def test_ctof():
# single pass
input_c = 0
correct_f = 32
returned_f = thermo.ctof(input_c)
npt.assert_almost_equal(returned_f, correct_f)
# array_like pass
input_c = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
input_c = np.asanyarray(input_c)
correct_f = [32, 50, 68, 86, 104, 122, 140, 158, 176, 194, 212]
correct_f = np.asanyarray(correct_f)
returned_f = thermo.ctof(input_c)
npt.assert_almost_equal(returned_f, correct_f)
# single masked
input_c = ma.masked
correct_f = ma.masked
returned_f = thermo.ctof(input_c)
npt.assert_(type(returned_f), type(correct_f))
# array_like pass
inds = [0, 5, 7]
input_c = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
input_c = np.ma.asanyarray(input_c)
correct_f = [32, 50, 68, 86, 104, 122, 140, 158, 176, 194, 212]
correct_f = np.ma.asanyarray(correct_f)
input_c[inds] = ma.masked
correct_f[inds] = ma.masked
returned_f = thermo.ctof(input_c)
npt.assert_almost_equal(returned_f, correct_f)
def test_ctof():
# single pass
input_c = 0
correct_f = 32
returned_f = thermo.ctof(input_c)
npt.assert_almost_equal(returned_f, correct_f)
# array_like pass
input_c = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
input_c = np.asanyarray(input_c)
correct_f = [32, 50, 68, 86, 104, 122, 140, 158, 176, 194, 212]
correct_f = np.asanyarray(correct_f)
returned_f = thermo.ctof(input_c)
npt.assert_almost_equal(returned_f, correct_f)
# single masked
input_c = ma.masked
correct_f = ma.masked
returned_f = thermo.ctof(input_c)
npt.assert_equal(type(returned_f), type(correct_f))
# array_like pass
inds = [0, 5, 7]
input_c = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
input_c = np.ma.asanyarray(input_c)
correct_f = [32, 50, 68, 86, 104, 122, 140, 158, 176, 194, 212]
correct_f = np.ma.asanyarray(correct_f)
input_c[inds] = ma.masked
correct_f[inds] = ma.masked
returned_f = thermo.ctof(input_c)
npt.assert_almost_equal(returned_f, correct_f)
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 wind_chill(prof):
'''
Surface Wind Chill Equation
Computes wind chill at the surface data point in the profile object
using the equation found at:
www.nws.noaa.gov/os/windchill/index.shtml
Parameters
----------
prof : Profile object
Returns
-------
wind_chill : wind chill value in (F)
'''
# Needs to be tested
sfc_temp = thermo.ctof(prof.tmpc[prof.get_sfc()])
sfc_wspd = utils.KTS2MPH(prof.wspd[prof.get_sfc()])
wind_chill = 35.74 + (0.6215*sfc_temp) - (35.75*(sfc_wspd**0.16)) + \
0.4275 * (sfc_temp) * (sfc_wspd**0.16)
return wind_chill
def wind_chill(prof):
'''
Surface Wind Chill Equation
Computes wind chill at the surface data point in the profile object
using the equation found at:
www.nws.noaa.gov/os/windchill/index.shtml
Parameters
----------
prof : Profile object
Returns
-------
wind_chill : wind chill value in (F)
'''
# Needs to be tested
sfc_temp = thermo.ctof(prof.tmpc[prof.get_sfc()])
sfc_wspd = utils.KTS2MPH(prof.wspd[prof.get_sfc()])
wind_chill = 35.74 + (0.6215*sfc_temp) - (35.75*(sfc_wspd**0.16)) + \
0.4275 * (sfc_temp) * (sfc_wspd**0.16)
return wind_chill
def fosberg(prof):
'''
The Fosberg Fire Weather Index
Adapted from code donated by Rich Thompson - NOAA Storm Prediction Center
Description:
The FWI (Fire Weather Index) is defined by a quantitative model that provides
a nonlinear filter of meteorological data which results in a linear relationship
between the combined meteorological variables of relative humidity and wind speed,
and the behavior of wildfires. Thus the index deals with only the weather conditions,
not the fuels. Several sets of conditions have been defined by Fosberg (Fosberg, 1978)
to apply this to fire weather management. The upper limits have been set to give an
index value of 100 if the moisture content is zero and the wind is 30 mph.
Thus, the numbers range from 0 to 100 and if any number is larger than 100, it is set back to 100.
The index can be used to measure changes in fire weather conditions. Over several years of use,
Fosberg index values of 50 or greater generally appear significant on a national scale.
The SPC fire weather verification scheme uses the Fosberg Index, but with a check for
both temperature (60F) and adjective fire danger rating (3-High, 4-Very High, 5-Extreme).
Description Source - http://www.spc.noaa.gov/exper/firecomp/INFO/fosbinfo.html
WARNING: This function has not been fully tested.
Parameters
----------
prof : profile object
Profile object
Returns
-------
param : number
Fosberg Fire Weather Index
'''
tmpf = thermo.ctof(prof.tmpc[prof.get_sfc()])
fmph = utils.KTS2MPH(prof.wspd[prof.get_sfc()])
rh = thermo.relh(prof.pres[prof.sfc], prof.tmpc[prof.sfc], prof.dwpc[prof.sfc])
if (rh <= 10):
em = 0.03229 + 0.281073*rh - 0.000578*rh*tmpf
elif (10 < rh <= 50):
em = 2.22749 + 0.160107*rh - 0.014784*tmpf
else:
em = 21.0606 + 0.005565*rh*rh - 0.00035*rh*tmpf - 0.483199*rh
em30 = em/30
u_sq = fmph*fmph
fmdc = 1 - 2*em30 + 1.5*em30*em30 - 0.5*em30*em30*em30
param = (fmdc*np.sqrt(1+u_sq))/0.3002
return param
def fosberg(prof):
'''
The Fosberg Fire Weather Index
Adapted from code donated by Rich Thompson - NOAA Storm Prediction Center
Description:
The FWI (Fire Weather Index) is defined by a quantitative model that provides
a nonlinear filter of meteorological data which results in a linear relationship
between the combined meteorological variables of relative humidity and wind speed,
and the behavior of wildfires. Thus the index deals with only the weather conditions,
not the fuels. Several sets of conditions have been defined by Fosberg (Fosberg, 1978)
to apply this to fire weather management. The upper limits have been set to give an
index value of 100 if the moisture content is zero and the wind is 30 mph.
Thus, the numbers range from 0 to 100 and if any number is larger than 100, it is set back to 100.
The index can be used to measure changes in fire weather conditions. Over several years of use,
Fosberg index values of 50 or greater generally appear significant on a national scale.
The SPC fire weather verification scheme uses the Fosberg Index, but with a check for
both temperature (60F) and adjective fire danger rating (3-High, 4-Very High, 5-Extreme).
Description Source - http://www.spc.noaa.gov/exper/firecomp/INFO/fosbinfo.html
WARNING: This function has not been fully tested.
Parameters
----------
prof - Profile object
Returns
-------
param - the Fosberg Fire Weather Index
'''
tmpf = thermo.ctof(prof.tmpc[prof.get_sfc()])
fmph = utils.KTS2MPH(prof.wspd[prof.get_sfc()])
rh = thermo.relh(prof.pres[prof.sfc], prof.tmpc[prof.sfc], prof.dwpc[prof.sfc])
if (rh <= 10):
em = 0.03229 + 0.281073*rh - 0.000578*rh*tmpf
elif (10 < rh <= 50):
em = 2.22749 + 0.160107*rh - 0.014784*tmpf
else:
em = 21.0606 + 0.005565*rh*rh - 0.00035*rh*tmpf - 0.483199*rh
em30 = em/30
u_sq = fmph*fmph
fmdc = 1 - 2*em30 + 1.5*em30*em30 - 0.5*em30*em30*em30
param = (fmdc*np.sqrt(1+u_sq))/0.3002
return param
def get_indices(self):
'''
Function to set any additional indices that are included in the
thermo window.
Parameters
----------
None
Returns
-------
None
'''
self.tei = params.tei(self)
self.esp = params.esp(self)
self.mmp = params.mmp(self)
self.wndg = params.wndg(self)
self.sig_severe = params.sig_severe(self)
self.dcape, self.dpcl_ttrace, self.dpcl_ptrace = params.dcape(self)
self.drush = thermo.ctof(self.dpcl_ttrace[-1])
def get_indices(self):
'''
Function to set any additional indices that are included in the
thermo window.
