def MWtemphum(self):
Theta = N.zeros_like(self.z, dtype=N.dtype('f8'))
RH = N.zeros_like(Theta)
P = N.zeros_like(Theta)
# P = self.get_stdP(self.z)
RV = N.zeros_like(Theta)
T = N.zeros_like(Theta)
# P[0] = 101325
P[0] = 100000
# P[0] = 99500
T[0] = self.th0
for idx, z in enumerate(self.z):
if z <= self.zT:
Theta[idx] = self.th0 + ((self.thT - self.th0) *
((z / self.zT)**1.25))
RH[idx] = 1 - (0.75 * ((z / self.zT)**1.25))
else:
Theta[idx] = self.thT * N.exp(
(mc.g / (mc.cp * self.tT)) * (z - self.zT))
RH[idx] = 0.25
for idx, z in enumerate(self.z):
if idx > 0:
dZ = self.z[idx] - self.z[idx - 1]
# dZZ = self.z[idx]
# dP = self.compute_P_at_z(P[idx-1],DryBulb[idx-1]-(6.5*dZ*10**-3),dZ)
# dP = self.compute_P_at_z(P[idx-1],T[idx-1],dZ)
# dP = self.compute_P_at_z(P[idx-1],T[idx-1]-(7*dZ*10**-3),dZ)
mT = T[idx - 1] - (7 * 0.5 * dZ * 10**-3)
# mT = T[idx-1]
P[idx] = self.compute_P_at_z(P[idx - 1], mT, RH[idx], dZ)
# P[idx] = P[idx-1] + (1.0*dP)
T[idx] = self.compute_drybulb(Theta[idx], P[idx])
pass
# pdb.set_trace()
T = self.compute_drybulb(Theta, P)
T_D = self.compute_Td(T, RH)
# RV = self.compute_rv(P,T,RH)
RV = self.compute_rv(P, Td=T_D)
RV = RV * 0.85
# Set constant mixing ratio at surface
# PBLidx = N.argmax(P<85000)
PBLidx = N.argmax(RV < self.qv0) #-2
# RV_const = RV[PBLidx+1]
RV_const = self.qv0
RV[0:PBLidx] = RV_const
RH2 = self.RH_invert_RV(P, T, RV)
# Fix constant theta-e in lowest layer
# hLCL = ( 20 + ((T[0]-273.15)/5)) * (100-(100*RH2[0]))
# pdb.set_trace()
# LCLidx = N.argmax(self.z>hLCL)#-2
# thE = self.compute_thetaep(P[LCLidx],RV[LCLidx],T[LCLidx],RH[LCLidx])
# Theta[0:LCLidx] = self.compute_theta(thE,RV[0:LCLidx],T[LCLidx])
# pdb.set_trace()
test_plot = False
if test_plot:
# Recalculate RH in PBL
# ES = self.compute_es(T)
# RVs = 0.622*(ES/(P-ES))
# RH2 = RV/RVs
# T_D = self.compute_Td(T,RH2)
# T = self.compute_drybulb(Theta,P)
T_D = self.compute_Td(T, RH2)
sT.plot_profile(P / 100, T - 273.15, T_D - 273.15,
'/home/johnlawson')
# pdb.set_trace()
return Theta, RV
def MWtemphum(self):
Theta = N.zeros_like(self.z,dtype=N.dtype('f8'))
RH = N.zeros_like(Theta)
P = N.zeros_like(Theta)
# P = self.get_stdP(self.z)
RV = N.zeros_like(Theta)
T = N.zeros_like(Theta)
# P[0] = 101325
P[0] = 100000
# P[0] = 99500
T[0] = self.th0
for idx,z in enumerate(self.z):
if z <= self.zT:
Theta[idx] = self.th0 + ((self.thT - self.th0)*(
(z/self.zT)**1.25))
RH[idx] = 1 - (0.75*((z/self.zT)**1.25))
else:
Theta[idx] = self.thT * N.exp(
