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Initialization.pyx
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#!python
#cython: boundscheck=False
#cython: wraparound=True
#cython: initializedcheck=False
#cython: cdivision=True
import netCDF4 as nc
import numpy as np
cimport numpy as np
from scipy.interpolate import PchipInterpolator,pchip_interpolate
cimport ParallelMPI
from NetCDFIO cimport NetCDFIO_Stats
cimport Grid
cimport PrognosticVariables
cimport DiagnosticVariables
from thermodynamic_functions cimport exner_c, entropy_from_thetas_c, thetas_t_c, qv_star_c, thetas_c, thetali_c
cimport ReferenceState
cimport Restart
from Forcing cimport AdjustedMoistAdiabat
from Thermodynamics cimport LatentHeat
from libc.math cimport sqrt, fmin, cos, exp, fabs
include 'parameters.pxi'
import cPickle
from scipy import interpolate
from fms_forcing_reader import reader
from cfsites_forcing_reader import cfreader
from cfgrid_forcing_reader import cfreader_grid
def InitializationFactory(namelist):
casename = namelist['meta']['casename']
if casename == 'SullivanPatton':
return InitSullivanPatton
elif casename == 'StableBubble':
return InitStableBubble
elif casename == 'SaturatedBubble':
return InitSaturatedBubble
elif casename == 'Bomex':
return InitBomex
elif casename == 'Gabls':
return InitGabls
elif casename == 'DYCOMS_RF01':
return InitDYCOMS_RF01
elif casename == 'DYCOMS_RF02':
return InitDYCOMS_RF02
elif casename == 'SMOKE':
return InitSmoke
elif casename == 'Rico':
return InitRico
elif casename == 'CGILS':
return InitCGILS
elif casename == 'ZGILS':
return InitZGILS
elif casename == 'GCMVarying':
return InitGCMVarying
elif casename == 'GCMMean':
return InitGCMMean
elif casename == 'GCMNew':
return InitGCMNew
else:
pass
def InitStableBubble(namelist, Grid.Grid Gr,PrognosticVariables.PrognosticVariables PV,
ReferenceState.ReferenceState RS, Th, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa, LatentHeat LH):
#Generate reference profiles
RS.Pg = 1.0e5
RS.Tg = 300.0
RS.qtg = 0.0
#Set velocities for Galilean transformation
RS.u0 = 0.0
RS.v0 = 0.0
RS.initialize(Gr, Th, NS, Pa)
#Get the variable number for each of the velocity components
cdef:
Py_ssize_t u_varshift = PV.get_varshift(Gr,'u')
Py_ssize_t v_varshift = PV.get_varshift(Gr,'v')
Py_ssize_t w_varshift = PV.get_varshift(Gr,'w')
Py_ssize_t s_varshift
Py_ssize_t thli_varshit
Py_ssize_t i,j,k
Py_ssize_t ishift, jshift
Py_ssize_t ijk
double t
double dist
try:
s_prognostic = namelist['thermodynamics']['s_prognostic']
except:
s_prognostic = True
if s_prognostic:
s_varshift = PV.get_varshift(Gr,'s')
else:
thli_varshift = PV.get_varshift(Gr, 'thli')
if s_prognostic:
for i in xrange(Gr.dims.nlg[0]):
ishift = i * Gr.dims.nlg[1] * Gr.dims.nlg[2]
for j in xrange(Gr.dims.nlg[1]):
jshift = j * Gr.dims.nlg[2]
