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_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 possible_watch(prof):
'''
Possible Weather/Hazard/Watch Type
This function generates a list of possible significant weather types
one can expect given a Profile object. (Currently works only for ConvectiveProfile.)
These possible weather types are computed via fuzzy logic through set thresholds that
have been found through a.) analyzing ingredients within the profile and b.) combining those ingredients
with forecasting experience to produce a suggestion of what hazards may exist. Some of the logic is
based on experience, some of it is based on actual National Weather Service criteria.
This function has not been formally verified and is not meant to be comprehensive nor
a source of strict guidance for weather forecasters. As always, the raw data is to be
consulted.
This code base is currently under development.
Wx Categories (ranked in terms of severity):
- PDS TOR
- TOR
- MRGL TOR
- SVR
- MRGL SVR
- FLASH FLOOD
- BLIZZARD
- WINTER STORM
- WIND CHILL
- FIRE WEATHER
- EXCESSIVE HEAT
- FREEZE
Suggestions for severe/tornado thresholds were contributed by Rich Thompson - NOAA Storm Prediction Center
Parameters
----------
prof : ConvectiveProfile object
Returns
-------
watch_types : a list of strings containing the weather types in code
colors : a list of the HEX colors corresponding to each weather type
'''
watch_types = []
colors = []
lr1 = params.lapse_rate(prof, 0, 1000, pres=False)
stp_eff = prof.stp_cin
stp_fixed = prof.stp_fixed
srw_4_6km = utils.mag(prof.srw_4_6km[0], prof.srw_4_6km[1])
sfc_8km_shear = utils.mag(prof.sfc_8km_shear[0], prof.sfc_8km_shear[1])
right_esrh = prof.right_esrh[0]
srh1km = prof.srh1km[0]
right_scp = prof.right_scp
## Cambios para el hemisferio sur JP JP
if prof.latitude < 0:
srh1km = -srh1km
stp_eff = -stp_eff
stp_fixed = -stp_fixed
right_scp = -prof.left_scp
right_esrh = -prof.left_esrh[0]
if stp_eff >= 3 and stp_fixed >= 3 and srh1km >= 200 and right_esrh >= 200 and srw_4_6km >= 15.0 and \
sfc_8km_shear > 45.0 and prof.sfcpcl.lclhght < 1000. and prof.mlpcl.lclhght < 1200 and lr1 >= 5.0 and \
prof.mlpcl.bminus >= -50 and prof.ebotm == 0:
watch_types.append("SPP TOR")
colors.append(constants.MAGENTA)
elif (stp_eff >= 3 or
stp_fixed >= 4) and prof.mlpcl.bminus >= -125. and prof.ebotm == 0:
watch_types.append("TOR")
colors.append("#FF0000")
elif (stp_eff >= 1 or stp_fixed >= 1) and (srw_4_6km >= 15.0 or sfc_8km_shear >= 40) and \
prof.mlpcl.bminus >= -50 and prof.ebotm == 0:
watch_types.append("TOR")
colors.append("#FF0000")
elif (stp_eff >= 1 or stp_fixed >= 1) and ((prof.low_rh + prof.mid_rh)/2. >= 60) and lr1 >= 5.0 and \
prof.mlpcl.bminus >= -50 and prof.ebotm == 0:
watch_types.append("TOR")
colors.append("#FF0000")
elif (stp_eff >= 1 or
stp_fixed >= 1) and prof.mlpcl.bminus >= -150 and prof.ebotm == 0.:
watch_types.append("MRGL TOR")
colors.append("#FF0000")
elif (stp_eff >= 0.5 and prof.right_esrh >= 150) or (stp_fixed >= 0.5 and srh1km >= 150) and \
prof.mlpcl.bminus >= -50 and prof.ebotm == 0.:
watch_types.append("MRGL TOR")
colors.append("#FF0000")
#t1 = tab.utils.FLOAT2STR(stp_eff, 1)
#t2 = tab.utils.FLOAT2STR(stp_fixed, 1)
#t3 = tab.utils.FLOAT2STR(srw_4_6km, 1)
#t4 = tab.utils.INT2STR(sfc_8km_shear)
#t5 = tab.utils.INT2STR(prof.mlpcl.bminus)
#t6 = tab.utils.INT2STR(prof.ebotm)
#with open('C:\\temp.txt', 'a') as f:
# f.write(t1 + ',' + t2 + ',' + t3 + ',' + t4 + ',' + t5 + ',' + t6 + '\n')
#SVR LOGIC
if (stp_fixed >= 1.0 or right_scp >= 4.0
or stp_eff >= 1.0) and prof.mupcl.bminus >= -50:
colors.append("#FFFF00")
watch_types.append("SVR")
elif right_scp >= 2.0 and (prof.ship >= 1.0 or
prof.dcape >= 750) and prof.mupcl.bminus >= -50:
colors.append("#FFFF00")
watch_types.append("SVR")
elif prof.sig_severe >= 30000 and prof.mmp >= 0.6 and prof.mupcl.bminus >= -50:
colors.append("#FFFF00")
watch_types.append("SVR")
elif prof.mupcl.bminus >= -75.0 and (prof.wndg >= 0.5 or prof.ship >= 0.5
or right_scp >= 0.5):
colors.append("#0099CC")
watch_types.append("MRGL SVR")
# Flash Flood Watch PWV is larger than normal and cloud layer mean wind speeds are slow
# This is trying to capture the ingredients of moisture and advection speed, but cannot
# handle precipitation efficiency or vertical motion
pw_climo_flag = prof.pwv_flag
pwat = prof.pwat
upshear = utils.comp2vec(prof.upshear_downshear[0],
prof.upshear_downshear[1])
if pw_climo_flag >= 2 and upshear[1] < 25:
watch_types.append("INUND REPENT")
colors.append("#5FFB17")
#elif pwat > 1.3 and upshear[1] < 25:
# watch_types.append("FLASH FLOOD")
# colors.append("#5FFB17")
# Blizzard if sfc winds > 35 mph and precip type detects snow
# Still needs to be tied into the
sfc_wspd = utils.KTS2MPH(prof.wspd[prof.get_sfc()])
if sfc_wspd > 35. and prof.tmpc[
prof.get_sfc()] <= 0 and "Snow" in prof.precip_type:
watch_types.append("TORM NIEVE")
colors.append("#3366FF")
# Wind Chill (if wind chill gets below -20 F)
if wind_chill(prof) < -20.:
watch_types.append("ST VIENTO")
colors.append("#3366FF")
# Fire WX (sfc RH < 30% and sfc_wind speed > 15 mph) (needs to be updated to include SPC Fire Wx Indices)
if sfc_wspd > 15. and thermo.relh(prof.pres[prof.get_sfc()],
prof.tmpc[prof.get_sfc()],
prof.tmpc[prof.get_sfc()]) < 30.:
watch_types.append("INCENDIOS")
colors.append("#FF9900")
# Excessive Heat (if Max_temp > 105 F and sfc dewpoint > 75 F)
if thermo.ctof(prof.dwpc[prof.get_sfc()]) > 75. and thermo.ctof(
params.max_temp(prof)) >= 105.:
watch_types.append("CALOR INTENSO")
colors.append("#CC33CC")
# Freeze (checks to see if wetbulb is below freezing and temperature isn't and wind speeds are low)
# Still in testing.
if thermo.ctof(prof.dwpc[prof.get_sfc()]) <= 32. and thermo.ctof(
prof.wetbulb[prof.get_sfc()]) <= 32 and prof.wspd[
prof.get_sfc()] < 5.:
watch_types.append("HELADAS")
colors.append("#3366FF")
watch_types.append("NINGUNA")
colors.append("#FFCC33")
return np.asarray(watch_types), np.asarray(colors)
''' Create the Sounding (Profile) Object '''
def possible_watch(prof):
'''
Possible Weather/Hazard/Watch Type
This function generates a list of possible significant weather types
one can expect given a Profile object. (Currently works only for ConvectiveProfile.)
These possible weather types are computed via fuzzy logic through set thresholds that
have been found through a.) analyzing ingredients within the profile and b.) combining those ingredients
with forecasting experience to produce a suggestion of what hazards may exist. Some of the logic is
based on experience, some of it is based on actual National Weather Service criteria.
This function has not been formally verified and is not meant to be comprehensive nor
a source of strict guidance for weather forecasters. As always, the raw data is to be
consulted.
This code base is currently under development.