(mc.g/(mc.cp*self.tT))*(z-self.zT))
RH[idx] = 0.25
for idx,z in enumerate(self.z):
if idx > 0:
dZ = self.z[idx] - self.z[idx-1]
# dZZ = self.z[idx]
# dP = self.compute_P_at_z(P[idx-1],DryBulb[idx-1]-(6.5*dZ*10**-3),dZ)
# dP = self.compute_P_at_z(P[idx-1],T[idx-1],dZ)
# dP = self.compute_P_at_z(P[idx-1],T[idx-1]-(7*dZ*10**-3),dZ)
mT = T[idx-1] - (7*0.5*dZ*10**-3)
# mT = T[idx-1]
P[idx] = self.compute_P_at_z(P[idx-1],mT,RH[idx],dZ)
# P[idx] = P[idx-1] + (1.0*dP)
T[idx] = self.compute_drybulb(Theta[idx],P[idx])
pass
# pdb.set_trace()
T = self.compute_drybulb(Theta,P)
T_D = self.compute_Td(T,RH)
# RV = self.compute_rv(P,T,RH)
RV = self.compute_rv(P,Td=T_D)
RV = RV*0.85
# Set constant mixing ratio at surface
# PBLidx = N.argmax(P<85000)
PBLidx = N.argmax(RV<self.qv0)#-2
# RV_const = RV[PBLidx+1]
RV_const = self.qv0
RV[0:PBLidx] = RV_const
RH2 = self.RH_invert_RV(P,T,RV)
# Fix constant theta-e in lowest layer
# hLCL = ( 20 + ((T[0]-273.15)/5)) * (100-(100*RH2[0]))
# pdb.set_trace()
# LCLidx = N.argmax(self.z>hLCL)#-2
# thE = self.compute_thetaep(P[LCLidx],RV[LCLidx],T[LCLidx],RH[LCLidx])
# Theta[0:LCLidx] = self.compute_theta(thE,RV[0:LCLidx],T[LCLidx])
# pdb.set_trace()
test_plot = False
if test_plot:
# Recalculate RH in PBL
# ES = self.compute_es(T)
# RVs = 0.622*(ES/(P-ES))
# RH2 = RV/RVs
# T_D = self.compute_Td(T,RH2)
# T = self.compute_drybulb(Theta,P)
T_D = self.compute_Td(T,RH2)
sT.plot_profile(P/100,T-273.15,T_D-273.15,'/home/johnlawson')
# pdb.set_trace()
return Theta, RV
def buoyancy(self, ):
""" Parcel buoyancy profile, or Eq. (A1) in MW01.
Returns
-------
b : array_like
Atmospheric profile of buoyancy.
qv : array_like
Atmospheric profile of mixing ratio
"""
z = self.z
E = self.E
m = self.m
zL = self.zL
H = self.H
zT = self.zT
RH = self.RH
# z' in Eq (1)
dz = z - zL
# Index of LCL, k2, and tropopause
Lidx = N.where(z == zL)[0][0]
k2idx = N.where(z == self.k2z)[0][0]
Tidx = N.where(z == zT)[0][0]
# Height above LCL of max buoyancy
self.Zb = H / m
# Buoyancy profile
b = ((E * dz * m**2) / (H**2)) * N.exp((m / H) * -dz)
# Set buoyancy to zero at and above tropopause
# troposphere = N.where(z>=zT)
# bT = copy.copy(b)
# bT[troposphere] = 0
# Generating other arrays
P = N.zeros_like(z, dtype=N.dtype('f8'))
Theta = N.zeros_like(P)
Rv = N.zeros_like(P)
DryBulb = N.zeros_like(P)
# Stats at k2
P[k2idx] = self.k2P
Rv[k2idx] = self.compute_rv(self.k2P, Td=self.k2Td)
self.k2RH = self.compute_RH(self.k2Td, self.k2T)
