def get_sars(self):
'''
Function to get the SARS analogues from the hail and
supercell databases. Requires calling get_kinematics()
and get_parcels() first. Also calculates the significant
hail parameter.
Function returns nothing, but sets the following variables:
self.matches - the matches from SARS HAIL
self.ship - significant hail parameter
self.supercell_matches - the matches from SARS SUPERCELL
Parameters
----------
None
Returns
-------
None
'''
sfc_6km_shear = utils.KTS2MS(
utils.mag(self.sfc_6km_shear[0], self.sfc_6km_shear[1]))
sfc_3km_shear = utils.KTS2MS(
utils.mag(self.sfc_3km_shear[0], self.sfc_3km_shear[1]))
sfc_9km_shear = utils.KTS2MS(
utils.mag(self.sfc_9km_shear[0], self.sfc_9km_shear[1]))
h500t = interp.temp(self, 500.)
lapse_rate = params.lapse_rate(self, 700., 500., pres=True)
srh3km = self.srh3km[0]
srh1km = self.srh1km[0]
mucape = self.mupcl.bplus
mlcape = self.mlpcl.bplus
mllcl = self.mlpcl.lclhght
mumr = thermo.mixratio(self.mupcl.pres, self.mupcl.dwpc)
self.ship = params.ship(self)
## Cambios para el hemisferio sur JP JP
if self.latitude < 0:
srh1km = -srh1km
srh3km = -srh3km
self.hail_database = 'sars_hail.txt'
self.supercell_database = 'sars_supercell.txt'
try:
self.matches = hail(self.hail_database, mumr, mucape, h500t,
lapse_rate, sfc_6km_shear, sfc_9km_shear,
sfc_3km_shear, srh3km)
except:
self.matches = ([], [], 0, 0, 0)
try:
self.supercell_matches = supercell(self.supercell_database, mlcape,
mllcl, h500t, lapse_rate,
utils.MS2KTS(sfc_6km_shear),
srh1km,
utils.MS2KTS(sfc_3km_shear),
utils.MS2KTS(sfc_9km_shear),
srh3km)
except Exception as e:
self.supercell_matches = ([], [], 0, 0, 0)
def non_parcel_bunkers_motion_experimental(prof):
'''
Compute the Bunkers Storm Motion for a Right Moving Supercell
Parameters
----------
prof : profile object
Profile Object
Returns
-------
rstu : number
Right Storm Motion U-component (kts)
rstv : number
Right Storm Motion V-component (kts)
lstu : number
Left Storm Motion U-component (kts)
lstv : number
Left Storm Motion V-component (kts)
'''
if prof.wdir.count() == 0:
return ma.masked, ma.masked, ma.masked, ma.masked
d = utils.MS2KTS(7.5) # Deviation value emperically derived as 7.5 m/s
## get the msl height of 500m, 5.5km, and 6.0km above the surface
msl500m = interp.to_msl(prof, 500.)
msl5500m = interp.to_msl(prof, 5500.)
msl6000m = interp.to_msl(prof, 6000.)
## get the pressure of the surface, 500m, 5.5km, and 6.0km levels
psfc = prof.pres[prof.sfc]
p500m = interp.pres(prof, msl500m)
p5500m = interp.pres(prof, msl5500m)
p6000m = interp.pres(prof, msl6000m)
## sfc-500m Mean Wind
mnu500m, mnv500m = mean_wind(prof, psfc, p500m)
## 5.5km-6.0km Mean Wind
mnu5500m_6000m, mnv5500m_6000m = mean_wind(prof, p5500m, p6000m)
# shear vector of the two mean winds
shru = mnu5500m_6000m - mnu500m
shrv = mnv5500m_6000m - mnv500m
# SFC-6km Mean Wind
mnu6, mnv6 = mean_wind(prof, psfc, p6000m)
# Bunkers Right Motion
tmp = d / utils.mag(shru, shrv)
rstu = mnu6 + (tmp * shrv)
rstv = mnv6 - (tmp * shru)
lstu = mnu6 - (tmp * shrv)
lstv = mnv6 + (tmp * shru)
return rstu, rstv, lstu, lstv
def non_parcel_bunkers_motion(prof):
'''
Compute the Bunkers Storm Motion for a Right Moving Supercell
Inputs
------
prof : profile object
Profile Object
Returns
-------
rstu : number
Right Storm Motion U-component
rstv : number
Right Storm Motion V-component
lstu : number
Left Storm Motion U-component
lstv : number
Left Storm Motion V-component
'''
d = utils.MS2KTS(7.5) # Deviation value emperically derived as 7.5 m/s
msl6km = interp.to_msl(prof, 6000.)
