def getVertVars(self):
u = wrf.unstaggerX(np.squeeze(self.readNCVariable('U')))
v = wrf.unstaggerY(np.squeeze(self.readNCVariable('V')))
w = wrf.unstaggerZ(np.squeeze(self.readNCVariable('W')))
ph = np.squeeze(self.readNCVariable('PH'))
phb = np.squeeze(self.readNCVariable('PHB'))
p = np.squeeze(self.readNCVariable('P'))
pb = np.squeeze(self.readNCVariable('PB'))
press = (p + pb) / 100.
height = wrf.unstaggerZ((ph + phb) / 9.81) / 1000.
return u, v, w, press, height
def shear0_6km(self):
#get the height array in reference to ground level
height = wrf.unstaggerZ(self.height) - wrf.unstaggerZ(self.height)[0,:,:]
u = self.dataSet.readNCVariable('U')
v = self.dataSet.readNCVariable('V')
ref_val = 6000. # meters
self.u10, self.v10, var1 = wrf.get_shear(u, v, height, ref_val)
self.var = wrf.convertWind_MStoKT(var1)
self.var2 = self.var
self.varTitle = "0-6km Shear (knots)\n" + self.dataSet.getTime()
#Set short variable title for time series
self.sTitle = "0-6km Shear (knots)"
def bulk_rich(self):
u = self.dataSet.readNCVariable('U')
v = self.dataSet.readNCVariable('V')
qv = self.dataSet.readNCVariable('QVAPOR')
u_corr = wrf.unstaggerX(u)
v_corr = wrf.unstaggerY(v)
tv = self.temp*(1+0.611*qv)
thetav = self.theta*(1+0.611*qv)
height = wrf.unstaggerZ(self.height)
#compute the vertical gradient of theta
dtheta = np.gradient(thetav)[0]
dz = np.gradient(height)[0]
du = np.gradient(u_corr)[0]
dv = np.gradient(v_corr)[0]
print(np.min(dtheta),np.max(dtheta),np.min(dz),np.max(dz))
print(np.mean(du),np.mean(dv))
print(np.min(du),np.max(du),np.min(dv),np.max(dv))
#compute bulk richardson number
self.u10 = u_corr
self.v10 = v_corr
self.var = ((self.g/tv)*(dtheta)*dz)/((du)**2+(dv)**2)
self.var2 = self.var
self.varTitle = 'Bulk Richardson number\n' + self.dataSet.getTime()
#Set short variable title for time series
self.sTitle = 'Bulk Richardson number'
def froude(self):
u = self.dataSet.readNCVariable('U')
v = self.dataSet.readNCVariable('V')
u_corr = wrf.unstaggerX(u)
v_corr = wrf.unstaggerY(v)
self.u10 = u_corr
self.v10 = v_corr
wind = (u_corr*u_corr + v_corr*v_corr)**(0.5)
height = wrf.unstaggerZ(self.height)
#compute the vertical gradient of theta
dtheta = np.gradient(self.theta)[0]
dz = np.gradient(height)[0]
#calculate the Brunt-Vaisala Frequency
dtheta_dz = dtheta/dz
dims = dtheta_dz.shape
N = np.zeros((dims[0],dims[1],dims[2]))
#Account for statically unstable and neutral conditions
N[dtheta_dz<=0] = 0.00001
N[dtheta_dz>0] = ((self.g/self.theta[dtheta_dz>0])*(dtheta_dz[dtheta_dz>0]))**(0.5)
self.var = wind/(height*N)
self.var2 = self.var
self.varTitle = "Froude Number\n" + self.dataSet.getTime()
#Set short variable title for time series
self.sTitle = "Froude Number"
def cape_MU(self):
#Switched to Cython
#self.var, self.var2 = wrf.get_cape(self.temp,self.qvapor,
# self.press,self.height,'most_unstable')
height = wrf.unstaggerZ(self.height)
self.var, self.var2 = wrf_cython.cape_mu(self.temp,self.qvapor,
self.press,height)
self.varTitle = "Most Unstable CAPE (J kg$^{-1}$)\n"+ self.dataSet.getTime()
#Set short variable title for time series
self.sTitle = "Most Unstable CAPE (J kg$^{-1}$)"
def cin_ML(self):
#Sitched to Cython
#self.var2, self.var = wrf.get_cape(self.temp,self.qvapor,
# self.press,self.height,'mixed_layer')
height = wrf.unstaggerZ(self.height)