for k in xrange(Gr.dims.nlg[2]):
ijk = ishift + jshift + k
PV.values[u_varshift + ijk] = 0.0
PV.values[v_varshift + ijk] = 0.0
PV.values[w_varshift + ijk] = 0.0
dist = np.sqrt(((Gr.x_half[i + Gr.dims.indx_lo[0]]/1000.0 - 25.6)/4.0)**2.0 + ((Gr.zp_half[k + Gr.dims.indx_lo[2]]/1000.0 - 3.0)/2.0)**2.0)
dist = fmin(dist,1.0)
t = (300.0 )*exner_c(RS.p0_half[k]) - 15.0*( cos(np.pi * dist) + 1.0) /2.0
PV.values[s_varshift + ijk] = Th.entropy(RS.p0_half[k],t,0.0,0.0,0.0)
else:
for i in xrange(Gr.dims.nlg[0]):
ishift = i * Gr.dims.nlg[1] * Gr.dims.nlg[2]
for j in xrange(Gr.dims.nlg[1]):
jshift = j * Gr.dims.nlg[2]
for k in xrange(Gr.dims.nlg[2]):
ijk = ishift + jshift + k
PV.values[u_varshift + ijk] = 0.0
PV.values[v_varshift + ijk] = 0.0
PV.values[w_varshift + ijk] = 0.0
dist = np.sqrt(((Gr.x_half[i + Gr.dims.indx_lo[0]]/1000.0 - 25.6)/4.0)**2.0 + ((Gr.zp_half[k + Gr.dims.indx_lo[2]]/1000.0 - 3.0)/2.0)**2.0)
dist = fmin(dist,1.0)
t = (300.0 )*exner_c(RS.p0_half[k]) - 15.0*( cos(np.pi * dist) + 1.0) /2.0
PV.values[thli_varshift + ijk] = thetali_c(RS.p0_half[k], t, 0.0, 0.0, 0.0, Th.get_lh(t))
if s_prognostic:
for i in xrange(Gr.dims.nlg[0]):
ishift = i * Gr.dims.nlg[1] * Gr.dims.nlg[2]
for j in xrange(Gr.dims.nlg[1]):
jshift = j * Gr.dims.nlg[2]
for k in xrange(Gr.dims.nlg[2]):
ijk = ishift + jshift + k
PV.values[u_varshift + ijk] = 0.0
PV.values[v_varshift + ijk] = 0.0
PV.values[w_varshift + ijk] = 0.0
dist = np.sqrt(((Gr.x_half[i + Gr.dims.indx_lo[0]]/1000.0 - 25.6)/4.0)**2.0 + ((Gr.z_half[k + Gr.dims.indx_lo[2]]/1000.0 - 3.0)/2.0)**2.0)
dist = fmin(dist,1.0)
t = (300.0 )*exner_c(RS.p0_half[k]) - 15.0*( cos(np.pi * dist) + 1.0) /2.0
PV.values[s_varshift + ijk] = Th.entropy(RS.p0_half[k],t,0.0,0.0,0.0)
else:
for i in xrange(Gr.dims.nlg[0]):
ishift = i * Gr.dims.nlg[1] * Gr.dims.nlg[2]
for j in xrange(Gr.dims.nlg[1]):
jshift = j * Gr.dims.nlg[2]
for k in xrange(Gr.dims.nlg[2]):
ijk = ishift + jshift + k
PV.values[u_varshift + ijk] = 0.0
PV.values[v_varshift + ijk] = 0.0
PV.values[w_varshift + ijk] = 0.0
dist = np.sqrt(((Gr.x_half[i + Gr.dims.indx_lo[0]]/1000.0 - 25.6)/4.0)**2.0 + ((Gr.z_half[k + Gr.dims.indx_lo[2]]/1000.0 - 3.0)/2.0)**2.0)
dist = fmin(dist,1.0)
t = (300.0 )*exner_c(RS.p0_half[k]) - 15.0*( cos(np.pi * dist) + 1.0) /2.0
PV.values[thli_varshift + ijk] = thetali_c(RS.p0_half[k], t, 0.0, 0.0, 0.0, Th.get_lh(t))
return
def InitSaturatedBubble(namelist,Grid.Grid Gr,PrognosticVariables.PrognosticVariables PV,
ReferenceState.ReferenceState RS, Th, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa, LatentHeat LH ):
#Generate reference profiles
RS.Pg = 1.0e5
RS.qtg = 0.02
#RS.Tg = 300.0
thetas_sfc = 320.0
qt_sfc = 0.0196 #RS.qtg
RS.qtg = qt_sfc
#Set velocities for Galilean transformation
RS.u0 = 0.0
RS.v0 = 0.0
def theta_to_T(p0_,thetas_,qt_):
T1 = Tt
T2 = Tt + 1.