Theta[k2idx] = self.compute_theta_2(self.k2P, self.k2T)
DryBulb[k2idx] = self.k2T
# Temperature at the LCL
TLCL = self.compute_TLCL(self.k2Td, self.k2T)
# self.k2the = self.compute_thetae_4(DryBulb[k2idx],P[k2idx],
# Rv[k2idx],TLCL)
self.k2the = self.compute_thetaep(P[k2idx], Rv[k2idx], self.k2T,
self.k2RH)
# self.k2the = self.compute_thetae_3(Theta[k2idx],self.k2P,self.k2Td, self.k2T,Rv[k2idx],TL=TLCL)
# self.k2the = self.compute_thetae(self.k2T,self.k2rv,self.k2P,self.k2Td)
# self.k2the = self.compute_thetae_2(self.k2th,self.k2rv,self.k2T)+4
# Get to next z level
# dZ = zdiff[znext]
# dP = self.compute_P_at_z(self.k2P,self.k2T,dZ)
# P[znext] = self.k2P + dP
# DryBulb[znext] = self.compute_T_with_LCL(P[znext],self.k2P,self.k2T)
# DryBulb[znext] = self.compute_T_invert_thetae(self.k2the,P[znext],self.k2rv,self.k2T)
# Rv[znext] = self.compute_rv(P[znext],T=DryBulb[znext],RH=self.RH)
# Theta-e is conserved, and use T of LCL
# Theta[znext] = self.compute_theta(self.k2the,Rv[znext],self.k2T)
# Theta[znext] = self.compute_theta_2(P[znext],DryBulb[znext])
# Now iterate up sounding from k2 thru LCL to top of model
for zidx in range(k2idx + 1, len(z)):
zbot = zidx - 1
dZ = z[zidx] - z[zbot]
dP = self.compute_P_at_z(P[zbot],
DryBulb[zbot] - (6.5 * dZ * 10**-3), dZ)
# dP = self.compute_P_at_z(P[zbot],DryBulb[zbot],dZ)
P[zidx] = P[zbot] + dP
if zidx < Lidx:
Rv[zidx] = Rv[k2idx]
# DryBulb[zidx] = self.compute_T_with_LCL(P[zidx],self.k2P,self.k2T)
Theta[zidx] = self.compute_theta(self.k2the, Rv[k2idx], TLCL)
DryBulb[zidx] = self.compute_drybulb(Theta[zidx], P[zidx])
elif zidx == Lidx:
# Using LCL RH = 0.9. Could try 1?
DryBulb[zidx] = TLCL
Theta[zidx] = self.compute_theta_2(P[zidx], TLCL)
Rv[zidx] = self.compute_rv(P[zidx],
T=DryBulb[zidx],
RH=self.RH)
TdLCL = self.compute_Td(DryBulb[zidx], self.RH)
# theLCL = self.compute_thetae_4(DryBulb[zidx],P[zidx],
# Rv[zidx],TLCL)
theLCL = self.compute_thetaep(P[zidx], Rv[zidx], DryBulb[zidx],
self.RH)
# theLCL = self.compute_thetae_3(Theta[zidx],P[zidx],TdLCL,TLCL,Rv[zidx])
elif zidx > Lidx:
T2 = self.compute_T_with_LCL(P[zidx], P[Lidx], TLCL)
Th2 = self.compute_theta_2(P[zidx], T2)
Rv2 = self.compute_rv(P[zidx], T=T2, RH=self.RH)
# Th1 = self.compute_theta(theLCL,Rv[zbot],TLCL)
# T1 = self.compute_drybulb(Th1,P[zidx])
# T3 = self.compute_T_invert_thetae(theLCL,P[zidx],Rv[zbot],TLCL)
# Th3 = self.compute_theta_2(P[zidx],T3)
Theta[zidx] = Th2
DryBulb[zidx] = T2