self.var2, self.var = np.array(wrf_cython.cape_ml(self.temp,self.qvapor,
self.press,height))
self.var *= -1
self.varTitle = "Mixed Layer CIN (J kg$^{-1}$)\n"+ self.dataSet.getTime()
#Set short variable title for time series
self.sTitle = "Mixed Layer CIN (J kg$^{-1}$)"
def cin_SB(self):
#Switched to Cython
#self.var2, self.var = wrf.get_cape(self.temp,self.qvapor,
# self.press,self.height,'surface_based')
height = wrf.unstaggerZ(self.height)
self.var2, self.var = np.array(wrf_cython.cape_sb(self.temp,self.qvapor,
self.press,height))
self.var *= -1
self.varTitle = "Surface-Based CIN (J kg$^{-1}$)\n"+ self.dataSet.getTime()
#Set short variable title for time series
self.sTitle = "Surface-Based CIN (J kg$^{-1}$)"
def mean_layer_temp(self):
height = wrf.unstaggerZ(self.height)
pblh = self.dataSet.readNCVariable('PBLH')
#Calculate mean layer temperature
#Switch to Cython
#self.var = wrf.mean_layer(self.temp,height,0,pblh)
points = pblh.shape
ref1 = np.zeros((points[1],points[0]),dtype=np.float32)
self.var = wrf_cython.mean_layer(self.temp,height,ref1,pblh)
self.var2 = self.var
self.varTitle = "Mean Layer Temperature (K) \n"+ self.dataSet.getTime()
#Set short variable title for time series
self.sTitle = "Mean Layer Temperature (K)"
def refl(self):
if 'REFL_10CM' not in self.dataSet.variableList:
qrain = self.dataSet.readNCVariable('QRAIN')
qsnow = self.dataSet.readNCVariable('QSNOW')
qgraup = self.dataSet.readNCVariable('QGRAUP')
height = wrf.unstaggerZ(self.height)
self.var = wrf.get_refl(qrain, qgraup, qsnow, self.rho, self.temp, self.height)
self.var2 = self.var
if 'REFL_10CM' in self.dataSet.variableList:
tmp = self.dataSet.readNCVariable('REFL_10CM')
if self.dataSet.dx[self.dataSet.currentGrid-1] < 12000.:
#get the index 1.5-km AGL
height = wrf.unstaggerZ(self.height)
diff = (self.height) - (self.height[0,:,:]+1500.)
zz = min(np.where(diff > 0)[0])
self.var = np.amax(tmp[0:zz,:,:], axis=0)
else:
self.var = np.amax(tmp,axis=0)
self.var2 = self.var
self.varTitle = "0-1km Simulated Radar Reflectivity (dBZ)\n" + self.dataSet.getTime()
#Set short variable title for time series
self.sTitle = "0-1km Simulated Radar Reflectivity (dBZ)"
def hgt_500mb(self):
u = self.dataSet.readNCVariable('U')
v = self.dataSet.readNCVariable('V')
u_corr = wrf.unstaggerX(u)
v_corr = wrf.unstaggerY(v)
height = wrf.unstaggerZ(self.height)
ref_val = 50000.
#Switched to Cython
#self.u10 = wrf.loglinear_interpolate(u_corr, self.press, ref_val)
#self.v10 = wrf.loglinear_interpolate(v_corr, self.press, ref_val)
self.u10 = np.array(wrf_cython.loglinear_interpolate(u_corr, self.press, ref_val))
self.v10 = np.array(wrf_cython.loglinear_interpolate(v_corr, self.press, ref_val))
#self.var = wrf.hypsometric(height,self.press, ref_val, self.temp)
self.var = np.array(wrf_cython.hypsometric(height, self.press, ref_val, self.temp))
self.var2 = self.var
self.varTitle = "500-mb Geopotential Height (m)\n" + self.dataSet.getTime()
#Set short variable title for time series
self.sTitle = "500-mb Geopotential Height (m)"
def winds_300mb(self):
u = self.dataSet.readNCVariable('U')
v = self.dataSet.readNCVariable('V')
u_corr = wrf.unstaggerX(u)
v_corr = wrf.unstaggerY(v)
height = wrf.unstaggerZ(self.height)
ref_val = 30000.