Parameters
----------
None
Returns
-------
None
'''
self.tei = params.tei(self)
self.esp = params.esp(self)
self.mmp = params.mmp(self)
self.wndg = params.wndg(self)
self.sig_severe = params.sig_severe(self)
self.dcape, self.dpcl_ttrace, self.dpcl_ptrace = params.dcape(self)
self.drush = thermo.ctof(self.dpcl_ttrace[-1])
def possible_watch(prof, use_left=False):
'''
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.
Wx Categories (ranked in terms of severity):
* PDS TOR
* TOR
* MRGL TOR
* SVR
* MRGL SVR
* FLASH FLOOD
* BLIZZARD
* EXCESSIVE HEAT
Suggestions for severe/tornado thresholds were contributed by Rich Thompson - NOAA Storm Prediction Center
Parameters
----------
prof : profile object
ConvectiveProfile object
use_left : bool
If True, uses the parameters computed from the left-mover bunkers vector to decide the watch type. If False,
uses parameters from the right-mover vector. The default is False.
Returns
-------
watch_types : numpy array
strings containing the weather types in code
'''
watch_types = []
lr1 = params.lapse_rate(prof, 0, 1000, pres=False)
if use_left:
stp_eff = prof.left_stp_cin
stp_fixed = prof.left_stp_fixed
srw_4_6km = utils.mag(prof.left_srw_4_6km[0], prof.left_srw_4_6km[1])
esrh = prof.left_esrh[0]
srh1km = prof.left_srh1km[0]
else:
stp_eff = prof.right_stp_cin
stp_fixed = prof.right_stp_fixed
srw_4_6km = utils.mag(prof.right_srw_4_6km[0], prof.right_srw_4_6km[1])
esrh = prof.right_esrh[0]
srh1km = prof.right_srh1km[0]
if prof.latitude < 0:
stp_eff = -stp_eff
stp_fixed = -stp_fixed
esrh = -esrh
srh1km = -srh1km
sfc_8km_shear = utils.mag(prof.sfc_8km_shear[0], prof.sfc_8km_shear[1])
if stp_eff >= 3 and stp_fixed >= 3 and srh1km >= 200 and 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")
elif (stp_eff >= 3
or stp_fixed >= 4) and prof.mlpcl.bminus > -125. and prof.ebotm == 0:
watch_types.append("TOR")
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")
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")
elif (stp_eff >= 1
or stp_fixed >= 1) and prof.mlpcl.bminus > -150 and prof.ebotm == 0.:
watch_types.append("MRGL TOR")
elif (stp_eff >= 0.5 and esrh >= 150) or (stp_fixed >= 0.5 and srh1km >= 150) and \
prof.mlpcl.bminus > -50 and prof.ebotm == 0.:
watch_types.append("MRGL TOR")
#SVR LOGIC
if use_left:
scp = prof.left_scp
else:
scp = prof.right_scp
if (stp_fixed >= 1.0 or scp >= 4.0
or stp_eff >= 1.0) and prof.mupcl.bminus >= -50:
watch_types.append("SVR")
elif scp >= 2.0 and (prof.ship >= 1.0
or prof.dcape >= 750) and prof.mupcl.bminus >= -50:
watch_types.append("SVR")
elif prof.sig_severe >= 30000 and prof.mmp >= 0.6 and prof.mupcl.bminus >= -50:
watch_types.append("SVR")
elif prof.mupcl.bminus >= -75.0 and (prof.wndg >= 0.5 or prof.ship >= 0.5
or scp >= 0.5):
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
# Likely is good for handling slow moving MCSes.
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")
#elif pwat > 1.3 and upshear[1] < 25:
# watch_types.append("FLASH FLOOD")
# 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("BLIZZARD")
# Wind Chill (if wind chill gets below -20 F)
# TODO: Be reinstated in future releases if the logic becomes a little more solid.
#if wind_chill(prof) < -20.:
# watch_types.append("WIND CHILL")
# Fire WX (sfc RH < 30% and sfc_wind speed > 15 mph) (needs to be updated to include SPC Fire Wx Indices)
# TODO: Be reinstated in future releases once the logic becomes a little more solid
#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")
# Excessive Heat (use the heat index calculation (and the max temperature algorithm))
temp = thermo.ctof(prof.tmpc[prof.get_sfc()])
rh = thermo.relh(prof.pres[prof.get_sfc()], temp,
prof.dwpc[prof.get_sfc()])
hi = heat_index(temp, rh)
if hi > 105.:
watch_types.append("EXCESSIVE HEAT")
# Freeze (checks to see if wetbulb is below freezing and temperature isn't and wind speeds are low)
# Still in testing. To be reinstated in future releases.
#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")
watch_types.append("NONE")
return np.asarray(watch_types)
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 '''
prof = profile.create_profile(profile='default', pres=pres, hght=hght, tmpc=tmpc,
dwpc=dwpc, wspd=wspd, wdir=wdir, latitude=latitude, longitude=longitude, missing=-999., strictQC=True)
#### Adding a Parcel Trace
sfcpcl = params.parcelx( prof, flag=1 ) # Surface Parcel
#fcstpcl = params.parcelx( prof, flag=2 ) # Forecast Parcel
mupcl = params.parcelx( prof, flag=3 ) # Most-Unstable Parcel
mlpcl = params.parcelx( prof, flag=4 ) # 100 mb Mean Layer Parcel
# Set the parcel trace to be plotted as the Most-Unstable parcel.
pcl = mupcl
# Temperature, dewpoint, virtual temperature, wetbulb, parcel profiles
temperature_trace, = skew.plot(prof.pres, prof.tmpc, 'r', linewidth=2) # temperature profile
# 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)
def plot_wof(prof, members, figname, xlat, xlon, idateobj, vdateobj, **kwargs):
# '''
# Plots SHARPpy SPC window as .png
#
# Parameters
# ----------
# prof : a Profile Object from sharppy.sharptab.profile
#
# kwargs
# ------
# parcel_type: Parcel choice for plotting. 'sfc','ml','mu','fcst' Default is 'ml'
# filename: Filename as a string. Default is 'sounding.png'
# logo: Logo for upper-left portion of the skew-t. Default is 'None' and does not plot a logo.
# logo_dxdy: Size of logo. Actual dimensions are dT and dp as it is plotted on the skewT. Default is (20,13) This worked for a 489x132 pixel image.
# '''
#kwargs
parcel_type = kwargs.get('parcel_type', 'ml')
xpts = kwargs.get('x_pts')
ypts = kwargs.get('y_pts')
#Figure User Input
p_grid_labels = [
'1000', '', '', '850', '', '', '700', '', '', '', '500', '', '', '',
'300', '', '200', '', '100'
] #labels for the pressure ticks. Standard.
p_grid = [1000, 850, 700, 500, 300, 200,
100] #where horizontal lines go across the skew-T
my_dpi = 55 #dots per inch for the plot. This is a pretty hi-res image.
pmax = 1050 #lowest pressure on the skew-T
pmin = 100 #highest pressure on the skew-T
dp = -10 #pressure spacing for creating skew-T background lines
presvals = np.arange(
int(pmax),
int(pmin) + dp,
dp) #pressure values used for creating skew-T background lines
# Colors for wind speed bars and hodograph
hgt_list_bar = [0, 1000, 3000, 6000, 9000, 20000]
hgt_list_hodo = [0, 1000, 3000, 6000, 9000, 10000]
hodo_color = [
cb_colors.orange6, cb_colors.green6, cb_colors.blue6,
cb_colors.purple6, cb_colors.red6
]
hodo_label = ['0-1km', '1-3km', '3-6km', '6-9km', '9-10km']
#convoluted mess to get the title to be aligned how I wanted. This should be changed for others...