Rv[zidx] = Rv2
# pdb.set_trace()
pdb.set_trace()
# Now iterate down from LCL to surface
for zidx in range(0, Lidx + 1)[::-1]:
ztop = zidx + 1
dZ = z[zidx] - z[ztop]
dP = self.compute_P_at_z(P[ztop],
DryBulb[ztop] + (6.5 * dZ * 10**-3), dZ)
# pdb.set_trace()
P[zidx] = P[ztop] + dP
# DryBulb[zidx] = self.compute_T_invert_thetae(self.k2the,P[zidx],Rv[zbot],self.k2T)
DryBulb[zidx] = self.compute_T_with_LCL(P[zidx], self.k2P,
self.k2T)
# Rv[zidx] = self.compute_rv(P[zidx],T=DryBulb[zidx],RH=self.RH)
Rv[zidx] = Rv[Lidx]
Theta[zidx] = self.compute_theta(self.k2the, Rv[zidx], self.k2T)
# MIGHT NEED RICHARDSON CONSTRAINT
# Create isothermal layer above tropopause
isotherm = DryBulb[Tidx]
# for zidx in range(Tidx+1,len(z)):
# Theta = isothermC * (100000/P)**(mc.R/mc.cp)
TRO = slice(Tidx + 1, len(z))
Theta[TRO] = self.compute_theta_2(P[TRO], isotherm)
DryBulb[TRO] = self.compute_drybulb(Theta[TRO], P[TRO])
# ttt = Theta/( (100000/P)**(mc.R/mc.cp))
# Enforce 90% again
# Rv[zidx] = self.compute_rv(p=P[zidx],T=ttt, RH=self.RH)
Rv[TRO] = self.compute_rv(p=P[TRO], T=DryBulb[TRO], RH=self.RH)
# Integrate CAPE between LCL and model top after isothermal calc
Et = integrate.trapz(b[Lidx:], x=z[Lidx:])
# Multiply buoyancy profile by CAPE ratio
B = b * (E / Et)
# Calc virtual temp perturbations from buoyancy
# Tpert = (B*Theta)/mc.g
Tpert = self.compute_thetapert(B, DryBulb, Rv)
# Change theta, sfc/LCL to tropopause, to create CAPE==E in sounding
THETA = Theta.copy()
modify_bLCL = False
if not modify_bLCL:
# Only change LCL upward
THETA[Lidx:Tidx] = Theta[Lidx:Tidx] - Tpert[Lidx:Tidx]
else:
# Change near surface too
THETA[:Tidx] = Theta[:Tidx] - Tpert[:Tidx]
# Enforce RH = 90% for new theta values
DRYBULB = self.compute_drybulb(THETA, P)
SL = slice(Lidx, Tidx)
RV = self.compute_rv(P, T=DRYBULB, RH=self.RH)
# RV[SL] = self.compute_rv(P[SL],T=DRYBULB[SL],RH=self.RH)
# Change PW
# Substract ~8C from theta and dewpoint
# T in C; RH in 0-100 %
# Td = utils.dewpoint(T,RH)
# Modified RH
# Extra fields for plotting skew-T
ES = self.compute_es(DRYBULB)
RVs = 0.622 * (ES / (P - ES))
RHs = RV / RVs
T_D = self.compute_Td(DRYBULB, RHs)
sT.plot_profile(P / 100, DRYBULB - 273.15, T_D - 273.15, self.fdir)
# sT.plot_profile(pres,oldtemps,dewpoints,self.fdir,fname='skewT_oldtemps.png')
pdb.set_trace()
return THETA, RV
def buoyancy(self,):
""" Parcel buoyancy profile, or Eq. (A1) in MW01.
Returns
-------
b : array_like
Atmospheric profile of buoyancy.
