def get_thermo(self):
'''
Function to generate thermodynamic indices.
Function returns nothing, but sets the following
variables:
self.k_idx - K Index, a severe weather index
self.pwat - Precipitable Water Vapor (inches)
self.lapserate_3km - 0 to 3km AGL lapse rate (C/km)
self.lapserate_3_6km - 3 to 6km AGL lapse rate (C/km)
self.lapserate_850_500 - 850 to 500mb lapse rate (C/km)
self.lapserate_700_500 - 700 to 500mb lapse rate (C/km)
self.convT - The Convective Temperature (F)
self.maxT - The Maximum Forecast Surface Temp (F)
self.mean_mixr - Mean Mixing Ratio
self.low_rh - low level mean relative humidity
self.mid_rh - mid level mean relative humidity
self.totals_totals - Totals Totals index, a severe weather index
Parameters
----------
None
Returns
-------
None
'''
## either get or calculate the indices, round to the nearest int, and
## convert them to strings.
## K Index
self.k_idx = params.k_index( self )
## precipitable water
self.pwat = params.precip_water( self )
## 0-3km agl lapse rate
self.lapserate_3km = params.lapse_rate( self, 0., 3000., pres=False )
## 3-6km agl lapse rate
self.lapserate_3_6km = params.lapse_rate( self, 3000., 6000., pres=False )
## 850-500mb lapse rate
self.lapserate_850_500 = params.lapse_rate( self, 850., 500., pres=True )
## 700-500mb lapse rate
self.lapserate_700_500 = params.lapse_rate( self, 700., 500., pres=True )
## 2-6 km max lapse rate
self.max_lapse_rate_2_6 = params.max_lapse_rate( self )
## convective temperature
self.convT = thermo.ctof( params.convective_temp( self ) )
## sounding forecast surface temperature
self.maxT = thermo.ctof( params.max_temp( self ) )
#fzl = str(int(self.sfcparcel.hght0c))
## 100mb mean mixing ratio
self.mean_mixr = params.mean_mixratio( self )
## 150mb mean rh
self.low_rh = params.mean_relh( self )
self.mid_rh = params.mean_relh( self, pbot=(self.pres[self.sfc] - 150),
ptop=(self.pres[self.sfc] - 350) )
## calculate the totals totals index
self.totals_totals = params.t_totals( self )
## calculate the inferred temperature advection
self.inf_temp_adv = params.inferred_temp_adv(self, lat=self.latitude)
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 get_thermo(self):
'''
Function to generate thermodynamic indices.
Function returns nothing, but sets the following
variables:
self.k_idx - K Index, a severe weather index
self.pwat - Precipitable Water Vapor (inches)
self.lapserate_3km - 0 to 3km AGL lapse rate (C/km)
self.lapserate_3_6km - 3 to 6km AGL lapse rate (C/km)
self.lapserate_850_500 - 850 to 500mb lapse rate (C/km)
self.lapserate_700_500 - 700 to 500mb lapse rate (C/km)
self.convT - The Convective Temperature (F)
self.maxT - The Maximum Forecast Surface Temp (F)
self.mean_mixr - Mean Mixing Ratio
self.low_rh - low level mean relative humidity
self.mid_rh - mid level mean relative humidity
self.totals_totals - Totals Totals index, a severe weather index
Parameters
----------
None
Returns
-------
None
'''
## either get or calculate the indices, round to the nearest int, and
## convert them to strings.
## K Index
self.k_idx = params.k_index( self )
## precipitable water
self.pwat = params.precip_water( self )
## 0-3km agl lapse rate
self.lapserate_3km = params.lapse_rate( self, 0., 3000., pres=False )
## 3-6km agl lapse rate
self.lapserate_3_6km = params.lapse_rate( self, 3000., 6000., pres=False )
## 850-500mb lapse rate
self.lapserate_850_500 = params.lapse_rate( self, 850., 500., pres=True )
## 700-500mb lapse rate
self.lapserate_700_500 = params.lapse_rate( self, 700., 500., pres=True )
## convective temperature
self.convT = thermo.ctof( params.convective_temp( self ) )
## sounding forecast surface temperature
self.maxT = thermo.ctof( params.max_temp( self ) )
#fzl = str(int(self.sfcparcel.hght0c))
## 100mb mean mixing ratio
self.mean_mixr = params.mean_mixratio( self )
## 150mb mean rh
self.low_rh = params.mean_relh( self )
self.mid_rh = params.mean_relh( self, pbot=(self.pres[self.sfc] - 150),
ptop=(self.pres[self.sfc] - 350) )
## calculate the totals totals index
self.totals_totals = params.t_totals( self )
## calculate the inferred temperature advection
self.inf_temp_adv = params.inferred_temp_adv(self)
def get_precip(self):
'''
Function to generate different indices and information
regarding any precipitation in the sounding. This helps fill
the data shown in the WINTER inset.
