ax, datetime, station, linewidth=1.0, LH_Tdepend=False):
"""
overlaying new curves of multiple soundings from Wyoming datasets
date: using datetime module. ex. datetime(2018,06,06)
station: station name. ex. 'MFL' Miami, Florida
"""
from siphon.simplewebservice.wyoming import WyomingUpperAir
date = datetime
station = station
df = WyomingUpperAir.request_data(date, station)
pressure = df['pressure'].values
Temp = df['temperature'].values
Temp_dew = df['dewpoint'].values
altitude = df['height'].values
q = mpcalc.mixing_ratio(
mpcalc.saturation_vapor_pressure(Temp_dew * units('degC')),
pressure * units('mbar'))
q = mpcalc.specific_humidity_from_mixing_ratio(q)
qs = mpcalc.mixing_ratio(
mpcalc.saturation_vapor_pressure(Temp * units('degC')),
pressure * units('mbar'))
# specific energies
if LH_Tdepend == False:
mse = mpcalc.moist_static_energy(altitude * units('meter'),
Temp * units('degC'), q)
mse_s = mpcalc.moist_static_energy(altitude * units('meter'),
Temp * units('degC'), qs)
dse = mpcalc.dry_static_energy(altitude * units('meter'),
Temp * units('degC'))
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)
# adding curves on the main axes
ax.plot(dse.magnitude, pressure, 'k', linewidth=linewidth)
ax.plot(mse.magnitude, pressure, 'b', linewidth=linewidth)
ax.plot(mse_s.magnitude, pressure, 'r', linewidth=linewidth)
def get_weather(self):
#------------------------------------------------------
#------------------------------------------------------
# Load TMY2
if self.file_ext == TMY2EXT:
f = open(self.weatherpath + self.city + self.file_ext, 'r')
# Header read for lat and lon
head = f.readline()
self.lat = int(head[39:41]) + int(head[42:44]) / 60.0
self.lon = int(head[47:50]) + int(head[51:53]) / 60.0
line = f.readline()
ind = 0
while line:
# Process the line
self.tothor[ind] = float(
line[17:21]) #Total horizontal solar Wh/m2
self.dirnorm[ind] = float(
line[23:27]) #Direct normal solar Wh/m2
self.difhor[ind] = float(
line[29:33]) #Diffuse Horizontal Solar Wh/m2
self.tdry[ind] = float(line[67:71]) * 0.1
#tdrybulb (deg C)
self.rhs[ind] = float(line[79:82]) * 0.01
#relative humidity (%)
self.tdew[ind] = float(line[73:77]) * 0.1
#tdew (deg C) to conform with TB code
self.press[ind] = float(line[84:88])
#atmospheric pressure (mbar) mb = 100 Pascals
#self.wind_speed[ind] = float(line[95:98]) * 0.1; #windspeed m/s
#self.wind_dir[ind] = float(line[90:93]); #wind direction azimuth
#self.cloud[ind] = float(line[59:61])/10.0; #Could cover fraction
#wd.ocloud = getint(line,63,2)/10.0; #Opaque cloud cover fraction
#wd.ceilht = getint(line,106,5); #Cloud ceiling height m
# Calculate specfic humidity from dry bulb, dew point, and atm pressure using MetPy
self.huss[ind] = mpcalc.specific_humidity_from_mixing_ratio(
mpcalc.mixing_ratio_from_relative_humidity(
mpcalc.relative_humidity_from_dewpoint(
self.tdry[ind] * units.degC,
self.tdew[ind] * units.degC),
self.tdry[ind] * units.degC,
self.press[ind] * units.mbar))
#Next line
line = f.readline()
ind = ind + 1
f.close()
#------------------------------------------------------
#------------------------------------------------------
# Load TMY3
elif self.file_ext == TMY3EXT:
f = open(
self.weatherpath + TMY3NUMBER[CITY.index(self.city)] +
self.file_ext, 'r')
# Header read for lat and lon
head = f.readline().split(',')
self.lat = float(head[4])
self.lon = float(head[5])
#Burn a line for the second part of the header.
line = f.readline()
line = f.readline()
ind = 0
while line:
line = line.split(',')
if len(line) < 20:
print('line is short!')