pv1 = Th.get_pv_star(T1)
pv2 = Th.get_pv_star(T2)
qs1 = qv_star_c(p0_, RS.qtg,pv1)
ql1 = np.max([0.0,qt_ - qs1])
L1 = Th.get_lh(T1)
f1 = thetas_ - thetas_t_c(p0_,T1,qt_,qt_-ql1,ql1,L1)
delta = np.abs(T1 - T2)
while delta >= 1e-12:
L2 = Th.get_lh(T2)
pv2 = Th.get_pv_star(T2)
qs2 = qv_star_c(p0_, RS.qtg, pv2)
ql2 = np.max([0.0,qt_ - qs2])
f2 = thetas_ - thetas_t_c(p0_,T2,qt_,qt_-ql2,ql2,L2)
Tnew = T2 - f2 * (T2 - T1)/(f2 - f1)
T1 = T2
T2 = Tnew
f1 = f2
delta = np.abs(T1 - T2)
return T2, ql2
if 's' in PV.name_index:
RS.Tg, ql = theta_to_T(RS.Pg,thetas_sfc,qt_sfc)
else:
RS.Tg = 307.616824335
RS.initialize(Gr, Th, NS, Pa)
#Get the variable number for each of the velocity components
cdef:
Py_ssize_t u_varshift = PV.get_varshift(Gr,'u')
Py_ssize_t v_varshift = PV.get_varshift(Gr,'v')
Py_ssize_t w_varshift = PV.get_varshift(Gr,'w')
Py_ssize_t s_varshift
Py_ssize_t thli_varshit
Py_ssize_t qt_varshift = PV.get_varshift(Gr,'qt')
Py_ssize_t i,j,k
Py_ssize_t ishift, jshift
Py_ssize_t ijk
double t
double dist
double thetas
if 's' in PV.name_index:
s_varshift = PV.get_varshift(Gr,'s')
for i in xrange(Gr.dims.nlg[0]):
ishift = i * Gr.dims.nlg[1] * Gr.dims.nlg[2]
for j in xrange(Gr.dims.nlg[1]):
jshift = j * Gr.dims.nlg[2]
for k in xrange(Gr.dims.nlg[2]):
ijk = ishift + jshift + k
dist = np.sqrt(((Gr.x_half[i + Gr.dims.indx_lo[0]]/1000.0 - 10.0)/2.0)**2.0 + ((Gr.zp_half[k + Gr.dims.indx_lo[2]]/1000.0 - 2.0)/2.0)**2.0)
dist = np.minimum(1.0,dist)
thetas = RS.Tg
thetas += 2.0 * np.cos(np.pi * dist / 2.0)**2.0
PV.values[s_varshift + ijk] = entropy_from_thetas_c(thetas,RS.qtg)
PV.values[u_varshift + ijk] = 0.0 - RS.u0
PV.values[v_varshift + ijk] = 0.0 - RS.v0
PV.values[w_varshift + ijk] = 0.0
PV.values[qt_varshift + ijk] = RS.qtg
else:
thli_varshift = PV.get_varshift(Gr,'thli')
for i in xrange(Gr.dims.nlg[0]):
ishift = i * Gr.dims.nlg[1] * Gr.dims.nlg[2]
for j in xrange(Gr.dims.nlg[1]):
jshift = j * Gr.dims.nlg[2]
for k in xrange(Gr.dims.nlg[2]):
ijk = ishift + jshift + k
dist = np.sqrt(((Gr.x_half[i + Gr.dims.indx_lo[0]]/1000.0 - 10.0)/2.0)**2.0 + ((Gr.zp_half[k + Gr.dims.indx_lo[2]]/1000.0 - 2.0)/2.0)**2.0)
dist = np.minimum(1.0,dist)
thetas = RS.Tg
thetas += 2.0 * np.cos(np.pi * dist / 2.0)**2.0
PV.values[thli_varshift + ijk] = thetas
PV.values[u_varshift + ijk] = 0.0 - RS.u0
PV.values[v_varshift + ijk] = 0.0 - RS.v0
PV.values[w_varshift + ijk] = 0.0
PV.values[qt_varshift + ijk] = RS.qtg
return
def InitSullivanPatton(namelist,Grid.Grid Gr,PrognosticVariables.PrognosticVariables PV,
ReferenceState.ReferenceState RS, Th, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa, LatentHeat LH ):
#Generate the reference profiles
RS.Pg = 1.0e5 #Pressure at ground
