def get_parcels(self):
'''
Function to generate various parcels and parcel
traces.
Returns nothing, but sets the following
variables:
self.mupcl : Most Unstable Parcel
self.sfcpcl : Surface Based Parcel
self.mlpcl : Mixed Layer Parcel
self.fcstpcl : Forecast Surface Parcel
self.ebottom : The bottom pressure level of
the effective inflow layer
self.etop : the top pressure level of
the effective inflow layer
self.ebotm : The bottom, meters (agl), of the
effective inflow layer
self.etopm : The top, meters (agl), of the
effective inflow layer
Parameters
----------
None
Returns
-------
None
'''
self.mupcl = params.parcelx( self, flag=3 )
if self.mupcl.lplvals.pres == self.pres[self.sfc]:
self.sfcpcl = self.mupcl
else:
self.sfcpcl = params.parcelx( self, flag=1 )
self.fcstpcl = params.parcelx( self, flag=2 )
self.mlpcl = params.parcelx( self, flag=4 )
self.usrpcl = params.Parcel()
## get the effective inflow layer data
self.ebottom, self.etop = params.effective_inflow_layer( self, mupcl=self.mupcl )
## if there was no effective inflow layer, set the values to masked
if self.etop is ma.masked or self.ebottom is ma.masked:
self.ebotm = ma.masked; self.etopm = ma.masked
self.effpcl = self.sfcpcl # Default to surface parcel, as in params.DefineProfile().
## otherwise, interpolate the heights given to above ground level
else:
self.ebotm = interp.to_agl(self, interp.hght(self, self.ebottom))
self.etopm = interp.to_agl(self, interp.hght(self, self.etop))
# The below code was adapted from params.DefineProfile()
# Lifting one additional parcel probably won't slow the program too much.
# It's just one more lift compared to all the lifts in the params.effective_inflow_layer() call.
mtha = params.mean_theta(self, self.ebottom, self.etop)
mmr = params.mean_mixratio(self, self.ebottom, self.etop)
effpres = (self.ebottom+self.etop)/2.
efftmpc = thermo.theta(1000., mtha, effpres)
effdwpc = thermo.temp_at_mixrat(mmr, effpres)
self.effpcl = params.parcelx(self, flag=5, pres=effpres, tmpc=efftmpc, dwpc=effdwpc) #This is the effective parcel.
def get_parcels(self):
'''
Function to generate various parcels and parcel
traces.
Returns nothing, but sets the following
variables:
self.mupcl : Most Unstable Parcel
self.sfcpcl : Surface Based Parcel
self.mlpcl : Mixed Layer Parcel
self.fcstpcl : Forecast Surface Parcel
self.ebottom : The bottom pressure level of
the effective inflow layer
self.etop : the top pressure level of
the effective inflow layer
self.ebotm : The bottom, meters (agl), of the
effective inflow layer
self.etopm : The top, meters (agl), of the
effective inflow layer
Parameters
----------
None
Returns
-------
None
'''
self.mupcl = params.parcelx( self, flag=3 )
if self.mupcl.lplvals.pres == self.pres[self.sfc]:
self.sfcpcl = self.mupcl
else:
self.sfcpcl = params.parcelx( self, flag=1 )
self.fcstpcl = params.parcelx( self, flag=2 )
self.mlpcl = params.parcelx( self, flag=4 )
self.usrpcl = params.Parcel()
## get the effective inflow layer data
self.ebottom, self.etop = params.effective_inflow_layer( self, mupcl=self.mupcl )
## if there was no effective inflow layer, set the values to masked
if self.etop is ma.masked or self.ebottom is ma.masked:
self.ebotm = ma.masked; self.etopm = ma.masked
self.effpcl = self.sfcpcl # Default to surface parcel, as in params.DefineProfile().
