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 readNCVariable(self,
vname,
barbs=None,
vectors=None,
contour2=None,
varonly=False):
variable = self.dsets[vname][self.currentTimeIndex]
if varonly == False:
if (hasattr(self.dsets[vname], 'description')):
self.description = self.dsets[vname].description
if (hasattr(self.dsets[vname], 'units')):
self.units = self.dsets[vname].units
if vname != 'Times':
if len(self.dsets[vname].dimensions) == 4:
if len(self.dsets[vname].shape) >= 3:
tmp = np.arange(1, self.nz[self.currentGrid - 1] + 1,
1)
self.levelList = ["%2d" % x for x in tmp]
else:
tmp = [1]
self.levelList = ["%2d" % x for x in tmp]
else:
self.levelList = ['NA']
if barbs == True or vectors == True:
if len(self.dsets[vname].shape) == 4:
tmp = self.dsets['U'][self.currentTimeIndex]
self.u10 = wrf.unstaggerX(tmp)
tmp = self.dsets['V'][self.currentTimeIndex]
self.v10 = wrf.unstaggerY(tmp)
else:
self.u10 = self.dsets['U10'][self.currentTimeIndex]
self.v10 = self.dsets['V10'][self.currentTimeIndex]
return variable
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 getVertVars(self):
u = wrf.unstaggerX(np.squeeze(self.readNCVariable('UU')))
v = wrf.unstaggerY(np.squeeze(self.readNCVariable('VV')))
height = np.squeeze(self.readNCVariable('GHT')) / 1000.
press = np.squeeze(self.readNCVariable('PRES')) / 100.
w = None
return u, v, w, press, height
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 theta_e(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
self.var = wrf.get_thetae(self.temp,self.qvapor,self.press)
self.var2 = self.var
self.varTitle = "$\mathsf{\Theta_{e}\, (K)}$\n"+ self.dataSet.getTime()
#Set short variable title for time series
self.sTitle = "$\mathsf{\Theta_{e}\, (K)}$"
def temp_3d(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
self.var = self.temp
self.var2 = self.var
self.varTitle = "Temperature (K)\n"+ self.dataSet.getTime()
#Set short variable title for time series
self.sTitle = "Temperature (K)"
def cloud_water(self):
#3D cloud water --> qcloud, qrain
qcloud = self.dataSet.readNCVariable('QCLOUD')
qrain = self.dataSet.readNCVariable('QRAIN')
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
self.var = qcloud + qrain
self.var2 = self.var
self.varTitle = "Total Cloud Water Mixing Ratio (kg kg$^{-1}$)\n"+ self.dataSet.getTime()
#Set short variable title for time series
self.sTitle = "Total Cloud Water Mixing Ratio (kg kg$^{-1}$)"
def wind_3d(self):
u = self.dataSet.readNCVariable('U')
v = self.dataSet.readNCVariable('V')
u_corr = wrf.unstaggerX(u)
v_corr = wrf.unstaggerY(v)
var1 = wrf.get_bulk_wind(u_corr,v_corr)
#self.var = wrf.convertWind_MStoKT(var1)
self.u10 = u_corr
self.v10 = v_corr
self.var = var1
self.var2 = self.var
#self.varTitle = "Total Wind (knots)\n"+ self.dataSet.getTime()
self.varTitle = "Total Wind (m s$^{-1}$)\n"+ self.dataSet.getTime()
#Set short variable title for time series
self.sTitle = "Total Wind (m s$^{-1}$)"
def ice_water(self):
#3D ice water --> qice, qsnow, qgraup
qice = self.dataSet.readNCVariable('QICE')
qsnow = self.dataSet.readNCVariable('QSNOW')
qgraup = self.dataSet.readNCVariable('QGRAUP')
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
self.var = qice + qsnow + qgraup
self.var2 = self.var
self.varTitle = "Total Ice Mixing Ratio (kg kg$^{-1}$)\n"+ self.dataSet.getTime()
#Set short variable title for time series
self.sTitle = "Total Ice Mixing Ratio (kg kg$^{-1}$)"
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 total_water(self):
#3D water --> qrain, qcloud, qice, qsnow, qgraup
qcloud = self.dataSet.readNCVariable('QCLOUD')
qrain = self.dataSet.readNCVariable('QRAIN')
qice = self.dataSet.readNCVariable('QICE')
qsnow = self.dataSet.readNCVariable('QSNOW')
qgraup = self.dataSet.readNCVariable('QGRAUP')
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
self.var = qice + qsnow + qgraup + qcloud + qrain
self.var2 = self.var
self.varTitle = "Total Water Mixing Ratio (kg kg$^{-1}$)\n"+ self.dataSet.getTime()
#Set short variable title for time series
self.sTitle = "Total Water Mixing Ratio (kg kg$^{-1}$)"
def temp_850mb(self):
u = self.dataSet.readNCVariable('U')
v = self.dataSet.readNCVariable('V')
u_corr = wrf.unstaggerX(u)
v_corr = wrf.unstaggerY(v)
ref_val = 85000.