RS.Tg = 300.0 #Temperature at ground
RS.qtg = 0.0 #Total water mixing ratio at surface
RS.u0 = 1.0 # velocities removed in Galilean transformation
RS.v0 = 0.0
RS.initialize(Gr, Th, NS, Pa)
#Get the variable number for each of the velocity components
np.random.seed(Pa.rank)
cdef:
Py_ssize_t u_varshift = PV.get_varshift(Gr,'u')
Py_ssize_t v_varshift = PV.get_varshift(Gr,'v')
Py_ssize_t w_varshift = PV.get_varshift(Gr,'w')
Py_ssize_t s_varshift = PV.get_varshift(Gr,'s')
Py_ssize_t i,j,k
Py_ssize_t ishift, jshift, e_varshift
Py_ssize_t ijk
double [:] theta = np.empty((Gr.dims.nlg[2]),dtype=np.double,order='c')
double t
#Generate initial perturbations (here we are generating more than we need)
cdef double [:] theta_pert = np.random.random_sample(Gr.dims.npg)
cdef double theta_pert_
for k in xrange(Gr.dims.nlg[2]):
if Gr.zp_half[k] <= 974.0:
theta[k] = 300.0
elif Gr.zp_half[k] <= 1074.0:
theta[k] = 300.0 + (Gr.zp_half[k] - 974.0) * 0.08
else:
theta[k] = 308.0 + (Gr.zp_half[k] - 1074.0) * 0.003
cdef double [:] p0 = RS.p0_half
#Now loop and set the initial condition
for i in xrange(Gr.dims.nlg[0]):
ishift = i * Gr.dims.nlg[1] * Gr.dims.nlg[2]
for j in xrange(Gr.dims.nlg[1]):
jshift = j * Gr.dims.nlg[2]
for k in xrange(Gr.dims.nlg[2]):
ijk = ishift + jshift + k
PV.values[u_varshift + ijk] = 1.0 - RS.u0
PV.values[v_varshift + ijk] = 0.0 - RS.v0
PV.values[w_varshift + ijk] = 0.0
#Now set the entropy prognostic variable including a potential temperature perturbation
if Gr.zp_half[k] < 200.0:
theta_pert_ = (theta_pert[ijk] - 0.5)* 0.1
else:
theta_pert_ = 0.0
t = (theta[k] + theta_pert_)*exner_c(RS.p0_half[k])
PV.values[s_varshift + ijk] = Th.entropy(RS.p0_half[k],t,0.0,0.0,0.0)
if 'e' in PV.name_index:
e_varshift = PV.get_varshift(Gr, 'e')
for i in xrange(Gr.dims.nlg[0]):
ishift = i * Gr.dims.nlg[1] * Gr.dims.nlg[2]
for j in xrange(Gr.dims.nlg[1]):
jshift = j * Gr.dims.nlg[2]
for k in xrange(Gr.dims.nlg[2]):
ijk = ishift + jshift + k
PV.values[e_varshift + ijk] = 0.0
return
def InitBomex(namelist,Grid.Grid Gr,PrognosticVariables.PrognosticVariables PV,
ReferenceState.ReferenceState RS, Th, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa, LatentHeat LH ):
#First generate the reference profiles
RS.Pg = 1.015e5 #Pressure at ground
RS.Tg = 300.4 #Temperature at ground
RS.qtg = 0.02245 #Total water mixing ratio at surface
RS.initialize(Gr, Th, NS, Pa)
try:
random_seed_factor = namelist['initialization']['random_seed_factor']
except:
random_seed_factor = 1
np.random.seed(Pa.rank * random_seed_factor)
#Get the variable number for each of the velocity components
cdef:
Py_ssize_t u_varshift = PV.get_varshift(Gr,'u')
Py_ssize_t v_varshift = PV.get_varshift(Gr,'v')
Py_ssize_t w_varshift = PV.get_varshift(Gr,'w')