## otherwise, interpolate the heights given to above ground level
else:
self.ebotm = interp.to_agl(self, interp.hght(self, self.ebottom))
self.etopm = interp.to_agl(self, interp.hght(self, self.etop))
# The below code was adapted from params.DefineProfile()
# Lifting one additional parcel probably won't slow the program too much.
# It's just one more lift compared to all the lifts in the params.effective_inflow_layer() call.
mtha = params.mean_theta(self, self.ebottom, self.etop)
mmr = params.mean_mixratio(self, self.ebottom, self.etop)
effpres = (self.ebottom+self.etop)/2.
efftmpc = thermo.theta(1000., mtha, effpres)
effdwpc = thermo.temp_at_mixrat(mmr, effpres)
self.effpcl = params.parcelx(self, flag=5, pres=effpres, tmpc=efftmpc, dwpc=effdwpc) #This is the effective parcel.
def indices(prof, debug=False):
# return a formatted-string list of stability and kinematic indices
sfcpcl = params.parcelx(prof, flag=1)
mupcl = params.parcelx(prof, flag=3) # most unstable
mlpcl = params.parcelx(prof, flag=4) # 100 mb mean layer parcel
pcl = mupcl
sfc = prof.pres[prof.sfc]
p3km = interp.pres(prof, interp.to_msl(prof, 3000.))
p6km = interp.pres(prof, interp.to_msl(prof, 6000.))
p1km = interp.pres(prof, interp.to_msl(prof, 1000.))
mean_3km = winds.mean_wind(prof, pbot=sfc, ptop=p3km)
sfc_6km_shear = winds.wind_shear(prof, pbot=sfc, ptop=p6km)
sfc_3km_shear = winds.wind_shear(prof, pbot=sfc, ptop=p3km)
sfc_1km_shear = winds.wind_shear(prof, pbot=sfc, ptop=p1km)
#print "0-3 km Pressure-Weighted Mean Wind (kt):", utils.comp2vec(mean_3km[0], mean_3km[1])[1]
#print "0-6 km Shear (kt):", utils.comp2vec(sfc_6km_shear[0], sfc_6km_shear[1])[1]
srwind = params.bunkers_storm_motion(prof)
srh3km = winds.helicity(prof, 0, 3000., stu=srwind[0], stv=srwind[1])
srh1km = winds.helicity(prof, 0, 1000., stu=srwind[0], stv=srwind[1])
#print "0-3 km Storm Relative Helicity [m2/s2]:",srh3km[0]
#### Calculating variables based off of the effective inflow layer:
# The effective inflow layer concept is used to obtain the layer of buoyant parcels that feed a storm's inflow.
# Here are a few examples of how to compute variables that require the effective inflow layer in order to calculate them:
stp_fixed = params.stp_fixed(
sfcpcl.bplus, sfcpcl.lclhght, srh1km[0],
utils.comp2vec(sfc_6km_shear[0], sfc_6km_shear[1])[1])
ship = params.ship(prof)
# If you get an error about not converting masked constant to python int
# use the round() function instead of int() - Ahijevych May 11 2016
# 2nd element of list is the # of decimal places
indices = {
'SBCAPE': [sfcpcl.bplus, 0, 'J $\mathregular{kg^{-1}}$'],
'SBCIN': [sfcpcl.bminus, 0, 'J $\mathregular{kg^{-1}}$'],
'SBLCL': [sfcpcl.lclhght, 0, 'm AGL'],
'SBLFC': [sfcpcl.lfchght, 0, 'm AGL'],