qv : array_like
Atmospheric profile of mixing ratio
"""
z = self.z
E = self.E
m = self.m
zL = self.zL
H = self.H
zT = self.zT
RH = self.RH
# z' in Eq (1)
dz = z-zL
# Index of LCL, k2, and tropopause
Lidx = N.where(z==zL)[0][0]
k2idx = N.where(z==self.k2z)[0][0]
Tidx = N.where(z==zT)[0][0]
# Height above LCL of max buoyancy
self.Zb = H/m
# Buoyancy profile
b = ((E*dz*m**2)/(H**2))*N.exp((m/H)*-dz)
# Set buoyancy to zero at and above tropopause
# troposphere = N.where(z>=zT)
# bT = copy.copy(b)
# bT[troposphere] = 0
# Generating other arrays
P = N.zeros_like(z,dtype=N.dtype('f8'))
Theta = N.zeros_like(P)
Rv = N.zeros_like(P)
DryBulb = N.zeros_like(P)
# Stats at k2
P[k2idx] = self.k2P
Rv[k2idx] = self.compute_rv(self.k2P,Td=self.k2Td)
self.k2RH = self.compute_RH(self.k2Td,self.k2T)
Theta[k2idx] = self.compute_theta_2(self.k2P,self.k2T)
DryBulb[k2idx] = self.k2T
# Temperature at the LCL
TLCL = self.compute_TLCL(self.k2Td,self.k2T)
# self.k2the = self.compute_thetae_4(DryBulb[k2idx],P[k2idx],
# Rv[k2idx],TLCL)
self.k2the = self.compute_thetaep(P[k2idx],Rv[k2idx],self.k2T,self.k2RH)
# self.k2the = self.compute_thetae_3(Theta[k2idx],self.k2P,self.k2Td, self.k2T,Rv[k2idx],TL=TLCL)
# self.k2the = self.compute_thetae(self.k2T,self.k2rv,self.k2P,self.k2Td)
# self.k2the = self.compute_thetae_2(self.k2th,self.k2rv,self.k2T)+4
# Get to next z level
# dZ = zdiff[znext]
# dP = self.compute_P_at_z(self.k2P,self.k2T,dZ)
# P[znext] = self.k2P + dP
# DryBulb[znext] = self.compute_T_with_LCL(P[znext],self.k2P,self.k2T)
# DryBulb[znext] = self.compute_T_invert_thetae(self.k2the,P[znext],self.k2rv,self.k2T)
# Rv[znext] = self.compute_rv(P[znext],T=DryBulb[znext],RH=self.RH)
# Theta-e is conserved, and use T of LCL
# Theta[znext] = self.compute_theta(self.k2the,Rv[znext],self.k2T)
# Theta[znext] = self.compute_theta_2(P[znext],DryBulb[znext])
# Now iterate up sounding from k2 thru LCL to top of model
for zidx in range(k2idx+1,len(z)):
zbot = zidx-1
dZ = z[zidx]-z[zbot]
dP = self.compute_P_at_z(P[zbot],DryBulb[zbot]-(6.5*dZ*10**-3),dZ)
# dP = self.compute_P_at_z(P[zbot],DryBulb[zbot],dZ)
P[zidx] = P[zbot] + dP
if zidx < Lidx:
Rv[zidx] = Rv[k2idx]
# DryBulb[zidx] = self.compute_T_with_LCL(P[zidx],self.k2P,self.k2T)
Theta[zidx] = self.compute_theta(self.k2the,Rv[k2idx],TLCL)
DryBulb[zidx] = self.compute_drybulb(Theta[zidx],P[zidx])
elif zidx == Lidx:
# Using LCL RH = 0.9. Could try 1?
DryBulb[zidx] = TLCL
Theta[zidx] = self.compute_theta_2(P[zidx],TLCL)
Rv[zidx] = self.compute_rv(P[zidx],T=DryBulb[zidx],RH=self.RH)
TdLCL = self.compute_Td(DryBulb[zidx],self.RH)
# theLCL = self.compute_thetae_4(DryBulb[zidx],P[zidx],
# Rv[zidx],TLCL)
theLCL = self.compute_thetaep(P[zidx],Rv[zidx],DryBulb[zidx],self.RH)
# theLCL = self.compute_thetae_3(Theta[zidx],P[zidx],TdLCL,TLCL,Rv[zidx])
elif zidx > Lidx:
T2 = self.compute_T_with_LCL(P[zidx],P[Lidx],TLCL)
Th2 = self.compute_theta_2(P[zidx],T2)
Rv2 = self.compute_rv(P[zidx],T=T2,RH=self.RH)
# Th1 = self.compute_theta(theLCL,Rv[zbot],TLCL)
# T1 = self.compute_drybulb(Th1,P[zidx])
# T3 = self.compute_T_invert_thetae(theLCL,P[zidx],Rv[zbot],TLCL)
# Th3 = self.compute_theta_2(P[zidx],T3)
Theta[zidx] = Th2
DryBulb[zidx] = T2
Rv[zidx] = Rv2
# pdb.set_trace()
pdb.set_trace()
# Now iterate down from LCL to surface
for zidx in range(0,Lidx+1)[::-1]:
ztop = zidx+1
dZ = z[zidx]-z[ztop]
dP = self.compute_P_at_z(P[ztop],DryBulb[ztop]+(6.5*dZ*10**-3),dZ)
# pdb.set_trace()
P[zidx] = P[ztop] + dP
# DryBulb[zidx] = self.compute_T_invert_thetae(self.k2the,P[zidx],Rv[zbot],self.k2T)
DryBulb[zidx] = self.compute_T_with_LCL(P[zidx],self.k2P,self.k2T)
# Rv[zidx] = self.compute_rv(P[zidx],T=DryBulb[zidx],RH=self.RH)
Rv[zidx] = Rv[Lidx]
Theta[zidx] = self.compute_theta(self.k2the,Rv[zidx],self.k2T)
# MIGHT NEED RICHARDSON CONSTRAINT
# Create isothermal layer above tropopause
isotherm = DryBulb[Tidx]
# for zidx in range(Tidx+1,len(z)):
# Theta = isothermC * (100000/P)**(mc.R/mc.cp)
TRO = slice(Tidx+1,len(z))
Theta[TRO] = self.compute_theta_2(P[TRO],isotherm)
DryBulb[TRO] = self.compute_drybulb(Theta[TRO],P[TRO])
# ttt = Theta/( (100000/P)**(mc.R/mc.cp))
# Enforce 90% again
# Rv[zidx] = self.compute_rv(p=P[zidx],T=ttt, RH=self.RH)
Rv[TRO] = self.compute_rv(p=P[TRO],T=DryBulb[TRO], RH=self.RH)
# Integrate CAPE between LCL and model top after isothermal calc
Et = integrate.trapz(b[Lidx:],x=z[Lidx:])
# Multiply buoyancy profile by CAPE ratio
B = b * (E/Et)
# Calc virtual temp perturbations from buoyancy
# Tpert = (B*Theta)/mc.g
Tpert = self.compute_thetapert(B,DryBulb,Rv)
# Change theta, sfc/LCL to tropopause, to create CAPE==E in sounding
THETA = Theta.copy()
modify_bLCL = False
if not modify_bLCL:
# Only change LCL upward
THETA[Lidx:Tidx] = Theta[Lidx:Tidx]-Tpert[Lidx:Tidx]
else:
# Change near surface too
THETA[:Tidx] = Theta[:Tidx]-Tpert[:Tidx]
# Enforce RH = 90% for new theta values
DRYBULB = self.compute_drybulb(THETA,P)
SL = slice(Lidx,Tidx)
RV = self.compute_rv(P,T=DRYBULB,RH=self.RH)
# RV[SL] = self.compute_rv(P[SL],T=DRYBULB[SL],RH=self.RH)
# Change PW
# Substract ~8C from theta and dewpoint
# T in C; RH in 0-100 %
# Td = utils.dewpoint(T,RH)
# Modified RH
# Extra fields for plotting skew-T
ES = self.compute_es(DRYBULB)
RVs = 0.622*(ES/(P-ES))
RHs = RV/RVs
T_D = self.compute_Td(DRYBULB,RHs)
sT.plot_profile(P/100,DRYBULB-273.15,T_D-273.15,self.fdir)
# sT.plot_profile(pres,oldtemps,dewpoints,self.fdir,fname='skewT_oldtemps.png')
pdb.set_trace()
return THETA, RV