spaces = 10 #22
# title_text_1 = '' #site + ' ' + dt.strftime('%Y/%m/%d %H:%M UTC ' + data_type)
# title_text_3 = 'Sounding Powered by SHARPpy'
sharptext = 'Sounding Powered by SHARPpy'
# title_text_2 = title_text_3 = ''
title_text_3 = 'WoFS Sounding {}N, {}W'.format(
xlat, xlon) + (' ' * spaces) + 'Init: {} Valid: {}'.format(
idateobj.strftime('%Y-%m-%d %H%M UTC'),
vdateobj.strftime('%Y-%m-%d %H%M UTC'))
#xlat, xlon, initdate, validdate
title_text = title_text_3 #title_text_1 + (' '*spaces) +title_text_2 + (' '*spaces) + title_text_3
#Figures out where at which height the sounding reached in the list of h_new
#Then interpolates pressure to height levels
h_new = [0, 1000, 3000, 6000, 9000, 12000, 15000]
for i in range(len(h_new)):
if np.max(prof.hght) > h_new[i]:
index = i
h_new_labels = ['0 km', '1 km', '3 km', '6 km', '9 km', '12 km', '15 km']
h_new_labels = h_new_labels[0:index + 1]
#p_interped_func = interpolate.interp1d(prof.hght, prof.pres)
p_interped = sharptab.interp.pres(
prof, sharptab.interp.to_msl(prof, h_new[0:index + 1]))
#Thin out the winds for better plotting (significant level data points seem to bunch together to closely
minimum_separation = 250. #minimum spacing between wind barbs (meters)
h_barb = np.array(prof.hght).tolist()
p_barb = np.array(prof.pres).tolist()
spd_barb = np.array(prof.wspd).tolist()
direc_barb = np.array(prof.wdir).tolist()
#adds units to our newly created pressure, speed, and direction arrays for wind barb plotting
#p_barb = p_barb * units.mbar
#spd_barb = spd_barb * units.knot
#direc_barb = direc_barb * units.deg
# Convert wind speed and direction to components
#u, v = get_wind_components(prof.wspd * units.knot, prof.wdir * units.deg)
u, v = utils.vec2comp(prof.wdir, prof.wspd)
u_barb, v_barb = utils.vec2comp(prof.wdir, prof.wspd)
#SELECT PARCEL AND GET PARCEL DATA FROM SPC_UTILS
sfcpcl = prof.sfcpcl #params.parcelx( prof, flag=1 )
fcstpcl = prof.fcstpcl #params.parcelx( prof, flag=2)
mupcl = prof.mupcl #params.parcelx( prof, flag=3 )
mlpcl = prof.mlpcl #params.parcelx( prof, flag=4 )
if parcel_type == 'sfc':
pcl = sfcpcl
pcl_box_level = -0.065
pcl_type = 1
elif parcel_type == 'fcst':
pcl = fcstpcl
pcl_box_level = -0.0875
pcl_type = 2
elif parcel_type == 'mu':
pcl = mupcl
pcl_box_level = -0.1325
pcl_type = 4
elif parcel_type == 'ml':
pcl = mlpcl
pcl_box_level = -0.11
pcl_type = 3
else:
print(
"ERROR! Select 'sfc', 'fcst', 'mu', or 'ml' for parcel type. (plot_spc(prof,parcel_type)"
)
print("Defaulting to surface parcel...")
pcl = sfcpcl
pcl_box_level = -0.065
#PLOTTING *************************************************************************************************************
#Create full figure
fig = plt.figure(figsize=(1180 / my_dpi, 800 / my_dpi),
dpi=my_dpi,
frameon=False)
#SKEW T ***************************************************
ax = fig.add_subplot(111, projection='skewx',
facecolor="w") #skewed x-axis
# plot dashed temperature lines
ax.xaxis.grid(color='k',
linestyle='--',
dashes=(3, 3),
alpha=0.5,
zorder=0)
# plot the moist-adiabats
for temp in np.arange(-10, 45, 5):
tw = []
for pres in presvals:
tw.append(thermo.wetlift(1050., temp, pres))
ax.semilogy(tw,
presvals,
color=cb_colors.purple6,
linestyle='--',
dashes=(5, 2),
alpha=.3) #cb_colors.purple6
# plot the dry adiabats
for t in np.arange(-50, 80, 20):
thetas = ((t + thermo.ZEROCNK) / (np.power(
(1000. / presvals), thermo.ROCP))) - thermo.ZEROCNK
ax.semilogy(thetas, presvals, 'k', alpha=.3)
#plot mixing ratio lines
mixing_ratio_list = range(6, 36, 4)
for mr in mixing_ratio_list:
plt.plot((thermo.temp_at_mixrat(mr, 1050) - 273,
thermo.temp_at_mixrat(mr, 600) - 273), (1050, 600),
'g-',
lw=1.0,
zorder=3,
alpha=0.6)
ax.annotate(str(mr),
xy=((thermo.temp_at_mixrat(mr, 600) - 273), (600 - 3)),
xytext=((thermo.temp_at_mixrat(mr, 600) - 273), (600 - 3)),
ha='center',
color='g',
family='sans-serif',
weight='bold',
zorder=3,
fontsize=10,
alpha=0.6)
#plot horizontal lines at standard pressure levels
for p_loc in p_grid:
ax.axhline(y=p_loc, ls='-', c='k', alpha=0.5, linewidth=1.5, zorder=3)
# PLOT THE DATA ON THE SKEW-T
# Plot the data using normal plotting functions, in this case using log scaling in Y, as dicatated by the typical meteorological plot
ax.semilogy(prof.wetbulb,
prof.pres,
c="c",
linestyle='-',
lw=1,
alpha=1.0,
zorder=3) # Plot the wetbulb profile
ax.annotate(str(int(round(thermo.ctof(prof.wetbulb[prof.sfc])))),
xy=(prof.wetbulb[prof.sfc], prof.pres[prof.sfc] + 30),
xytext=(prof.wetbulb[prof.sfc], prof.pres[prof.sfc] + 30),
ha='left',
color="c",
family='sans-serif',
weight='normal',
zorder=7,
fontsize=12,
alpha=1.0) # annotate the sfc wetbulb in F
ax.semilogy(prof.dwpc,
prof.pres,
c=cb_colors.blue6,
linestyle='-',
lw=3,
zorder=3) # plot the dewpoint profile
ax.annotate(str(int(round(thermo.ctof(prof.dwpc[prof.sfc])))),
xy=(prof.dwpc[prof.sfc], prof.pres[prof.sfc] + 30),
xytext=(prof.dwpc[prof.sfc], prof.pres[prof.sfc] + 30),
ha='left',
color=cb_colors.blue6,
family='sans-serif',
weight='bold',
zorder=7,
fontsize=12,
alpha=1.0) # annotate the sfc dewpoint in F
ax.semilogy(prof.tmpc,
prof.pres,
c=cb_colors.orange6,
linestyle='-',
lw=3,
zorder=3) # Plot the temperature profile
ax.semilogy(prof.vtmp,
prof.pres,
c=cb_colors.orange6,
linestyle='--',
lw=3,
zorder=3) # Plot the temperature profile
ax.annotate(str(int(round(thermo.ctof(prof.tmpc[prof.sfc])))),
xy=(prof.tmpc[prof.sfc], prof.pres[prof.sfc] + 30),
xytext=(prof.tmpc[prof.sfc], prof.pres[prof.sfc] + 30),
ha='left',
color=cb_colors.orange6,
family='sans-serif',
weight='bold',
zorder=7,
fontsize=12,
alpha=1.0) # annotate the sfc temp in F
ax.semilogy(pcl.ttrace,
pcl.ptrace,
c=cb_colors.gray6,
linestyle='--',
dashes=(3, 3),
lw=1.5,
alpha=1.0,
zorder=3) # plot the parcel trace
#member_cape = []
if members is not None:
# print( "Plotting members...")
for m_idx in range(len(members['tmpc'])):
ax.semilogy(members['dwpc'][m_idx],
members['pres'][m_idx],
c=cb_colors.blue6,
linestyle='-',
lw=1.,
zorder=1,
alpha=.6) # plot the dewpoint profile
ax.semilogy(members['tmpc'][m_idx],
members['pres'][m_idx],
c=cb_colors.orange6,
linestyle='-',
lw=1.,
zorder=1,
alpha=.6) # Plot the temperature profile
# set label and tick marks for pressure and temperature
ax.xaxis.set_major_locator(plt.MultipleLocator(10))
ax.set_xticks(np.arange(-100, 60, 10))
ax.set_xticklabels([str(i) for i in np.arange(-100, 60, 10)],
color=cb_colors.gray7,
fontsize=12)
ax.set_xlim(-50, 50)
ax.yaxis.set_major_formatter(plt.ScalarFormatter())
ax.set_yticks(np.linspace(1000, 100, 19))
ax.set_yticklabels(p_grid_labels, color=cb_colors.gray7, fontsize=12)
ax.set_ylim(1050, 100)
#plot the title text
plt.text(0.05,
0.97,
title_text,
fontsize=15,
color=cb_colors.gray8,
weight='bold',
ha='left',
transform=fig.transFigure)
#x_hodo.annotate(sharptext, xy=(0.95, 0.95), xytext=(0.95, 0.95),xycoords='axes fraction',textcoords='axes fraction',ha='center', va='bottom', color=cb_colors.gray7, family='sans-serif', weight='bold', zorder=3,fontsize=14)
plt.text(0.8,
0.97,
sharptext,
fontsize=15,
color=cb_colors.gray8,
weight='bold',
ha='left',
transform=fig.transFigure)
#adjust the skew-T plot to make room for the rest of the figures. This was important to make everything line up.