#Switched to Cython
#self.u10 = wrf.loglinear_interpolate(u_corr, self.press, ref_val)
#self.v10 = wrf.loglinear_interpolate(v_corr, self.press, ref_val)
self.u10 = np.array(wrf_cython.loglinear_interpolate(u_corr, self.press, ref_val))
self.v10 = np.array(wrf_cython.loglinear_interpolate(v_corr, self.press, ref_val))
var1 = wrf.get_bulk_wind(self.u10,self.v10)
self.var = wrf.convertWind_MStoKT(var1)
#self.var2 = wrf.hypsometric(height, self.press, ref_val, self.temp)
self.var2 = np.array(wrf_cython.hypsometric(height, self.press, ref_val, self.temp))
self.varTitle = "300-mb Wind\n" + self.dataSet.getTime()
#Set short variable title for time series
self.sTitle = "300-mb Wind"
def richardson(self):
u = self.dataSet.readNCVariable('U')
v = self.dataSet.readNCVariable('V')
u_corr = wrf.unstaggerX(u)
v_corr = wrf.unstaggerY(v)
wind = (u_corr*u_corr + v_corr*v_corr)**(0.5)
height = wrf.unstaggerZ(self.height)
#compute the vertical gradient of theta
dtheta = np.gradient(self.theta)[0]
du = np.gradient(wind)[0]
dz = np.gradient(height)[0]
#compute the richardson number
self.u10 = u_corr
self.v10 = v_corr
self.var = ((self.g/self.theta)*(dtheta/dz))/(du/dz)**(2)
self.var2 = self.var
self.varTitle = "Richardson Number\n" + self.dataSet.getTime()
#Set short variable title for time series
self.sTitle = "Richardson Number"
def temp_500mb(self):
u = self.dataSet.readNCVariable('U')
v = self.dataSet.readNCVariable('V')
u_corr = wrf.unstaggerX(u)
v_corr = wrf.unstaggerY(v)
height = wrf.unstaggerZ(self.height)
ref_val = 50000.
#Switched to Cython
#var1 = wrf.loglinear_interpolate(self.temp, self.press, ref_val)
#self.u10 = wrf.loglinear_interpolate(u_corr, self.press, ref_val)
#self.v10 = wrf.loglinear_interpolate(v_corr, self.press, ref_val)
var1 = np.array(wrf_cython.loglinear_interpolate(self.temp, self.press, ref_val))
self.u10 = np.array(wrf_cython.loglinear_interpolate(u_corr, self.press, ref_val))
self.v10 = np.array(wrf_cython.loglinear_interpolate(v_corr, self.press, ref_val))
self.var = wrf.convertT_KtoC(var1)
#self.var2 = wrf.hypsometric(height, self.press, ref_val, self.temp)
self.var2 = np.array(wrf_cython.hypsometric(height, self.press, ref_val, self.temp))
self.varTitle = "500-mb Temperature ($^{\circ}$C)\n" + self.dataSet.getTime()
#Set short variable title for time series
self.sTitle = "500-mb Temperature ($^{\circ}$C)"
def vort_500mb(self):
u = self.dataSet.readNCVariable('U')
v = self.dataSet.readNCVariable('V')
u_corr = wrf.unstaggerX(u)
v_corr = wrf.unstaggerY(v)
height = wrf.unstaggerZ(self.height)
ref_val = 50000.
#Switched to Cython
#self.u10 = wrf.loglinear_interpolate(u_corr, self.press, ref_val)
#self.v10 = wrf.loglinear_interpolate(v_corr, self.press, ref_val)
self.u10 = np.array(wrf_cython.loglinear_interpolate(u_corr, self.press, ref_val))
self.v10 = np.array(wrf_cython.loglinear_interpolate(v_corr, self.press, ref_val))
self.var = wrf.rel_vort(self.u10, self.v10,
self.dataSet.dx[self.dataSet.currentGrid-1],
self.dataSet.dy[self.dataSet.currentGrid-1])
#self.var2 = wrf.hypsometric(height, self.press, ref_val, self.temp)
self.var2 = np.array(wrf_cython.hypsometric(height, self.press, ref_val, self.temp))
self.varTitle = "500-mb Relative Vorticity ($10^{-5}$ $s^{-1}$)\n" +\
self.dataSet.getTime()
#Set short variable title for time series
self.sTitle = "500-mb Relative Vorticity ($10^{-5}$ $s^{-1}$)"