p6km = interp.pres(prof, msl6km)
# SFC-6km Mean Wind
mnu6, mnv6 = mean_wind_npw(prof, prof.pres[prof.sfc], p6km)
# SFC-6km Shear Vector
shru, shrv = wind_shear(prof, prof.pres[prof.sfc], p6km)
# Bunkers Right Motion
tmp = d / utils.mag(shru, shrv)
# Cambios para el hemisferio sur JP JP
if prof.latitude < 0:
lstu = mnu6 + (tmp * shrv)
lstv = mnv6 - (tmp * shru)
rstu = mnu6 - (tmp * shrv)
rstv = mnv6 + (tmp * shru)
else:
rstu = mnu6 + (tmp * shrv)
rstv = mnv6 - (tmp * shru)
lstu = mnu6 - (tmp * shrv)
lstv = mnv6 + (tmp * shru)
return rstu, rstv, lstu, lstv
def parseCLS(sfile):
## read in the file
data = np.array([l.strip() for l in sfile.split('\n')])
latitude = float(data[3].split(',')[5])
longitude = float(data[3].split(',')[4])
## necessary index points
start_idx = 15
finish_idx = len(data)
## put it all together for StringIO
full_data = '\n'.join(data[start_idx:finish_idx][:])
sound_data = StringIO(full_data)
## read the data into arrays
data = np.genfromtxt(sound_data)
clean_data = []
for i in data:
if i[1] != 9999 and i[2] != 999 and i[3] != 999 and i[7] != 999 and i[
8] != 999 and i[14] != 99999:
clean_data.append(i)
p = np.array([i[1] for i in clean_data])
h = np.array([i[14] for i in clean_data])
T = np.array([i[2] for i in clean_data])
Td = np.array([i[3] for i in clean_data])
wdir = np.array([i[8] for i in clean_data])
wspd = np.array([i[7] for i in clean_data])
wspd = utils.MS2KTS(wspd)
wdir[
wdir ==
360] = 0. # Some wind directions are 360. Like in /glade/p/work/ahijevyc/GFS/Joaquin/g132325165.frd
max_points = 250
s = p.size / max_points
if s == 0: s = 1
print("stride=", s)
return p[::s], h[::s], T[::s], Td[::
s], wdir[::
s], wspd[::
s], latitude, longitude
def non_parcel_bunkers_motion(prof):
'''
Compute the Bunkers Storm Motion for a Right Moving Supercell
Parameters
----------
prof : profile object
Profile Object
Returns
-------
rstu : number
Right Storm Motion U-component (kts)
rstv : number
Right Storm Motion V-component (kts)
lstu : number
Left Storm Motion U-component (kts)
lstv : number
Left Storm Motion V-component (kts)
'''
if prof.wdir.count() == 0:
return ma.masked, ma.masked, ma.masked, ma.masked
d = utils.MS2KTS(7.5) # Deviation value emperically derived as 7.5 m/s
msl6km = interp.to_msl(prof, 6000.)
p6km = interp.pres(prof, msl6km)
# SFC-6km Mean Wind
mnu6, mnv6 = mean_wind_npw(prof, prof.pres[prof.sfc], p6km)
# SFC-6km Shear Vector
shru, shrv = wind_shear(prof, prof.pres[prof.sfc], p6km)
# Bunkers Right Motion
tmp = d / utils.mag(shru, shrv)
rstu = mnu6 + (tmp * shrv)
rstv = mnv6 - (tmp * shru)
lstu = mnu6 - (tmp * shrv)
lstv = mnv6 + (tmp * shru)
return rstu, rstv, lstu, lstv
''' Create the Sounding (Profile) Object '''
def _parse(self):
"""
Parse the netCDF file according to the variable naming and
dimmensional conventions of the WRF-ARW.
"""
## open the file and also store the lat/lon of the selected point
file_data = self._downloadFile()
gridx = self._file_name[1]
gridy = self._file_name[2]
## calculate the nearest grid point to the map point
idx = self._find_nearest_point(file_data, gridx, gridy)
## check to see if this is a 4D netCDF4 that includes all available times.
## If it does, open and compute the variables as 4D variables
if len(file_data.variables["T"][:].shape) == 4:
## read in the data from the WRF file and conduct necessary processing
theta = file_data.variables["T"][:, :, idx[0], idx[1]] + 300.0
qvapr = file_data.variables["QVAPOR"][:, :, idx[0],
idx[1]] * 10**3 #g/kg
mpres = (file_data.variables["P"][:, :, idx[0], idx[1]] +
file_data.variables["PB"][:, :, idx[0], idx[1]]) * .01
mhght = file_data.variables[
"PH"][:, :, idx[0],
idx[1]] + file_data.variables["PHB"][:, :, idx[0],
idx[1]] / G
## unstagger the height grid
mhght = (mhght[:, :-1, :, :] + mhght[:, 1:, :, :]) / 2.