plt.subplots_adjust(left=0.05, right=0.55, top=0.96, bottom=0.15)
#Plot the height labels on the left axis
ax2 = ax.twinx(
) #makes a twin of the skew-T subplot that's not skewed at 45 degrees
plt.yscale('log', nonposy='clip')
plt.yticks(p_interped, h_new_labels, color=cb_colors.green4, ha='left')
ax2.yaxis.tick_left()
ax2.tick_params(direction='in',
pad=-15,
axis='both',
which='major',
colors=cb_colors.green4,
length=10,
width=1.5)
ax2.set_yticklabels(h_new_labels,
fontsize=12,
weight='bold',
color=cb_colors.green4)
x = np.random.uniform(0.0, 10.0, 15)
y = np.random.uniform(0.0, 10.0, 15)
# Plot LCL and LFC levels
plt.plot((38, 42), (pcl.lfcpres, pcl.lfcpres),
c="darkgreen",
lw=2.0,
zorder=3)
ax2.annotate('LFC',
xy=(40, pcl.lfcpres),
xytext=(40, pcl.lfcpres),
ha='center',
va='bottom',
color=cb_colors.green5,
family='sans-serif',
weight='bold',
zorder=3,
fontsize=12)
plt.plot((38, 42), (pcl.lclpres, pcl.lclpres),
c="r",
linestyle='-',
lw=2.0,
zorder=3)
ax2.annotate('LCL',
xy=(40, pcl.lclpres + 5.),
xytext=(40, pcl.lclpres + 5.),
ha='center',
va='top',
color=cb_colors.red5,
family='sans-serif',
weight='bold',
zorder=3,
fontsize=12)
plt.plot((38, 42), (pcl.elpres, pcl.elpres),
c="m",
linestyle='-',
lw=2.0,
zorder=3)
ax2.annotate('EL',
xy=(40, pcl.elpres),
xytext=(40, pcl.elpres),
ha='center',
va='bottom',
color=cb_colors.purple5,
family='sans-serif',
weight='bold',
zorder=3,
fontsize=12)
# Plot Eff Inflow Layer
eff_inflow = (prof.ebottom, prof.etop)
eff_inflow_top = sharptab.interp.to_agl(
prof, sharptab.interp.hght(prof, eff_inflow[1]))
eff_inflow_bottom = sharptab.interp.to_agl(
prof, sharptab.interp.hght(prof, eff_inflow[0]))
bunkers = prof.srwind
effective_srh = prof.right_esrh
plt.plot((-25, -20), (eff_inflow[0], eff_inflow[0]),
c=cb_colors.red5,
linestyle='-',
lw=1.75,
zorder=3)
plt.plot((-25, -20), (eff_inflow[1], eff_inflow[1]),
c=cb_colors.red5,
linestyle='-',
lw=1.75,
zorder=3)
plt.plot((-22.5, -22.5), (eff_inflow[0], eff_inflow[1]),
c=cb_colors.red5,
linestyle='-',
lw=1.75,
zorder=3)
try:
plt.annotate(str(int(eff_inflow_bottom)) + 'm ',
xy=(-25, eff_inflow[0]),
xytext=(-25, eff_inflow[0]),
ha='right',
va='center',
color=cb_colors.red5,
zorder=3,
fontsize=12,
weight='bold')
plt.annotate(str(int(eff_inflow_top)) + 'm ',
xy=(-25, eff_inflow[1]),
xytext=(-25, eff_inflow[1]),
ha='right',
va='center',
color=cb_colors.red5,
zorder=3,
fontsize=12,
weight='bold')
plt.annotate(str(int(effective_srh[0])) + ' m$^2$/s$^2$',
xy=(-22.5, eff_inflow[1] - 10),
xytext=(-22.5, eff_inflow[1] - 10),
ha='center',
va='bottom',
color=cb_colors.red5,
zorder=3,
fontsize=12,
weight='bold')
except:
print("NO EFF INFLOW")
# PLOT WINDBARBS
p_barb = np.asarray(p_barb)
pidx = idx = np.where(np.asarray(p_barb) >= 100.)[0]
wind_barbs = ax2.barbs(55 * np.ones(len(p_barb[idx])),
p_barb[idx],
u_barb[idx],
v_barb[idx],
barbcolor=cb_colors.gray7,
flagcolor='None',
zorder=10,
lw=1.0,
length=7)
wind_barbs.set_clip_on(False)
ax2.invert_yaxis()
ax2.set_xlim(-50, 50)
ax2.set_ylim(1050, 100)
spd_barb = np.asarray(spd_barb)
# Create hodograph ********************************************************************************************
ax_hod = fig.add_axes([0.60, 0.45, 0.38, 0.475],
frameon=False) #, facecolor='k')
# Set the characteristics of the tick marks
for tick in ax_hod.xaxis.get_major_ticks():
tick.label.set_fontsize(12)
tick.label.set_color(cb_colors.gray7)
tick.label.set_weight('bold')
for tick in ax_hod.yaxis.get_major_ticks():
tick.label.set_fontsize(12)
tick.label.set_color(cb_colors.gray7)
tick.label.set_weight('bold')
for i in range(10, 90, 10):
# Draw the range rings around the hodograph.
circle = plt.Circle((0, 0),
i,
linestyle='--',
color='k',
alpha=.3,
fill=False)
ax_hod.add_artist(circle)
# Set the tick parameters to displace the tick labels from the hodograph axes
ax_hod.tick_params(axis='x',
which='major',
labelsize=10,
color=cb_colors.gray7,
pad=-235,
length=0)
ax_hod.tick_params(axis='y',
which='major',
labelsize=10,
color=cb_colors.gray7,
pad=-315,
length=0)
# Plot the hodograph axes
ax_hod.axvline(0, color=cb_colors.gray7, linestyle='-', linewidth=2.)
ax_hod.axhline(0, color=cb_colors.gray7, linestyle='-', linewidth=2.)
ax_hod.set_yticks(range(-60, 65, 10))
ax_hod.set_xticks(range(-70, 75, 10))
hod_yticklabels = [str(abs(i)) for i in range(-60, 65, 10)]
#hod_yticklabels[len(hod_yticklabels)/2] = ''
hod_xticklabels = [str(abs(i)) for i in range(-70, 75, 10)]
#hod_xticklabels[len(hod_xticklabels)/2] = ''
ax_hod.set_yticklabels(hod_yticklabels,
fontsize=12,
weight='bold',
color=cb_colors.gray7)
ax_hod.set_xticklabels(hod_xticklabels,
fontsize=12,
weight='bold',
color=cb_colors.gray7)