Wx Categories (ranked in terms of severity):
- PDS TOR
- TOR
- MRGL TOR
- SVR
- MRGL SVR
- FLASH FLOOD
- BLIZZARD
- WINTER STORM
- WIND CHILL
- FIRE WEATHER
- EXCESSIVE HEAT
- FREEZE
Suggestions for severe/tornado thresholds were contributed by Rich Thompson - NOAA Storm Prediction Center
Parameters
----------
prof : ConvectiveProfile object
Returns
-------
watch_types : a list of strings containing the weather types in code
colors : a list of the HEX colors corresponding to each weather type
'''
watch_types = []
colors = []
lr1 = params.lapse_rate( prof, 0, 1000, pres=False )
stp_eff = prof.stp_cin
stp_fixed = prof.stp_fixed
srw_4_6km = utils.mag(prof.srw_4_6km[0],prof.srw_4_6km[1])
sfc_8km_shear = utils.mag(prof.sfc_8km_shear[0],prof.sfc_8km_shear[1])
right_esrh = prof.right_esrh[0]
srh1km = prof.srh1km[0]
if stp_eff >= 3 and stp_fixed >= 3 and srh1km >= 200 and right_esrh >= 200 and srw_4_6km >= 15.0 and \
sfc_8km_shear > 45.0 and prof.sfcpcl.lclhght < 1000. and prof.mlpcl.lclhght < 1200 and lr1 >= 5.0 and \
prof.mlpcl.bminus > -50 and prof.ebotm == 0:
watch_types.append("PDS TOR")
colors.append(constants.MAGENTA)
elif (stp_eff >= 3 or stp_fixed >= 4) and prof.mlpcl.bminus > -125. and prof.ebotm == 0:
watch_types.append("TOR")
colors.append("#FF0000")
elif (stp_eff >= 1 or stp_fixed >= 1) and (srw_4_6km >= 15.0 or sfc_8km_shear >= 40) and \
prof.mlpcl.bminus > -50 and prof.ebotm == 0:
watch_types.append("TOR")
colors.append("#FF0000")
elif (stp_eff >= 1 or stp_fixed >= 1) and ((prof.low_rh + prof.mid_rh)/2. >= 60) and lr1 >= 5.0 and \
prof.mlpcl.bminus > -50 and prof.ebotm == 0:
watch_types.append("TOR")
colors.append("#FF0000")
elif (stp_eff >= 1 or stp_fixed >= 1) and prof.mlpcl.bminus > -150 and prof.ebotm == 0.:
watch_types.append("MRGL TOR")
colors.append("#FF0000")
elif (stp_eff >= 0.5 and prof.right_esrh >= 150) or (stp_fixed >= 0.5 and srh1km >= 150) and \
prof.mlpcl.bminus > -50 and prof.ebotm == 0.:
watch_types.append("MRGL TOR")
colors.append("#FF0000")
#SVR LOGIC
if (stp_fixed >= 1.0 or prof.right_scp >= 4.0 or stp_eff >= 1.0) and prof.mupcl.bminus >= -50:
colors.append("#FFFF00")
watch_types.append("SVR")
elif prof.right_scp >= 2.0 and (prof.ship >= 1.0 or prof.dcape >= 750) and prof.mupcl.bminus >= -50:
colors.append("#FFFF00")
watch_types.append("SVR")
elif prof.sig_severe >= 30000 and prof.mmp >= 0.6 and prof.mupcl.bminus >= -50:
colors.append("#FFFF00")
watch_types.append("SVR")
elif prof.mupcl.bminus >= -75.0 and (prof.wndg >= 0.5 or prof.ship >= 0.5 or prof.right_scp >= 0.5):
colors.append("#0099CC")
watch_types.append("MRGL SVR")
# Flash Flood Watch PWV is larger than normal and cloud layer mean wind speeds are slow
# This is trying to capture the ingredients of moisture and advection speed, but cannot
# handle precipitation efficiency or vertical motion
pw_climo_flag = prof.pwv_flag
pwat = prof.pwat
upshear = utils.comp2vec(prof.upshear_downshear[0],prof.upshear_downshear[1])
if pw_climo_flag >= 2 and upshear[1] < 25:
watch_types.append("FLASH FLOOD")
colors.append("#5FFB17")
#elif pwat > 1.3 and upshear[1] < 25:
# watch_types.append("FLASH FLOOD")
# colors.append("#5FFB17")
# Blizzard if sfc winds > 35 mph and precip type detects snow
# Still needs to be tied into the
sfc_wspd = utils.KTS2MPH(prof.wspd[prof.get_sfc()])
if sfc_wspd > 35. and prof.tmpc[prof.get_sfc()] <= 0:
watch_types.append("BLIZZARD")
colors.append("#3366FF")
# Wind Chill (if wind chill gets below -20 F)
if wind_chill(prof) < -20.:
watch_types.append("WIND CHILL")
colors.append("#3366FF")
# Fire WX (sfc RH < 30% and sfc_wind speed > 15 mph) (needs to be updated to include SPC Fire Wx Indices)
if sfc_wspd > 15. and thermo.relh(prof.pres[prof.get_sfc()], prof.tmpc[prof.get_sfc()], prof.dwpc[prof.get_sfc()]) < 30. :
watch_types.append("FIRE WEATHER")
colors.append("#FF9900")
# Excessive Heat (if Max_temp > 105 F and sfc dewpoint > 75 F)
if thermo.ctof(prof.dwpc[prof.get_sfc()]) > 75. and thermo.ctof(params.max_temp(prof)) >= 105.:
watch_types.append("EXCESSIVE HEAT")
colors.append("#CC33CC")
# Freeze (checks to see if wetbulb is below freezing and temperature isn't and wind speeds are low)
# Still in testing.
if thermo.ctof(prof.dwpc[prof.get_sfc()]) <= 32. and thermo.ctof(prof.wetbulb[prof.get_sfc()]) <= 32 and prof.wspd[prof.get_sfc()] < 5.:
watch_types.append("FREEZE")
colors.append("#3366FF")
watch_types.append("NONE")
colors.append("#FFCC33")
return np.asarray(watch_types), np.asarray(colors)