Py_ssize_t s_varshift
Py_ssize_t thli_varshift
Py_ssize_t qt_varshift = PV.get_varshift(Gr,'qt')
Py_ssize_t i,j,k
Py_ssize_t ishift, jshift
Py_ssize_t ijk, e_varshift
double temp
double qt_
double [:] thetal = np.empty((Gr.dims.nlg[2]),dtype=np.double,order='c')
double [:] qt = np.empty((Gr.dims.nlg[2]),dtype=np.double,order='c')
double [:] u = np.empty((Gr.dims.nlg[2]),dtype=np.double,order='c')
Py_ssize_t count
theta_pert = (np.random.random_sample(Gr.dims.npg )-0.5)*0.1
qt_pert = (np.random.random_sample(Gr.dims.npg )-0.5)*0.025/1000.0
for k in xrange(Gr.dims.nlg[2]):
#Set Thetal profile
if Gr.zp_half[k] <= 520.:
thetal[k] = 298.7
if Gr.zp_half[k] > 520.0 and Gr.zp_half[k] <= 1480.0:
thetal[k] = 298.7 + (Gr.zp_half[k] - 520) * (302.4 - 298.7)/(1480.0 - 520.0)
if Gr.zp_half[k] > 1480.0 and Gr.zp_half[k] <= 2000:
thetal[k] = 302.4 + (Gr.zp_half[k] - 1480.0) * (308.2 - 302.4)/(2000.0 - 1480.0)
if Gr.zp_half[k] > 2000.0:
thetal[k] = 308.2 + (Gr.zp_half[k] - 2000.0) * (311.85 - 308.2)/(3000.0 - 2000.0)
#Set qt profile
if Gr.zp_half[k] <= 520:
qt[k] = 17.0 + (Gr.zp_half[k]) * (16.3-17.0)/520.0
if Gr.zp_half[k] > 520.0 and Gr.zp_half[k] <= 1480.0:
qt[k] = 16.3 + (Gr.zp_half[k] - 520.0)*(10.7 - 16.3)/(1480.0 - 520.0)
if Gr.zp_half[k] > 1480.0 and Gr.zp_half[k] <= 2000.0:
qt[k] = 10.7 + (Gr.zp_half[k] - 1480.0) * (4.2 - 10.7)/(2000.0 - 1480.0)
if Gr.zp_half[k] > 2000.0:
qt[k] = 4.2 + (Gr.zp_half[k] - 2000.0) * (3.0 - 4.2)/(3000.0 - 2000.0)
#Change units to kg/kg
qt[k]/= 1000.0
#Set u profile
if Gr.zp_half[k] <= 700.0:
u[k] = -8.75
if Gr.zp_half[k] > 700.0:
u[k] = -8.75 + (Gr.zp_half[k] - 700.0) * (-4.61 - -8.75)/(3000.0 - 700.0)
#Set velocities for Galilean transformation
RS.v0 = 0.0
RS.u0 = 0.5 * (np.amax(u)+np.amin(u))
#Now loop and set the initial condition
#First set the velocities
count = 0
if 's' in PV.name_index:
s_varshift = PV.get_varshift(Gr,'s')
for i in xrange(Gr.dims.nlg[0]):
ishift = i * Gr.dims.nlg[1] * Gr.dims.nlg[2]
for j in xrange(Gr.dims.nlg[1]):
jshift = j * Gr.dims.nlg[2]
for k in xrange(Gr.dims.nlg[2]):
ijk = ishift + jshift + k
PV.values[u_varshift + ijk] = u[k] - RS.u0
PV.values[v_varshift + ijk] = 0.0 - RS.v0
PV.values[w_varshift + ijk] = 0.0
if Gr.zp_half[k] <= 1600.0:
temp = (thetal[k] + (theta_pert[count])) * exner_c(RS.p0_half[k])
qt_ = qt[k] + qt_pert[count]
else:
temp = (thetal[k]) * exner_c(RS.p0_half[k])
qt_ = qt[k]
PV.values[s_varshift + ijk] = Th.entropy(RS.p0_half[k],temp,qt_,0.0,0.0)
PV.values[qt_varshift + ijk] = qt_
count += 1
else:
thli_varshift = PV.get_varshift(Gr,'thli')
for i in xrange(Gr.dims.nlg[0]):
ishift = i * Gr.dims.nlg[1] * Gr.dims.nlg[2]
for j in xrange(Gr.dims.nlg[1]):
jshift = j * Gr.dims.nlg[2]