# Plot the hodograph using the color scheme for different layers (0-3, 3-6, etc.)
bounds = [0, 1000, 3000, 6000, 9000, 12000]
for i in range(1, len(bounds), 1):
subset_idxs = np.where(
(prof.hght <= sharptab.interp.to_msl(prof, bounds[i]))
& (prof.hght >= sharptab.interp.to_msl(prof, bounds[i - 1])))
subset_hghts = np.ma.concatenate(
([sharptab.interp.to_msl(prof, bounds[i - 1])],
prof.hght[subset_idxs], [sharptab.interp.to_msl(prof,
bounds[i])]))
u, v = sharptab.interp.components(
prof, sharptab.interp.pres(prof, subset_hghts))
ax_hod.plot(u,
v,
c=hodo_color[i - 1],
linewidth=2.5,
label=hodo_label[i - 1],
zorder=3)
if members is not None:
for mprof in members['member_profs']:
for i in range(1, len(bounds), 1):
subset_idxs = np.where(
(mprof.hght <= sharptab.interp.to_msl(mprof, bounds[i]))
& (mprof.hght >= sharptab.interp.to_msl(
mprof, bounds[i - 1])))
subset_hghts = np.ma.concatenate(
([sharptab.interp.to_msl(mprof, bounds[i - 1])
], mprof.hght[subset_idxs],
[sharptab.interp.to_msl(mprof, bounds[i])]))
u, v = sharptab.interp.components(
mprof, sharptab.interp.pres(mprof, subset_hghts))
ax_hod.plot(u,
v,
c=hodo_color[i - 1],
linewidth=1.25,
alpha=0.6,
label=hodo_label[i - 1],
zorder=1)
# Get the Bunkers storm motions and convert them to strings to plot
bunkers = srwind = prof.srwind
bunkers_rt = utils.comp2vec(bunkers[0], bunkers[1])
bunkers_lf = utils.comp2vec(bunkers[2], bunkers[3])
bunkers_rt_str = str(int(np.ma.around(bunkers_rt[0], 0))) + "/" + str(
int(np.ma.around(bunkers_rt[1], 0)))
bunkers_lf_str = str(int(np.ma.around(bunkers_lf[0], 0))) + "/" + str(
int(np.ma.around(bunkers_lf[1], 0)))
# Plot the effective inflow layer on the hodograph
effubot, effvbot = sharptab.interp.components(prof, eff_inflow[0])
effutop, effvtop = sharptab.interp.components(prof, eff_inflow[1])
ax_hod.plot([effubot, srwind[0]], [effvbot, srwind[1]],
c='c',
linewidth=1.5)
ax_hod.plot([effutop, srwind[0]], [effvtop, srwind[1]],
c='c',
linewidth=1.5)
# Annotate where the Bunkers storm motion vectors are on the hodograph
ax_hod.plot(srwind[0],
srwind[1],
marker='o',
fillstyle='none',
markeredgecolor=cb_colors.blue8,
markeredgewidth=1.5,
markersize=11)
ax_hod.annotate(bunkers_rt_str + ' RM', (srwind[0] + 1.5, srwind[1] - 1.5),
fontsize=12,
va="top",
ha="left",
color='k',
weight='bold',
zorder=10)
ax_hod.plot(srwind[2],
srwind[3],
marker='o',
fillstyle='none',
markeredgecolor=cb_colors.blue8,
markeredgewidth=1.5,
markersize=11)
ax_hod.annotate(bunkers_lf_str + ' LM', (srwind[2] + 1.5, srwind[3] - 1.5),
fontsize=12,
va="top",
ha="left",
color='k',
weight='bold',
zorder=10)
# Annotate where the Corfidi MBE vectors are on the hodograph
corfidi = prof.upshear_downshear
corfidi_up = utils.comp2vec(corfidi[0], corfidi[1])
corfidi_dn = utils.comp2vec(corfidi[2], corfidi[3])
c = 'k' #'#0A74C6'
ax_hod.plot(corfidi[0],
corfidi[1],
marker='o',
fillstyle='none',
markeredgecolor=c,
markeredgewidth=1.5,
markersize=9)
ax_hod.annotate(str(int(corfidi_up[0])) + '/' + str(int(corfidi_up[1])) +
' UP', (corfidi[0] + 1.5, corfidi[1] - 1.5),
fontsize=12,
va="top",
ha="left",
color=cb_colors.purple8,
weight='bold',
zorder=10)
ax_hod.plot(corfidi[2],
corfidi[3],
marker='o',
fillstyle='none',
markeredgecolor=c,
markeredgewidth=1.5,
markersize=9)
ax_hod.annotate(str(int(corfidi_dn[0])) + '/' + str(int(corfidi_dn[1])) +
' DN', (corfidi[2] + 1.5, corfidi[3] - 1.5),
fontsize=12,
va="top",
ha="left",
color=cb_colors.purple8,
weight='bold',
zorder=10)
# Get the cloud-layer mean wind
mean_cloudlayer = winds.mean_wind(prof, pbot=pcl.lclpres, ptop=pcl.elpres)
mean_cloudlayer_comp = utils.comp2vec(mean_cloudlayer[0],
mean_cloudlayer[1])
try:
mean_cloudlayer_str = str(int(np.ma.around(
mean_cloudlayer_comp[0], 0))) + "/" + str(
int(np.ma.around(mean_cloudlayer_comp[1], 0)))
except:
mean_cloudlayer_str = 'M/M'
# Write the critical angle to the hodograph.
if eff_inflow[0] == prof.pres[prof.sfc]:
ax_hod.annotate('Critical Angle = ' + str(int(prof.critical_angle)),
(-65, -50),
fontsize=12,
va="bottom",
ha="left",
color=cb_colors.green6,
weight='bold',
zorder=10)
ax_hod.set_xlim(-80, 80)
ax_hod.set_ylim(-70, 70)
#BELOW IS STUFF FOR BOXES/BORDERS ******************************************************************************
ax3 = ax2.twinx()
ax3.axes.get_xaxis().set_visible(False)
ax3.axes.get_yaxis().set_visible(False)
ax3.set_yticks([])
ax3.set_yticklabels([])
#Big Thick Box around Skew-T
#box = ax3.add_patch(patches.Rectangle((-50, 0), 110.0, 1.0,fill=False,linewidth=2,edgecolor="w",zorder=3))
#box.set_clip_on(False)
#box around hodograph
#box = ax_hod.add_patch(patches.Rectangle((-80., -70.), 160., 140.,fill=False,linewidth=2,edgecolor="w",zorder=4))
#box.set_clip_on(False)
inset_color = cb_colors.gray7
#THICK TEXT BOX around Thermodynamics Text
box = ax3.add_patch(
patches.Rectangle((-58.0, -0.035),
54,
-0.12,
fill=False,
linewidth=2,
edgecolor=inset_color,
zorder=4))
box.set_clip_on(False)
#THICK TEXT BOX around Kinematics Text
box = ax3.add_patch(
patches.Rectangle((-3.0, -0.035),
60,
-0.12,
fill=False,
linewidth=2,
edgecolor=inset_color,
zorder=4))
box.set_clip_on(False)
#THICK TEXT BOX Around Dynamics Text
# box = ax3.add_patch(patches.Rectangle((9.0, -0.04), 60, -0.38,fill=False,linewidth=2,edgecolor=inset_color,zorder=4))
# box.set_clip_on(False)
#THICK TEXT BOX Around SARS Text
# box = ax3.add_patch(patches.Rectangle((69.0, -0.04), 55, -0.38,fill=False,linewidth=2,edgecolor=inset_color,zorder=4))
# box.set_clip_on(False)
#Thermodynamics
#box = ax3.add_patch(patches.Rectangle((-55.0, -0.04), 64.0, -0.12,fill=False,linewidth=1,edgecolor=inset_color,zorder=4))
#box.set_clip_on(False)
#box = ax3.add_patch(patches.Rectangle((-55.0, -0.04), 64.0, -0.025,fill=False,linewidth=1,edgecolor=inset_color,zorder=4))
#box.set_clip_on(False)
