def do_thermodynamics(self):
assert 'temp' in self.soundingdata, \
"Temperature needed for thermodynamics! Add TEMP"
assert 'pres' in self.soundingdata, \
"Pressure needed for thermodynamics! Add PRES"
assert 'dwpt' in self.soundingdata, \
"Moisture needed for thermodynamics! Add DWPT"
# primary variables
prespa = self.soundingdata['pres'] * 100.
tempc = self.soundingdata['temp']
tempk = tempc + degCtoK
dwptc = self.soundingdata['dwpt']
# secondary variables
e = VaporPressure(dwptc)
esat = VaporPressure(tempc)
# assign/extract other variables
if 'thta' not in self.soundingdata:
self.soundingdata['thta'] = Theta(tempk, prespa)
if 'thte' not in self.soundingdata:
self.soundingdata['thte'] = ThetaE(tempk, prespa, e)
if 'thtv' not in self.soundingdata:
self.soundingdata['thtv'] = ThetaV(tempk, prespa, e)
if 'relh' not in self.soundingdata:
self.soundingdata['relh'] = 100. * e / esat
return
def surface_parcel(self,mixdepth=125): """Returns parameters for a parcel initialised by: 1. Surface pressure (i.e. pressure of lowest level) 2. Surface temperature determined from max(theta) of lowest <mixdepth> mbar 3. Dew point temperature representative of lowest <mixdepth> mbar Inputs: mixdepth (mbar): depth to average mixing ratio over """ pres=self.data["pres"] temp=self.data["temp"] dwpt=self.data["dwpt"] # identify the layers for averaging layers=pres>pres[0]-mixdepth # parcel pressure is surface pressure pres_s=pres[0] # average theta over mixheight to give # parcel temperature thta_mix=Theta(temp[layers]+273.15,pres[layers]*100.).max() temp_s=TempK(thta_mix,pres_s*100)-273.15 # average mixing ratio over mixheight vpres=SatVap(dwpt) mixr=MixRatio(vpres,pres*100) mixr_mix=mixr[layers].mean() vpres_s=MixR2VaporPress(mixr_mix,pres_s*100) # surface dew point temp dwpt_s=DewPoint(vpres_s) return pres_s,temp_s,dwpt_s
def mixed_layer_parcel(self, depth=100):
"""Returns parameters for a parcel initialised by:
1. Surface pressure (i.e. pressure of lowest level)
2. Surface temperature determined from mean(theta) of lowest <depth> mb
3. Dew point temperature representative of lowest <depth> mbar
Inputs:
depth (mbar): depth to average mixing ratio over
"""
pres = self.soundingdata["pres"]
temp = self.soundingdata["temp"]
dwpt = self.soundingdata["dwpt"]
pres0, temp0, dwpt0, null = self.surface_parcel()
# identify the layers for averaging
layers = pres > (pres0-depth)
# average theta over mixheight to give
# parcel temperature
thta_mix = Theta(temp[layers]+degCtoK, pres[layers]*100.).mean()
temp_s = TempK(thta_mix, pres0*100) - degCtoK
# average mixing ratio over mixheight
vpres = VaporPressure(dwpt)
mixr = MixRatio(vpres, pres*100)
mixr_mix = mixr[layers].mean()
vpres_s = MixR2VaporPress(mixr_mix, pres0*100)
# surface dew point temp
dwpt_s = DewPoint(vpres_s)
# print "----- Mixed Layer Parcel Characteristics -----"
# print "Mixed layer depth : %5d mb "%depth
# print "Mean mixed layer potential temperature: %5.1f K"%thta_mix
# print "Mean mixed layer mixing ratio : %5.2f g/kg"%
# (mixr_mix*1e3)
return pres0, temp_s, dwpt_s, 'ml'
raise NotImplementedError
Qccoarse=np.copy(Pcoarse)
Qrcoarse=np.copy(Pcoarse)
for la in range(Pcoarse.shape[1]):
for lo in range(Pcoarse.shape[2]):
Pcoarse[:,la,lo]=np.mean(rgrP[:,la*iRatio:la*iRatio+iRatio,lo*iRatio:lo*iRatio+iRatio], axis=(1,2))
Tcoarse[:,la,lo]=np.mean(rgrT[:,la*iRatio:la*iRatio+iRatio,lo*iRatio:lo*iRatio+iRatio], axis=(1,2))
Qvcoarse[:,la,lo]=np.mean(rgrQv[:,la*iRatio:la*iRatio+iRatio,lo*iRatio:lo*iRatio+iRatio], axis=(1,2))