muwin = file_data.variables["U"][:, :, idx[0], idx[1]]
mvwin = file_data.variables["V"][:, :, idx[0], idx[1]]
## convert the potential temperature to air temperature
mtmpc = thermo.theta(1000.0, theta - 273.15, p2=mpres)
## convert the mixing ratio to dewpoint
mdwpc = thermo.temp_at_mixrat(qvapr, mpres)
## convert the grid relative wind to earth relative
U = muwin * file_data.variables['COSALPHA'][
0, idx[0], idx[1]] - mvwin * file_data.variables['SINALPHA'][
0, idx[0], idx[1]]
V = mvwin * file_data.variables['COSALPHA'][
0, idx[0], idx[1]] + muwin * file_data.variables['SINALPHA'][
0, idx[0], idx[1]]
## convert from m/s to kts
muwin = utils.MS2KTS(U)
mvwin = utils.MS2KTS(V)
## if the data is not 4D, then it must be assumed that this is a file containing only a single time
else:
## read in the data from the WRF file and conduct necessary processing
theta = file_data.variables["T"][:, idx[0], idx[1]] + 300.0
qvapr = file_data.variables["QVAPOR"][:, idx[0],
idx[1]] * 10**3 #g/kg
mpres = (file_data.variables["P"][:, idx[0], idx[1]] +
file_data.variables["PB"][:, idx[0], idx[1]]) * .01
mhght = file_data.variables["PH"][:, idx[0],
idx[1]] + file_data.variables[
"PHB"][:, idx[0], idx[1]] / G
## unstagger the height grid
mhght = (mhght[:-1, :, :] + mhght[1:, :, :]) / 2.
muwin = file_data.variables["U"][:, idx[0], idx[1]]
mvwin = file_data.variables["V"][:, idx[0], idx[1]]
## convert the potential temperature to air temperature
mtmpc = thermo.theta(1000.0, theta - 273.15, p2=mpres)
## convert the mixing ratio to dewpoint
mdwpc = thermo.temp_at_mixrat(qvapr, mpres)
## convert the grid relative wind to earth relative
U = muwin * file_data.variables['COSALPHA'][
0, idx[0], idx[1]] - mvwin * file_data.variables['SINALPHA'][
0, idx[0], idx[1]]
V = mvwin * file_data.variables['COSALPHA'][
0, idx[0], idx[1]] + muwin * file_data.variables['SINALPHA'][
0, idx[0], idx[1]]
## convert from m/s to kts
muwin = utils.MS2KTS(U)
mvwin = utils.MS2KTS(V)
## get the model start time of the file
inittime = dattim.datetime.strptime(str(file_data.START_DATE),
'%Y-%m-%d_%H:%M:%S')
profiles = []
dates = []
## loop over the available times
for i in range(file_data.variables["T"][:].shape[0]):
## make sure the arrays are 1D
prof_pres = mpres[i].flatten()
prof_hght = mhght[i].flatten()
prof_tmpc = mtmpc[i].flatten()
prof_dwpc = mdwpc[i].flatten()
prof_uwin = muwin[i].flatten()
prof_vwin = mvwin[i].flatten()
## compute the time of the profile
try:
delta = dattim.timedelta(
minutes=int(file_data.variables["XTIME"][i]))
curtime = inittime + delta
except KeyError:
var = ''.join(
np.asarray(file_data.variables['Times'][i], dtype=str))
curtime = dattim.datetime.strptime(var, '%Y-%m-%d_%H:%M:%S')
date_obj = curtime
## construct the profile object
prof = profile.create_profile(profile="raw",
pres=prof_pres,
hght=prof_hght,
tmpc=prof_tmpc,
dwpc=prof_dwpc,
u=prof_uwin,
v=prof_vwin,
location=str(gridx) + "," +
str(gridy),
date=date_obj,
missing=-999.0,
latitude=gridy,
strictQC=False)
## append the dates and profiles
profiles.append(prof)
dates.append(date_obj)
## create a profile collection - dictionary has no key since this
## is not an ensemble model
prof_coll = prof_collection.ProfCollection({'': profiles}, dates)
return prof_coll