# Write the parcel properties to the inset.
#x_list = [0, 0.08, 0.17, 0.25, 0.33, 0.39, 0.47]
x_list = np.array([0, 0.08, 0.17, 0.25, 0.33, 0.39, 0.47]) - 0.075
y_list = [-0.045, -0.07, -0.0925, -0.115, -0.1375]
A = [
"SFC", prof.sfcpcl.bplus,
int(prof.sfcpcl.bminus), prof.sfcpcl.lclhght, prof.sfcpcl.li5,
prof.sfcpcl.lfchght, prof.sfcpcl.elhght
]
# B = ["FCST", prof.fcstpcl.bplus, int(prof.fcstpcl.bminus), prof.fcstpcl.lclhght, prof.fcstpcl.li5, prof.fcstpcl.lfchght, prof.fcstpcl.elhght]
C = [
"ML", prof.mlpcl.bplus,
int(prof.mlpcl.bminus), prof.mlpcl.lclhght, prof.mlpcl.li5,
prof.mlpcl.lfchght, prof.mlpcl.elhght
]
D = [
"MU", prof.mupcl.bplus,
int(prof.mupcl.bminus), prof.mupcl.lclhght, prof.mupcl.li5,
prof.mupcl.lfchght, prof.mupcl.elhght
]
#mlcape = C[1]
#print('mlcape',mlcape)
data = np.array([["PCL", "CAPE", "CINH", "LCL", "LI", "LFC", "EL"],
[
str(int(np.ma.around(A[i], 0))) if
(type(A[i]) == np.float64) else str(A[i])
for i in range(len(A))
],
[
str(int(np.ma.around(C[i], 0))) if
(type(C[i]) == np.float64) else str(C[i])
for i in range(len(C))
],
[
str(int(np.ma.around(D[i], 0))) if
(type(D[i]) == np.float64) else str(D[i])
for i in range(len(D))
]])
for i in range(data.shape[0]):
for j in range(data.shape[1]):
ax2.annotate(data[i, j], (x_list[j], y_list[i]),
xycoords="axes fraction",
fontsize=12,
va="top",
ha="left",
color=cb_colors.gray7,
weight='bold')
# Draw a box around the selected parcel being shown in the Skew-T
box = ax3.add_patch(
patches.Rectangle((-57.7, pcl_box_level),
53.,
0.0225,
fill=False,
linewidth=1,
edgecolor=cb_colors.purple4,
zorder=4))
box.set_clip_on(False)
# Write the lapse rates to the inset.
#box = ax3.add_patch(patches.Rectangle((-55.0, -0.305), 43.0, -0.115,fill=False,linewidth=1,edgecolor="w",zorder=4))
#box.set_clip_on(False)
x_list = [0.15, 0.16]
y_list = np.arange(-0.315, -.40, -0.0225)
data = np.array([[
"0-3km AGL LR =",
str(np.ma.around(prof.lapserate_3km, 1)) + " C/km"
], [
"3-6km AGL LR =",
str(np.ma.around(prof.lapserate_3_6km, 1)) + " C/km"
],
[
"850-500mb LR =",
str(np.ma.around(prof.lapserate_850_500, 1)) + " C/km"
],
[
"700-500mb LR =",
str(np.ma.around(prof.lapserate_700_500, 1)) + " C/km"
]])
for i in range(data.shape[0]):
for j in range(data.shape[1]):
if (j % 2 == 0):
ax2.annotate(data[i, j], (x_list[j], y_list[i]),
xycoords="axes fraction",
fontsize=12,
va="top",
ha="right",
color='k',
weight='bold')
else:
ax2.annotate(data[i, j], (x_list[j], y_list[i]),
xycoords="axes fraction",
fontsize=12,
va="top",
ha="left",
color='k',
weight='bold')
#Severe Indices
#box = ax3.add_patch(patches.Rectangle((-12.0, -0.305), 21.0, -0.115,fill=False,linewidth=1,edgecolor="w",zorder=4))
#box.set_clip_on(False)
x_list = [0.52, 0.53]
y_list = np.arange(-0.315, -.40, -0.0225)
# This looks lifted from the Profile class. Don't need this.
sfc = prof.pres[prof.sfc]
p6km = sharptab.interp.pres(prof, sharptab.interp.to_msl(prof, 6000.))
p8km = sharptab.interp.pres(prof, sharptab.interp.to_msl(prof, 8000.))
# ebot_hght = interp.to_agl(prof, interp.hght(prof, eff_inflow[0]))
# etop_hght = interp.to_agl(prof, interp.hght(prof, eff_inflow[1]))
# Mean winds
#mean_1km = winds.mean_wind(prof, pbot=sfc, ptop=p1km)
mean_1km_comp = prof.mean_1km #utils.comp2vec(mean_1km[0],mean_1km[1])
#mean_3km = winds.mean_wind(prof, pbot=sfc, ptop=p3km)
mean_3km_comp = prof.mean_3km #utils.comp2vec(mean_3km[0],mean_3km[1])
#mean_eff = winds.mean_wind(prof, pbot=eff_inflow[0], ptop=eff_inflow[1])
mean_eff_comp = utils.comp2vec(
*prof.mean_eff) #utils.comp2vec(mean_eff[0],mean_eff[1])
if type(eff_inflow[0]) != np.float64:
mean_eff_comp = ['---', '--']
mean_6km = winds.mean_wind(prof, pbot=sfc, ptop=p6km)
mean_6km_comp = prof.mean_6km #utils.comp2vec(mean_6km[0],mean_6km[1])
mean_8km = winds.mean_wind(prof, pbot=sfc, ptop=p8km)
mean_8km_comp = prof.mean_8km #utils.comp2vec(mean_8km[0],mean_8km[1])
mean_cloudlayer = winds.mean_wind(prof, pbot=pcl.lclpres, ptop=pcl.elpres)
mean_cloudlayer_comp = prof.mean_lcl_el #utils.comp2vec(mean_cloudlayer[0],mean_cloudlayer[1])
mean_ebwd = winds.mean_wind(prof, pbot=eff_inflow[0], ptop=eff_inflow[1])
mean_ebwd_comp = utils.comp2vec(
*prof.mean_ebw) #utils.comp2vec(mean_ebwd[0],mean_ebwd[1])
if type(eff_inflow[0]) != np.float64:
mean_ebwd_comp = ['---', '--']
bunkers_rt = utils.comp2vec(bunkers[0], bunkers[1])
bunkers_lf = utils.comp2vec(bunkers[2], bunkers[3])
corfidi_up = utils.comp2vec(corfidi[0], corfidi[1])
corfidi_dn = utils.comp2vec(corfidi[2], corfidi[3])
srw_1km = prof.srw_1km #utils.comp2vec(*winds.sr_wind(prof, pbot=sfc, ptop=p1km, stu=prof.srwind[0], stv=prof.srwind[1]))
srw_3km = prof.srw_3km #utils.comp2vec(*winds.sr_wind(prof, pbot=sfc, ptop=p3km, stu=prof.srwind[0], stv=prof.srwind[1]))
srw_eff = utils.comp2vec(
*prof.srw_eff
) #utils.comp2vec(*winds.sr_wind(prof, pbot=eff_inflow[0], ptop=eff_inflow[1], stu=prof.srwind[0], stv=prof.srwind[1]))
if type(eff_inflow[0]) != np.float64:
srw_eff = ['---', '--']
srw_6km = prof.srw_6km #utils.comp2vec(*winds.sr_wind(prof, pbot=sfc, ptop=p6km, stu=prof.srwind[0], stv=prof.srwind[1]))
srw_8km = prof.srw_8km #utils.comp2vec(*winds.sr_wind(prof, pbot=sfc, ptop=p8km, stu=prof.srwind[0], stv=prof.srwind[1]))
srw_cloudlayer = prof.srw_lcl_el #utils.comp2vec(*winds.sr_wind(prof, pbot=pcl.lclpres, ptop=pcl.elpres, stu=prof.srwind[0], stv=prof.srwind[1]))
srw_ebwd = utils.comp2vec(
*prof.srw_ebw
) #utils.comp2vec(*winds.sr_wind(prof, pbot=eff_inflow[0], ptop=eff_inflow[1], stu=prof.srwind[0], stv=prof.srwind[1]))
if type(eff_inflow[0]) != np.float64:
srw_ebwd = ['---', '--']
srw_46km = utils.comp2vec(
*prof.srw_4_6km
) #utils.comp2vec(*winds.sr_wind(prof, pbot=p4km, ptop=p6km, stu=prof.srwind[0], stv=prof.srwind[1]))
sfc_8km_shear = prof.sfc_8km_shear #winds.wind_shear(prof, pbot=sfc, ptop=p8km)
sfc_6km_shear = prof.sfc_6km_shear #winds.wind_shear(prof, pbot=sfc, ptop=p6km)
sfc_3km_shear = prof.sfc_3km_shear #winds.wind_shear(prof, pbot=sfc, ptop=p3km)
sfc_1km_shear = prof.sfc_1km_shear #winds.wind_shear(prof, pbot=sfc, ptop=p1km)
effective_shear = prof.eff_shear #winds.wind_shear(prof, pbot=eff_inflow[0], ptop=etop_hght)
cloudlayer_shear = prof.lcl_el_shear #winds.wind_shear(prof,pbot= pcl.lclpres, ptop=pcl.elpres)
srh3km = prof.srh3km #winds.helicity(prof, 0, 3000., stu = bunkers[0], stv = bunkers[1])
srh1km = prof.srh1km #winds.helicity(prof, 0, 1000., stu = bunkers[0], stv = bunkers[1])
stp_fixed = prof.stp_fixed #params.stp_fixed(pcl.bplus, pcl.lclhght, srh1km[0], utils.comp2vec(sfc_6km_shear[0], sfc_6km_shear[1])[1])
ship = prof.ship
effective_srh = prof.right_esrh #winds.helicity(prof, ebot_hght, etop_hght, stu = bunkers[0], stv = bunkers[1])
ebwd = prof.ebwd #winds.wind_shear(prof, pbot=eff_inflow[0], ptop=eff_inflow[1])
ebwspd = prof.ebwspd
scp = prof.right_scp
stp_cin = prof.stp_cin #params.stp_cin(pcl.bplus, effective_srh[0], ebwspd, pcl.lclhght, pcl.bminus)