'SBEL': [sfcpcl.elhght, 0, 'm AGL'],
'SBLI': [sfcpcl.li5, 0, 'C'],
'MLCAPE': [mlpcl.bplus, 0, 'J $\mathregular{kg^{-1}}$'],
'MLCIN': [mlpcl.bminus, 0, 'J $\mathregular{kg^{-1}}$'],
'MLLCL': [mlpcl.lclhght, 0, 'm AGL'],
'MLLFC': [mlpcl.lfchght, 0, 'm AGL'],
'MLEL': [mlpcl.elhght, 0, 'm AGL'],
'MLLI': [mlpcl.li5, 0, 'C'],
'MUCAPE': [mupcl.bplus, 0, 'J $\mathregular{kg^{-1}}$'],
'MUCIN': [mupcl.bminus, 0, 'J $\mathregular{kg^{-1}}$'],
'MULCL': [mupcl.lclhght, 0, 'm AGL'],
'MULFC': [mupcl.lfchght, 0, 'm AGL'],
'MUEL': [mupcl.elhght, 0, 'm AGL'],
'MULI': [mupcl.li5, 0, 'C'],
'0-1 km SRH': [srh1km[0], 0, '$\mathregular{m^{2}s^{-2}}$'],
'0-1 km Shear':
[utils.comp2vec(sfc_1km_shear[0], sfc_1km_shear[1])[1], 0, 'kt'],
'0-3 km SRH': [srh3km[0], 0, '$\mathregular{m^{2}s^{-2}}$'],
'0-6 km Shear':
[utils.comp2vec(sfc_6km_shear[0], sfc_6km_shear[1])[1], 0, 'kt'],
'PWV': [params.precip_water(prof), 2, 'inch'],
'K-index': [params.k_index(prof), 0, ''],
'STP(fix)': [stp_fixed, 1, ''],
'SHIP': [ship, 1, '']
}
eff_inflow = params.effective_inflow_layer(prof)
if any(eff_inflow):
ebot_hght = interp.to_agl(prof, interp.hght(prof, eff_inflow[0]))
etop_hght = interp.to_agl(prof, interp.hght(prof, eff_inflow[1]))
#print "Effective Inflow Layer Bottom Height (m AGL):", ebot_hght
#print "Effective Inflow Layer Top Height (m AGL):", etop_hght
effective_srh = winds.helicity(prof,
ebot_hght,
etop_hght,
stu=srwind[0],
stv=srwind[1])
indices['Eff. SRH'] = [
effective_srh[0], 0, '$\mathregular{m^{2}s^{-2}}$'
]
#print "Effective Inflow Layer SRH (m2/s2):", effective_srh[0]
ebwd = winds.wind_shear(prof, pbot=eff_inflow[0], ptop=eff_inflow[1])
ebwspd = utils.mag(*ebwd)
indices['EBWD'] = [ebwspd, 0, 'kt']
#print "Effective Bulk Wind Difference:", ebwspd
scp = params.scp(mupcl.bplus, effective_srh[0], ebwspd)
indices['SCP'] = [scp, 1, '']
stp_cin = params.stp_cin(mlpcl.bplus, effective_srh[0], ebwspd,
mlpcl.lclhght, mlpcl.bminus)
indices['STP(cin)'] = [stp_cin, 1, '']
#print "Supercell Composite Parameter:", scp
#print "Significant Tornado Parameter (w/CIN):", stp_cin
#print "Significant Tornado Parameter (fixed):", stp_fixed
# Update the indices within the indices dictionary on the side of the plot.
string = ''
for index, value in sorted(indices.items()):
if np.ma.is_masked(value[0]):
if debug:
print("skipping masked value for index=", index)
continue
if debug:
print("index=", index)
print("value=", value)
format = '%.' + str(value[1]) + 'f'
string += index + ": " + format % value[0] + " " + value[2] + '\n'
return string
''' Create the Sounding (Profile) Object '''
p3km = interp.pres(prof, interp.to_msl(prof, 3000.))
p6km = interp.pres(prof, interp.to_msl(prof, 6000.))
p1km = interp.pres(prof, interp.to_msl(prof, 1000.))