Qccoarse[:,la,lo]=np.mean(rgrQc[:,la*iRatio:la*iRatio+iRatio,lo*iRatio:lo*iRatio+iRatio], axis=(1,2))
Qrcoarse[:,la,lo]=np.mean(rgrQr[:,la*iRatio:la*iRatio+iRatio,lo*iRatio:lo*iRatio+iRatio], axis=(1,2))
rgrP=Pcoarse
rgrT=Tcoarse
rgrQv=Qvcoarse
rgrQc=Qccoarse
rgrQr=Qrcoarse
# Calculate boyancy
ThetaK=Theta(rgrT,rgrP)
ThetaP=ThetaK*(1+0.608*rgrQv-rgrQc-rgrQr)
AvArea=int(100000/rgrDX[dx])
ThetaP_mean=scipy.ndimage.uniform_filter(ThetaP[:,:,:],[0,AvArea,AvArea])
Boyancy=(9.81*(ThetaP-ThetaP_mean))/ThetaP_mean
# Calculate cold pool intensity
NAN=np.copy(Boyancy)
for lev in range(Boyancy.shape[0]):
NAN[lev,:,:]=(Boyancy[lev,:,:] > -0.005)
NAN[lev,:,:][NAN[lev,:,:] == 1]=np.nan
if lev > 1:
NAN[lev,:,:]=NAN[lev,:,:]+NAN[lev-1,:,:]
Boyancy_Low=np.copy(Boyancy)
Boyancy_Low[np.isnan(NAN)]=np.nan
def make_skewt_axes(self, pmax=1100., pmin=50.):
self.fig = figure(figsize=(9, 8))
self.fig.clf()
rcParams.update({\
'font.size':10,})
P = linspace(pmax, pmin, 37)
pres_levels = np.arange(1000, 0, -100)
self.skewxaxis = self.fig.add_axes([.085, .1, .73, .8],
projection='skewx')
self.skewxaxis.set_yscale('log')
xticklocs = arange(-80, 45, 10)
T0 = xticklocs
w = array([
0.0001, 0.0004, 0.001, 0.002, 0.004, 0.007, 0.01, 0.016, 0.024,
0.032
])
self.skewxaxis.add_mixratio_isopleths(w,
P[P >= 550],
color='purple',
ls='--',
alpha=1.,
lw=0.5)
self.skewxaxis.add_dry_adiabats(linspace(250, 440, 20) - 273.15,
P,
color='red',
ls='--',
alpha=1.,
lw=0.5)
self.skewxaxis.add_moist_adiabats(linspace(8, 32, 7),
P[P >= 200],
color='g',
ls='--',
alpha=1.,
lw=0.5)
self.skewxaxis.other_housekeeping()
self.wbax = self.fig.add_axes([0.75, 0.1, 0.1, 0.8],
sharey=self.skewxaxis,
frameon=False) # wind barb axis
self.wbax.xaxis.set_ticks([], [])
self.wbax.yaxis.grid(True, ls='-', color='y', lw=0.5)
for tick in self.wbax.yaxis.get_major_ticks():
# tick.label1On = False
pass
self.wbax.get_yaxis().set_tick_params(size=0, color='y')
self.wbax.set_xlim(-1.5, 1.5)
self.wbax.get_yaxis().set_visible(False)
# Set up standard atmosphere height scale on
# LHS of plot. It's jus
majorLocatorKM = MultipleLocator(2)
majorLocatorKFT = MultipleLocator(5)
minorLocator = MultipleLocator(1)
self.htax = self.fig.add_axes([0.1, 0.1, 1e-6, 0.8],
sharey=self.skewxaxis,
frameon=False)
self.htax.xaxis.set_ticks([], [])
self.htax.spines['left'].set_color('k')
self.htax.spines['right'].set_visible(False)
self.htax.get_yaxis().set_tick_params(size=0, color='y')
self.htax.get_yaxis().set_visible(False)
pres_heights = np.array([
self.data['hght'][np.argmin(np.abs(_ - self.data['pres']))]
for _ in pres_levels
])
for ph in range(pres_levels.shape[0]):
self.htax.text(0,
pres_levels[ph],
'%d m' % pres_heights[ph],
fontsize=8)
#print pres_heights
self.fig.text(0.83, 0.9, 'Sounding Params')
#$\underline{Sounding Params}$')
#h0 = interp(0, self.data['temp'], self.data['hght'])
h0 = self.data['hght'][np.argmin(abs(self.data['temp'] - 0))]
h20 = self.data['hght'][np.argmin(abs(self.data['temp'] + 20))]
h40 = self.data['hght'][np.argmin(abs(self.data['temp'] + 40))]
lcl_height = 216 * (self.data['temp'][0] - self.data['dwpt'][0])
mr = MixRatio(SatVap(self.data['dwpt']), self.data['pres'] * 100)
pdiff = -1.0 * np.diff(self.data['pres'])
#print mr.shape, pdiff.shape
# precipitable water
pw = np.sum(
np.array([
mr[_] * 100.0 * pdiff[_] / 9.8 for _ in range(pdiff.shape[0])
]))
# crude estimate of wet bulb temp
tw = self.data['temp'][0] - (self.data['temp'][0] -
self.data['dwpt'][0]) / 3.