brn_shear = pcl.brnshear
# Draw the SCP, STP, SHIP indices to the plot
# TODO: Include the color variations on this to denote intensity of the index.
# if prof.stp_fixed is ma.masked:
# temp_stp_fixed = '0.0'
# else:
# temp_stp_fixed = str(round(prof.stp_fixed,1))
# if prof.stp_cin is ma.masked:
# temp_stp_cin = '0.0'
# else:
# temp_stp_cin = str(round(prof.stp_cin,1))
# if prof.right_scp is ma.masked:
# temp_right_scp = '0.0'
# else:
# temp_right_scp = str(round(prof.right_scp,1))
# data = np.array([["Supercell =",temp_right_scp],
# ["STP (cin) =",temp_stp_cin],
# ["STP (fix) =",temp_stp_fixed]])
data = np.array([["Supercell =",
str(np.ma.around(prof.right_scp, 1))],
["STP (cin) =",
str(np.ma.around(prof.stp_cin, 1))],
["STP (fix) =",
str(np.ma.around(prof.stp_fixed, 1))]])
data = np.array([["Supercell =",
str(np.ma.around(prof.right_scp, 1))],
["STP (cin) =",
str(np.ma.around(prof.stp_cin, 1))],
["STP (fix) =",
str(np.ma.around(prof.stp_fixed, 1))],
["SHIP =", str(np.ma.around(prof.ship, 1))]])
'''
for i in range(data.shape[0]):
for j in range(data.shape[1]):
d = float(data[i,1])
if i == 0:
if d >= 19.95:
c = MAGENTA
elif d >= 11.95:
c = RED
elif d >= 1.95:
c = YELLOW
elif d >= .45:
c = WHITE
elif d >= -.45:
c = LBROWN
elif d < -0.45:
c = CYAN
elif i == 1:
if d >= 8:
c = MAGENTA
elif d >= 4:
c = RED
elif d >= 2:
c = YELLOW
elif d >= 1:
c = WHITE
elif d >= .5:
c = LBROWN
elif d < .5:
c = DBROWN
stpCinColor = c
elif i == 2:
if d >= 7:
c = MAGENTA
elif d >= 5:
c = RED
elif d >= 2:
c = YELLOW
elif d >= 1:
c = WHITE
elif d >= 0.5:
c = LBROWN
else:
c = DBROWN
elif i == 3:
if d >= 5:
c = MAGENTA
elif d >= 2:
c = RED
elif d >= 1:
c = YELLOW
elif d >= .5:
c = WHITE
else:
c = DBROWN
if (j % 2 == 0):
ax2.annotate(data[i,j], (x_list[j], y_list[i]), xycoords="axes fraction",
fontsize=12, va="top", ha="right", color=c, weight='bold')
else:
ax2.annotate(data[i,j], (x_list[j], y_list[i]), xycoords="axes fraction",
fontsize=12, va="top", ha="left", color=cb_colors.gray7, weight='bold')
'''
# Draw the kinematic inset on the plot
# box = ax3.add_patch(patches.Rectangle((9.0, -0.04), 60.0, -0.025,fill=False,linewidth=1,edgecolor="w",zorder=4))
# box.set_clip_on(False)
x_list = np.array([0.60, 0.84, 0.97, 1.08, 1.18]) - 0.12
y_list = [-0.045]
y_list.extend(np.arange(-.07, -0.12, -.0225).tolist())
y_list.extend(np.arange(-.145, -0.22, -.0225).tolist())
y_list.extend(np.arange(-.2425, -0.27, -.0225).tolist())
y_list.extend(np.arange(-0.295, -.4, -0.0225).tolist())
A = [
"SFC-1km", srh1km[0],
utils.comp2vec(sfc_1km_shear[0], sfc_1km_shear[1])[1]
]
A2 = [mean_1km_comp, srw_1km]
B = [
"SFC-3km", srh3km[0],
utils.comp2vec(sfc_3km_shear[0], sfc_3km_shear[1])[1]
]
B2 = [mean_3km_comp, srw_3km]
C = [
"Eff Inflow Layer", effective_srh[0],
utils.comp2vec(effective_shear[0], effective_shear[1])[1]
]
C2 = [mean_eff_comp, srw_eff]
# D = ["SFC-6km", "", utils.comp2vec(sfc_6km_shear[0],sfc_6km_shear[1])[1]]
# D2 = [mean_6km_comp, srw_6km]
# E = ["SFC-8km", "", utils.comp2vec(sfc_8km_shear[0],sfc_8km_shear[1])[1]]
# E2 = [mean_8km_comp, srw_8km]
# F = ["LCL-EL (CLoud Layer)", "", utils.comp2vec(cloudlayer_shear[0],cloudlayer_shear[1])[1]]
# F2 = [mean_cloudlayer_comp, srw_cloudlayer]
# G = ["Eff Shear (EBWD)", "", utils.comp2vec(ebwd[0],ebwd[1])[1]]
# G2 = [mean_ebwd_comp, srw_ebwd]
# H = ["BRN Shear (m2/s2)", "", brn_shear, "", ""]
# I = ["4-6km SR Wind", ""]
# I2 = [str(int(round(srw_46km[0],0)))+"/"+str(int(round(srw_46km[1],0)))]
# I3 = ["", ""]
# J = ["...Storm Motion Vectors...", "", "", "", ""]
# K = ["Bunkers Right", ""]
# K2 = [str(int(round(bunkers_rt[0],0)))+"/"+str(int(round(bunkers_rt[1],0)))]
# K3 = ["", ""]
# L = ["Bunkers Left", ""]
# L2 = [str(int(round(bunkers_lf[0],0)))+"/"+str(int(round(bunkers_lf[1],0)))]
# L3 = ["", ""]
# M = ["Corfidi Downshear", ""]
# M2 = [str(int(round(corfidi_dn[0],0)))+"/"+str(int(round(corfidi_dn[1],0)))]
# M3 = ["", ""]
# N = ["Corfidi Upshear", ""]
# N2 = [str(int(round(corfidi_up[0],0)))+"/"+str(int(round(corfidi_up[1],0)))]
# N3 = ["", ""]
data = np.array([np.array(["", "SRH (m2/s2)", "Shear (kt)", "MnWind", "SRW"]),
np.array([ str(int(round(A[i],0))) if (type(A[i])== np.float64) else str(A[i]) for i in range(len(A)) ]+\
[ str(int(np.ma.around(A2[i][0],0)))+"/"+str(int(np.ma.around(A2[i][1],0))) if (type(A2[i][0])== np.ma.core.MaskedArray) else str(A2[i][0])+"/"+str(A2[i][1]) for i in range(len(A2)) ]),
np.array([ str(int(round(B[i],0))) if (type(B[i])== np.float64) else str(B[i]) for i in range(len(B)) ]+\
[ str(int(np.ma.around(B2[i][0],0)))+"/"+str(int(np.ma.around(B2[i][1],0))) if (type(B2[i][0])== np.ma.core.MaskedArray) else str(B2[i][0])+"/"+str(B2[i][1]) for i in range(len(B2)) ]),
np.array([ str(int(np.ma.around(C[i],0))) if (type(C[i])== np.float64) else str(C[i]) for i in range(len(C)) ]+\
[ str(int(np.ma.around(C2[i][0],0)))+"/"+str(int(np.ma.around(C2[i][1],0))) if (type(C2[i][0])== np.ma.core.MaskedArray) else str(C2[i][0])+"/"+str(C2[i][1]) for i in range(len(C2)) ])]) #,