#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.loglinear_interpolate(self.temp, self.press, ref_val)
var1 = np.array(wrf_cython.loglinear_interpolate(self.temp, self.press, ref_val))
self.var = wrf.convertT_KtoC(var1)
self.var2 = wrf.get_mslp(self.height, self.press, self.temp, self.qvapor)
self.varTitle = "850-mb Temperature\n" + self.dataSet.getTime()
#Set short variable title for time series
self.sTitle = "850-mb Temperature"
def rh_700mb(self):
u = self.dataSet.readNCVariable('U')
v = self.dataSet.readNCVariable('V')
u_corr = wrf.unstaggerX(u)
v_corr = wrf.unstaggerY(v)
ref_val = 70000.
#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_rh(self.temp, self.qvapor, self.press)
# self.var = wrf.loglinear_interpolate(var1, self.press, ref_val)
self.var = np.array(wrf_cython.loglinear_interpolate(var1, self.press, ref_val))
self.var2 = self.var
self.varTitle = "700-mb Relative Humidity (%)\n" + self.dataSet.getTime()
#Set short variable title for time series
self.sTitle = "700-mb Relative Humidity (%)"
def PotentialVorticity(self):
#Read in variables
u = self.dataSet.readNCVariable('U')
v = self.dataSet.readNCVariable('V')
f = self.dataSet.readNCVariable('F')
#Define grid spacing in meters
dx = self.dataSet.dx[self.dataSet.currentGrid-1]
dy = self.dataSet.dy[self.dataSet.currentGrid-1]
u_corr = wrf.unstaggerX(u)
v_corr = wrf.unstaggerY(v)
self.u10 = u_corr
self.v10 = v_corr
self.var = wrf.pot_vort(u,v,f,dx,dy,self.press,self.theta)
self.var2 = self.var
self.varTitle = "Potential Voricity (PVU)\n"+ self.dataSet.getTime()
#Set short variable title for time series
self.sTitle = "Potential Voricity (PVU)"
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 vort_850mb(self):
u = self.dataSet.readNCVariable('U')
v = self.dataSet.readNCVariable('V')
u_corr = wrf.unstaggerX(u)
v_corr = wrf.unstaggerY(v)
ref_val = 85000.
#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.get_mslp(self.height, self.press, self.temp, self.qvapor)
self.varTitle = "850-mb Relative Voriticity ($10^{-5}$ $s^{-1}$)\n" +\
self.dataSet.getTime()
#Set short variable title for time series
self.sTitle = "850-mb Relative Voriticity ($10^{-5}$ $s^{-1}$)"
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 divergence(self):
u = self.dataSet.readNCVariable('U')
v = self.dataSet.readNCVariable('V')
u_corr = wrf.unstaggerX(u)
v_corr = wrf.unstaggerY(v)
#For the WRF-ARW Arakawa C grid we don't need to use the unstagger winds
# to calculate the gradient at the mass points
#du = np.gradient(u_corr)[2]
#dv = np.gradient(v_corr)[1]
#Get dimensions
dimsu = u.shape
dimsv = v.shape
du = u[:,:,1:dimsu[2]] - u[:,:,0:dimsu[2]-1]
dv = v[:,1:dimsv[1],:] - v[:,0:dimsv[1]-1,:]
dx = self.dataSet.dx[self.dataSet.currentGrid-1]
dy = self.dataSet.dy[self.dataSet.currentGrid-1]
self.u10 = u_corr
self.v10 = v_corr
self.var = (du/dx + dv/dy)*pow(10,5)
self.var2 = self.var
self.varTitle = "Divergence (10$^{-5}$ s$^{-1}$)\n" + self.dataSet.getTime()
#Set short variable title for time series
self.sTitle = "Divergence (10$^{-5}$ s$^{-1}$)"