Returns nothing, but sets the following
variables:
self.dgz_pbot, self.dgz_ptop : the dendretic growth zone (DGZ) top and bottom (mb)
self.dgz_meanrh : DGZ mean relative humidity (%)
self.dgz_pw : the preciptable water vapor in the DGZ (inches)
self.dgz_meanq : the mean water vapor mixing ratio in the DGZ (g/kg)
self.dgz_meanomeg : the mean omega in the DGZ (microbars/second)
self.oprh : the OPRH variable (units don't mean anything)
self.plevel, self.phase, self.tmp, self.st : the initial phase, level, temperature, and state of any precip in the sounding
self.tpos, self.tneg, self.ttop, self.tbot : positive and negative temperature layers in the sounding
self.wpos, self.wneg, self.wtop, self.wbot : positive and negative wetbulb layers in the soundings
self.precip_type : the best guess precipitation type
Parameters
----------
None
Returns
-------
None
'''
self.dgz_pbot, self.dgz_ptop = params.dgz(self)
self.dgz_meanrh = params.mean_relh(self,
pbot=self.dgz_pbot,
ptop=self.dgz_ptop)
self.dgz_pw = params.precip_water(self,
pbot=self.dgz_pbot,
ptop=self.dgz_ptop)
self.dgz_meanq = params.mean_mixratio(self,
pbot=self.dgz_pbot,
ptop=self.dgz_ptop)
self.dgz_meanomeg = params.mean_omega(
self, pbot=self.dgz_pbot,
ptop=self.dgz_ptop) * 10 # to microbars/sec
self.oprh = self.dgz_meanomeg * self.dgz_pw * (self.dgz_meanrh / 100.)
self.plevel, self.phase, self.tmp, self.st = watch_type.init_phase(
self)
self.tpos, self.tneg, self.ttop, self.tbot = watch_type.posneg_temperature(
self, start=self.plevel)
self.wpos, self.wneg, self.wtop, self.wbot = watch_type.posneg_wetbulb(
self, start=self.plevel)
self.precip_type = watch_type.best_guess_precip(
self, self.phase, self.plevel, self.tmp, self.tpos, self.tneg)
def get_precip(self):
'''
Function to generate different indices and information
regarding any precipitation in the sounding. This helps fill
the data shown in the WINTER inset.
Returns nothing, but sets the following
variables:
self.dgz_pbot, self.dgz_ptop : the dendretic growth zone (DGZ) top and bottom (mb)
self.dgz_meanrh : DGZ mean relative humidity (%)
self.dgz_pw : the preciptable water vapor in the DGZ (inches)
self.dgz_meanq : the mean water vapor mixing ratio in the DGZ (g/kg)
self.dgz_meanomeg : the mean omega in the DGZ (microbars/second)
self.oprh : the OPRH variable (units don't mean anything)
self.plevel, self.phase, self.tmp, self.st : the initial phase, level, temperature, and state of any precip in the sounding
self.tpos, self.tneg, self.ttop, self.tbot : positive and negative temperature layers in the sounding
self.wpos, self.wneg, self.wtop, self.wbot : positive and negative wetbulb layers in the soundings
self.precip_type : the best guess precipitation type
Parameters
----------
None
Returns
-------
None
'''
self.dgz_pbot, self.dgz_ptop = params.dgz(self)
self.dgz_meanrh = params.mean_relh(self, pbot=self.dgz_pbot, ptop=self.dgz_ptop)
self.dgz_pw = params.precip_water(self, pbot=self.dgz_pbot, ptop=self.dgz_ptop)
self.dgz_meanq = params.mean_mixratio(self, pbot=self.dgz_pbot, ptop=self.dgz_ptop)
self.dgz_meanomeg = params.mean_omega(self, pbot=self.dgz_pbot, ptop=self.dgz_ptop) * 10 # to microbars/sec
self.oprh = self.dgz_meanomeg * self.dgz_pw * (self.dgz_meanrh/100.)
self.plevel, self.phase, self.tmp, self.st = watch_type.init_phase(self)
self.tpos, self.tneg, self.ttop, self.tbot = watch_type.posneg_temperature(self, start=self.plevel)
self.wpos, self.wneg, self.wtop, self.wbot = watch_type.posneg_wetbulb(self, start=self.plevel)
self.precip_type = watch_type.best_guess_precip(self, self.phase, self.plevel, self.tmp, self.tpos, self.tneg)
''' Create the Sounding (Profile) Object '''