# np.array([ str(int(round(D[i],0))) if (type(D[i])== np.float64) else str(D[i]) for i in range(len(D)) ]+\
# [ str(int(round(D2[i][0],0)))+"/"+str(int(round(D2[i][1],0))) if (type(D2[i][0])== np.ma.core.MaskedArray) else str(D2[i][0])+"/"+str(D2[i][1]) for i in range(len(D2)) ]),
# np.array([ str(int(round(E[i],0))) if (type(E[i])== np.float64) else str(E[i]) for i in range(len(E)) ]+\
# [ str(int(round(E2[i][0],0)))+"/"+str(int(round(E2[i][1],0))) if (type(E2[i][0])== np.ma.core.MaskedArray) else str(E2[i][0])+"/"+str(E2[i][1]) for i in range(len(E2)) ]),
# np.array([ str(int(round(F[i],0))) if (type(F[i])== np.float64) else str(F[i]) for i in range(len(F)) ]+\
# [ str(int(round(F2[i][0],0)))+"/"+str(int(round(F2[i][1],0))) if (type(F2[i][0])== np.ma.core.MaskedArray) else str(F2[i][0])+"/"+str(F2[i][1]) for i in range(len(F2)) ]),
# np.array([ str(int(round(G[i],0))) if (type(G[i])== np.float64) else str(G[i]) for i in range(len(G)) ]+\
# [ str(int(round(G2[i][0],0)))+"/"+str(int(round(G2[i][1],0))) if (type(G2[i][0])== np.ma.core.MaskedArray) else str(G2[i][0])+"/"+str(G2[i][1]) for i in range(len(G2)) ]),
# np.array([ str(int(round(H[i],0))) if (type(H[i])== np.float64) else str(H[i]) for i in range(len(H)) ]),
# np.array(I+I2+I3),
# np.array(J),
# np.array(K+K2+K3),
# np.array(L+L2+L3),
# np.array(M+M2+M3),
# np.array(N+N2+N3)])
#x_list = np.array([0, 0.08, 0.17, 0.25, 0.33, 0.39, 0.47])-0.05
y_list = [-0.045, -0.07, -0.0925, -0.115, -0.1375]
for i in range(data.shape[0]):
for j in range(data[0].shape[0]):
if j > 0:
ax2.annotate(data[i][j], (x_list[j], y_list[i]),
xycoords="axes fraction",
fontsize=12,
va="top",
ha="right",
color=cb_colors.gray7,
weight='bold')
else:
ax2.annotate(data[i][j], (x_list[j], y_list[i]),
xycoords="axes fraction",
fontsize=12,
va="top",
ha="left",
color=cb_colors.gray7,
weight='bold')
# wind_1km = utils.vec2comp(prof.wind1km[0], prof.wind1km[1])
# wind_6km = utils.vec2comp(prof.wind6km[0], prof.wind6km[1])
# wind_barbs = ax2.barbs(58, 2200, wind_1km[0], wind_1km[1], color='#AA0000', zorder=3, lw=1.25,length=9)
# wind_barbs.set_clip_on(False)
# wind_barbs = ax2.barbs(58, 2200, wind_6km[0], wind_6km[1], color='#0A74C6', zorder=3, lw=1.25,length=9)
# wind_barbs.set_clip_on(False)
# ax2.annotate("1km & 6km AGL\nWind Barbs", (1.08,-0.37), xycoords="axes fraction", fontsize=12, va="top", ha="center", color='#0A74C6', weight='bold')
# Draw the CAPE vs. SRH Scatter
#ax_EFSTP = fig.add_axes([0.74625, 0.0229, 0.20375, 0.2507], frameon=False)
#x_EFSTP = fig.add_axes([0.72625, 0.0229, 0.20375, 0.2507], frameon=False)
ax_EFSTP = fig.add_axes([0.625, 0.05, 0.35, 0.3], frameon=False)
for member_no, c in zip(
np.arange(1, 19, 1),
np.tile([
cb_colors.orange6, cb_colors.orange6, cb_colors.green6,
cb_colors.green6, cb_colors.purple6, cb_colors.purple6
], (3, 1))):
memidx = [0, 10, 11, 12, 13, 14, 15, 16, 17, 1, 2, 3, 4, 5, 6, 7, 8, 9]
ax_EFSTP.scatter(xpts[memidx[member_no - 1], :, :].ravel(),
ypts[memidx[member_no - 1], :, :].ravel(),
color=c,
marker='o',
s=5,
alpha=0.7)
ax_EFSTP.set_xticks(np.arange(0, 5001, 1000))
ax_EFSTP.set_yticks(np.arange(0, 601, 100))
#ax_EFSTP.set_xticklabels(np.arange(0,5001,1000),color='k',fontsize=12)
#ax_EFSTP.set_yticklabels(np.arange(0,601,100),color='k',fontsize=12)
ax_EFSTP.set_xlabel('MLCAPE', weight='bold', fontsize=14)
ax_EFSTP.set_ylabel('0-1km SRH', weight='bold', fontsize=14)
ax_EFSTP.tick_params(axis='both', labelsize=12, labelcolor='k')
ax_EFSTP.set_xlim(-200, 5000)
ax_EFSTP.set_ylim(-100, 600)
#ax_EFSTP.tick_params(direction='in', axis='x', which='major', colors=cb_colors.gray4,length=0,width=1.5,size=12)#pad=-10
#ax_EFSTP.tick_params(direction='in', axis='y', which='major', colors=cb_colors.gray4,length=0,width=1.5,size=12)#pad=-23
ax_EFSTP.grid(color=cb_colors.gray4,
linestyle='--',
dashes=(3, 3),
alpha=0.75,
zorder=0,
linewidth=1.25)
ax_EFSTP.text(0.8,
0.95,
'YSU',
color=cb_colors.orange6,
transform=ax_EFSTP.transAxes,
fontsize=16,
weight='bold')
ax_EFSTP.text(0.8,
0.89,
'MYJ',
color=cb_colors.green6,
transform=ax_EFSTP.transAxes,
fontsize=16,
weight='bold')
ax_EFSTP.text(0.8,
0.83,
'MYNN',
color=cb_colors.purple6,
transform=ax_EFSTP.transAxes,
fontsize=16,
weight='bold')
box = ax_EFSTP.add_patch(
patches.Rectangle((0, -1),
13,
13,
fill=False,
linewidth=2,
edgecolor=cb_colors.gray4,
zorder=10))
box.set_clip_on(False)
ax_EFSTP.annotate("0-1 km SRH vs. 100-mb MLCAPE", (0.5, 1.075),
xycoords="axes fraction",
fontsize=14,
va="center",
ha="center",
color=cb_colors.gray7,
weight='bold')
ax_EFSTP.annotate("(9 km neighborhood)", (0.5, 1.025),
xycoords="axes fraction",
fontsize=12,
va="center",
ha="center",
color=cb_colors.gray7,
weight='bold')
plt.savefig(figname, facecolor=fig.get_facecolor()) #, edgecolor=None)
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)
#### Adding a Parcel Trace
sfcpcl = params.parcelx(prof, flag=1) # Surface Parcel
#fcstpcl = params.parcelx( prof, flag=2 ) # Forecast Parcel
mupcl = params.parcelx(prof, flag=3) # Most-Unstable Parcel
mlpcl = params.parcelx(prof, flag=4) # 100 mb Mean Layer Parcel
# Set the parcel trace to be plotted as the Most-Unstable parcel.
pcl = mupcl
# Temperature, dewpoint, virtual temperature, wetbulb, parcel profiles
temperature_trace, = skew.plot(prof.pres, prof.tmpc, 'r',
linewidth=2) # temperature profile
# 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())
vtemp_trace, = skew.plot(prof.pres, prof.vtmp, 'r',
linewidth=0.5) # Virtual temperature profile
wetbulb_trace, = skew.plot(prof.pres, prof.wetbulb,
'c-') # wetbulb profile
dewpoint_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',