# Process the line
self.tothor[ind] = float(
line[4]) #Total horizontal solar Wh/m2
self.dirnorm[ind] = float(line[7]) #Direct normal solar Wh/m2
self.difhor[ind] = float(
line[10]) #Diffuse Horizontal Solar Wh/m2
self.tdry[ind] = float(line[31])
#tdrybulb (deg C)
self.rhs[ind] = float(line[37]) * 0.01
#relative humidity (%)
self.tdew[ind] = float(line[34])
#tdew (deg C) to conform with TB code
self.press[ind] = float(line[40])
#atmospheric pressure (mbar) mb = 100 Pascals
#self.wind_speed[ind] = float(line[46]); #windspeed m/s
#self.wind_dir[ind] = float(line[43]); #wind direction azimuth
# Calculate specfic humidity from dry bulb, dew point, and atm pressure using MetPy
self.huss[ind] = mpcalc.specific_humidity_from_mixing_ratio(
mpcalc.mixing_ratio_from_relative_humidity(
mpcalc.relative_humidity_from_dewpoint(
self.tdry[ind] * units.degC,
self.tdew[ind] * units.degC),
self.tdry[ind] * units.degC,
self.press[ind] * units.mbar))
#Next line
line = f.readline()
ind = ind + 1
f.close()
def test_specific_humidity_from_mixing_ratio():
"""Test specific humidity from mixing ratio."""
w = 0.01215 * units.dimensionless
q = specific_humidity_from_mixing_ratio(w)
assert_almost_equal(q, 0.01200, 5)
def get_specific_humidity(profiles):
qv = mpcalc.specific_humidity_from_mixing_ratio(profiles["mr"])
profiles["qv"] = (["launch_time", "zlay"], qv.magnitude)
return profiles
def gradient_fluxes(df): # This method is very sensitive to input data quality
"""Returns Sensible Heat Flux and Latent Heat Flux based on Steffen & DeMaria (1996) method"""
g = 9.81 # m/s**2
cp = 1005 # J/kg/K
k = 0.4 # von Karman
Lv = 2.50e6 # J/kg
fillvalue = common.fillvalue_float
ht_low, ht_high, ta_low, ta_high, wspd_low, wspd_high, rh_low, rh_high, phi_m, phi_h = ([] for _ in range(10))
# Average temp from both sensors for height1 and height2
ta1 = df.loc[:, ("ta_tc1", "ta_cs1")]
ta2 = df.loc[:, ("ta_tc2", "ta_cs2")]
df['ta1'] = ta1.mean(axis=1)
df['ta2'] = ta2.mean(axis=1)
# Assign low and high depending on height of sensors
idx = 0
while idx < len(df):
if df['wind_sensor_height_1'][idx] == fillvalue or df['wind_sensor_height_2'][idx] == fillvalue:
ht_low.append(np.nan)
ht_high.append(np.nan)
ta_low.append(df['ta1'][idx])
ta_high.append(df['ta2'][idx])
wspd_low.append(df['wspd1'][idx])
wspd_high.append(df['wspd2'][idx])
rh_low.append(df['rh1'][idx])
rh_high.append(df['rh2'][idx])
elif df['wind_sensor_height_1'][idx] > df['wind_sensor_height_2'][idx]:
ht_low.append(df['wind_sensor_height_2'][idx])
ht_high.append(df['wind_sensor_height_1'][idx])
ta_low.append(df['ta2'][idx])
ta_high.append(df['ta1'][idx])
wspd_low.append(df['wspd2'][idx])
wspd_high.append(df['wspd1'][idx])
rh_low.append(df['rh2'][idx])
rh_high.append(df['rh1'][idx])
else:
ht_low.append(df['wind_sensor_height_1'][idx])
ht_high.append(df['wind_sensor_height_2'][idx])
