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 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 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 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 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$^{-1}$)"