mean_3km = winds.mean_wind(prof, pbot=sfc, ptop=p3km)
sfc_6km_shear = winds.wind_shear(prof, pbot=sfc, ptop=p6km)
sfc_3km_shear = winds.wind_shear(prof, pbot=sfc, ptop=p3km)
sfc_1km_shear = winds.wind_shear(prof, pbot=sfc, ptop=p1km)
srwind = params.bunkers_storm_motion(prof)
srh3km = winds.helicity(prof, 0, 3000., stu=srwind[0], stv=srwind[1])
srh1km = winds.helicity(prof, 0, 1000., stu=srwind[0], stv=srwind[1])
stp_fixed = params.stp_fixed(
sfcpcl.bplus, sfcpcl.lclhght, srh1km[0],
utils.comp2vec(sfc_6km_shear[0], sfc_6km_shear[1])[1])
ship = params.ship(prof)
eff_inflow = params.effective_inflow_layer(prof)
ebot_hght = interp.to_agl(prof, interp.hght(prof, eff_inflow[0]))
etop_hght = interp.to_agl(prof, interp.hght(prof, eff_inflow[1]))
effective_srh = winds.helicity(prof,
ebot_hght,
etop_hght,
stu=srwind[0],
stv=srwind[1])
ebwd = winds.wind_shear(prof, pbot=eff_inflow[0], ptop=eff_inflow[1])
ebwspd = utils.mag(ebwd[0], ebwd[1])
scp = params.scp(mupcl.bplus, effective_srh[0], ebwspd)
stp_cin = params.stp_cin(mlpcl.bplus, effective_srh[0], ebwspd, mlpcl.lclhght,
mlpcl.bminus)
indices = {'SBCAPE': [int(sfcpcl.bplus), 'J/kg'],\
'SBCIN': [int(sfcpcl.bminus), 'J/kg'],\
# annotate temperature in F at bottom of T profile
temperatureF = skew.ax.text(prof.tmpc[0], prof.pres[0]+10, utils.INT2STR(thermo.ctof(prof.tmpc[0])),
verticalalignment='top', horizontalalignment='center', size=7, color=temperature_trace.get_color())
skew.plot(prof.pres, prof.vtmp, 'r', linewidth=0.5) # Virtual temperature profile
skew.plot(prof.pres, prof.wetbulb, 'c-') # wetbulb profile
dwpt_trace, = skew.plot(prof.pres, prof.dwpc, 'g', linewidth=2) # dewpoint profile
# annotate dewpoint in F at bottom of dewpoint profile
dewpointF = skew.ax.text(prof.dwpc[0], prof.pres[0]+10, utils.INT2STR(thermo.ctof(prof.dwpc[0])),
verticalalignment='top', horizontalalignment='center', size=7, color=dwpt_trace.get_color())
skew.plot(pcl.ptrace, pcl.ttrace, 'brown', linestyle="dashed" ) # parcel temperature trace
skew.ax.set_ylim(1050,100)
skew.ax.set_xlim(-50,45)
# Plot the effective inflow layer using purple horizontal lines
eff_inflow = params.effective_inflow_layer(prof)
inflow_bot = skew.ax.axhline(eff_inflow[0], color='purple',xmin=0.38, xmax=0.45)
inflow_top = skew.ax.axhline(eff_inflow[1], color='purple',xmin=0.38, xmax=0.45)
srwind = params.bunkers_storm_motion(prof)
# annotate effective inflow layer SRH
if eff_inflow[0]:
ebot_hght = interp.to_agl(prof, interp.hght(prof, eff_inflow[0]))
etop_hght = interp.to_agl(prof, interp.hght(prof, eff_inflow[1]))
effective_srh = winds.helicity(prof, ebot_hght, etop_hght, stu = srwind[0], stv = srwind[1])
# Set position of label
# x position is mean of horizontal line bounds
# For some reason this makes a big white space on the left side and for all subsequent plots.
inflow_SRH = skew.ax.text(
np.mean(inflow_top.get_xdata()), eff_inflow[1],
'%.0f' % effective_srh[0] + ' ' + '$\mathregular{m^{2}s^{-2}}$',
verticalalignment='bottom', horizontalalignment='center', size=6, transform=inflow_bot.get_transform(), color=inflow_top.get_color()