# now some shear calculations
wspd6km = self.data['sknt'][np.argmin(abs(self.data['hght'] - 6000))]
wdir6km = self.data['drct'][np.argmin(abs(self.data['hght'] - 6000))]
udiff = wspd6km * np.cos(
np.radians(270 - wdir6km)) - self.data['sknt'][3] * np.cos(
np.radians(270 - self.data['drct'][3]))
vdiff = wspd6km * np.sin(
np.radians(270 - wdir6km)) - self.data['sknt'][3] * np.sin(
np.radians(270 - self.data['drct'][3]))
#print udiff, vdiff
shear6km = np.sqrt(udiff**2 + vdiff**2)
#trop_index = tropopause_index(self.data['temp'])
#if trop_index == -999: tropopause_pressure = -999.
#else: tropopause_pressure = self.data['pres'][trop_index]
# New way to do tropopause pressure
# first calculate the parameters that go in
smooth_temp = running_mean(self.data['temp'], 3, ntimes=30)
dp = np.gradient(self.data['pres'])
#lapse = 1000.0*np.diff(smooth)/np.diff(S.data['hght'])
deriv = np.gradient(smooth_temp, dp)
deriv_smooth = running_mean(deriv, 3, ntimes=30)
theta = running_mean(Theta(273.15 + self.data['temp'],
self.data['pres'] * 100.0),
3,
ntimes=50)
dtheta = -1.0 * running_mean(np.gradient(theta, dp), 3, ntimes=10)
rh = running_mean(RH(self.data['temp'], self.data['dwpt']),
3,
ntimes=30)
drh = -1.0 * np.gradient(rh, dp)
weights = {
'theta': 1.0,
'dtdp': 1.0,
'T': 1.0,
'dthetadp': 1.0,
'drhdp': 0.6,
'pres': 1.0
}
input_data = {
'theta': theta,
'dtdp': deriv_smooth,
'T': smooth_temp,
'dthetadp': dtheta,
'drhdp': drh,
'pres': self.data['pres']
}
tropopause_pressure = fuzzy_tropopause(input_data, weights)
self.fig.text(0.83, 0.88, '0%s: %d m' % (degC, h0))
self.fig.text(0.83, 0.86, '-20%s: %d m' % (degC, h20))
self.fig.text(0.83, 0.84, '-40%s: %d m' % (degC, h40))
self.fig.text(0.83, 0.82, 'LCL: %d m' % lcl_height)
self.fig.text(0.83, 0.80, 'PW: %.1f mm' % pw)
self.fig.text(0.83, 0.78, '6 km shear: %d kts' % shear6km)
self.fig.text(0.83, 0.76, 'Trop: %d hPa' % (tropopause_pressure))
self.fig.text(0.83, 0.70, 'Surface')
self.fig.text(0.83, 0.68, 'P: %.1f hPa' % self.data['pres'][0])
self.fig.text(0.83, 0.66, 'Ht: %d m' % (self.data['hght'][0]))
self.fig.text(0.83, 0.64, 'T: %.1f %s' % (self.data['temp'][0], degC))
self.fig.text(0.83, 0.62,
'T$_D$: %.1f %s' % (self.data['dwpt'][0], degC))
self.fig.text(0.83, 0.60, 'T$_W$: %.1f %s' % (tw, degC))
# now do the hodograph?
self.hodoax = self.fig.add_axes([0.08, 0.69, 0.20, 0.20],
frameon=True,
polar=True)
self.hodoax.xaxis.set_ticks([], [])
self.hodoax.yaxis.set_ticks([], [])
speed_mask = np.ma.masked_where(self.data['sknt'] > 999,
self.data['sknt'])
angle = 270 - self.data['drct']
rad_angle = np.radians(angle)
pres_mask = np.bitwise_and(
self.data['pres'] > 200.,
self.data['drct'] < 361.) # only look at valid winds below 200 mb
if self.fmt == 'EDT' or self.fmt == 'EC':
self.hodoax.scatter(rad_angle[pres_mask], self.data['sknt'][pres_mask], c = self.data['hght'][pres_mask], \
edgecolors = 'none', s = 5, cmap = plt.cm.jet_r)
elif self.fmt == 'UWYO':
self.hodoax.plot(rad_angle,
self.data['sknt'],
c='red',
linewidth=3)
#self.hodoax.plot(rad_angle, speed_mask, c = 'red', linewidth = 3)
#self.hodoax.set_rmax(100)
self.hodoax.set_yticks(np.arange(0, 150, 30))
self.hodoax.tick_params(labelsize=5)
self.hodoax.set_rlabel_position(180)
self.hodoax.set_xticks(np.arange(0, 2 * np.pi, np.pi / 2))
self.hodoax.set_xticklabels([])
self.hodoax.grid(True)