ta_low.append(df['ta1'][idx])
ta_high.append(df['ta2'][idx])
wspd_low.append(df['wspd1'][idx])
wspd_high.append(df['wspd2'][idx])
rh_low.append(df['rh1'][idx])
rh_high.append(df['rh2'][idx])
idx += 1
# Convert lists to arrays
ht_low = np.asarray(ht_low)
ht_high = np.asarray(ht_high)
ta_low = np.asarray(ta_low)
ta_high = np.asarray(ta_high)
wspd_low = np.asarray(wspd_low)
wspd_high = np.asarray(wspd_high)
rh_low = np.asarray(rh_low)
rh_high = np.asarray(rh_high)
ps = np.asarray(df['ps'].values)
# Potential Temperature
pot_tmp_low = potential_temperature(ps * units.pascal, ta_low * units.kelvin).magnitude
pot_tmp_high = potential_temperature(ps * units.pascal, ta_high * units.kelvin).magnitude
pot_tmp_avg = (pot_tmp_low + pot_tmp_high)/2
ta_avg = (ta_low + ta_high)/2
# Ri
du = wspd_high-wspd_low
du = np.asarray([fillvalue if i == 0 else i for i in du])
pot_tmp_avg = np.asarray([fillvalue if i == 0 else i for i in pot_tmp_avg])
ri = g*(pot_tmp_high - pot_tmp_low)*(ht_high - ht_low)/(pot_tmp_avg*du)
# Phi
for val in ri:
if val < -0.03:
phi = (1-18*val)**-0.25
phi_m.append(phi)
phi_h.append(phi/1.3)
elif -0.03 <= val < 0:
phi = (1-18*val)**-0.25
phi_m.append(phi)
phi_h.append(phi)
else:
phi = (1-5.2*val)**-1
phi_m.append(phi)
phi_h.append(phi)
phi_e = phi_h
# air density
rho = density(ps * units.pascal, ta_avg * units.kelvin, 0).magnitude # Use average temperature
# SH
ht_low = np.asarray([fillvalue if i == 0 else i for i in ht_low])
num = np.asarray([-a1 * cp * k**2 * (b1 - c1) * (d1 - e1) for a1, b1, c1, d1, e1 in
zip(rho, pot_tmp_high, pot_tmp_low, wspd_high, wspd_low)])
dnm = [a2 * b2 * np.log(c2 / d2)**2 for a2, b2, c2, d2 in
zip(phi_h, phi_m, ht_high, ht_low)]
dnm = np.asarray([fillvalue if i == 0 else i for i in dnm])
sh = num/dnm
sh = [fillvalue if abs(i) >= 100 else i for i in sh]
# Specific Humidity
mixing_ratio_low = mixing_ratio_from_relative_humidity(rh_low, ta_low * units.kelvin, ps * units.pascal)
mixing_ratio_high = mixing_ratio_from_relative_humidity(rh_high, ta_high * units.kelvin, ps * units.pascal)
q_low = specific_humidity_from_mixing_ratio(mixing_ratio_low).magnitude
q_high = specific_humidity_from_mixing_ratio(mixing_ratio_high).magnitude
q_low = q_low/100 # Divide by 100 to make it in range [0,1]
q_high = q_high/100
# LH
num = np.asarray([-a1 * Lv * k**2 * (b1 - c1) * (d1 - e1) for a1, b1, c1, d1, e1 in
zip(rho, q_high, q_low, wspd_high, wspd_low)])
dnm = [a2 * b2 * np.log(c2 / d2)**2 for a2, b2, c2, d2 in
zip(phi_e, phi_m, ht_high, ht_low)]
dnm = np.asarray([fillvalue if i == 0 else i for i in dnm])
lh = num/dnm
lh = [fillvalue if abs(i) >= 100 else i for i in lh]
return sh, lh
def test_specific_humidity_from_mixing_ratio():
"""Test specific humidity from mixing ratio."""
w = 0.01215 * units.dimensionless
q = specific_humidity_from_mixing_ratio(w)
assert_almost_equal(q, 0.01200, 5)