for k in xrange(Gr.dims.nlg[2]):
ijk = ishift + jshift + k
PV.values[u_varshift + ijk] = u[k] - RS.u0
PV.values[v_varshift + ijk] = 0.0 - RS.v0
PV.values[w_varshift + ijk] = 0.0
if Gr.zp_half[k] <= 1600.0:
PV.values[thli_varshift + ijk] = (thetal[k] + (theta_pert[count]))
PV.values[qt_varshift + ijk] = qt[k] + qt_pert[count]
else:
PV.values[thli_varshift + ijk] = thetal[k]
PV.values[qt_varshift + ijk] = qt[k]
count += 1
if 'e' in PV.name_index:
e_varshift = PV.get_varshift(Gr, 'e')
for i in xrange(Gr.dims.nlg[0]):
ishift = i * Gr.dims.nlg[1] * Gr.dims.nlg[2]
for j in xrange(Gr.dims.nlg[1]):
jshift = j * Gr.dims.nlg[2]
for k in xrange(Gr.dims.nlg[2]):
ijk = ishift + jshift + k
PV.values[e_varshift + ijk] = 1.0-Gr.zp_half[k]/3000.0
return
def InitGabls(namelist,Grid.Grid Gr, PrognosticVariables.PrognosticVariables PV,
ReferenceState.ReferenceState RS, Th, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa, LatentHeat LH ):
#Generate the reference profiles
RS.Pg = 1.0e5 #Pressure at ground
RS.Tg = 265.0 #Temperature at ground
RS.qtg = 0.0 #Total water mixing ratio at surface
RS.u0 = 8.0 # velocities removed in Galilean transformation
RS.v0 = 0.0
RS.initialize(Gr, Th, NS, Pa)
#Get the variable number for each of the velocity components
np.random.seed(Pa.rank)
cdef:
Py_ssize_t u_varshift = PV.get_varshift(Gr,'u')
Py_ssize_t v_varshift = PV.get_varshift(Gr,'v')
Py_ssize_t w_varshift = PV.get_varshift(Gr,'w')
Py_ssize_t s_varshift = PV.get_varshift(Gr,'s')
Py_ssize_t i,j,k
Py_ssize_t ishift, jshift, e_varshift
Py_ssize_t ijk
double [:] theta = np.empty((Gr.dims.nlg[2]),dtype=np.double,order='c')
double t
#Generate initial perturbations (here we are generating more than we need)
cdef double [:] theta_pert = np.random.random_sample(Gr.dims.npg)
cdef double theta_pert_
for k in xrange(Gr.dims.nlg[2]):
if Gr.zp_half[k] <= 100.0:
theta[k] = 265.0
else:
theta[k] = 265.0 + (Gr.zp_half[k] - 100.0) * 0.01
cdef double [:] p0 = RS.p0_half
#Now loop and set the initial condition
#First set the velocities
for i in xrange(Gr.dims.nlg[0]):
ishift = i * Gr.dims.nlg[1] * Gr.dims.nlg[2]
for j in xrange(Gr.dims.nlg[1]):
jshift = j * Gr.dims.nlg[2]
for k in xrange(Gr.dims.nlg[2]):
ijk = ishift + jshift + k
PV.values[u_varshift + ijk] = 8.0 - RS.u0
PV.values[v_varshift + ijk] = 0.0 - RS.v0
PV.values[w_varshift + ijk] = 0.0
#Now set the entropy prognostic variable including a potential temperature perturbation
if Gr.zp_half[k] < 50.0:
theta_pert_ = (theta_pert[ijk] - 0.5)* 0.1
else:
theta_pert_ = 0.0
t = (theta[k] + theta_pert_)*exner_c(RS.p0_half[k])
PV.values[s_varshift + ijk] = Th.entropy(RS.p0_half[k],t,0.0,0.0,0.0)
if 'e' in PV.name_index:
e_varshift = PV.get_varshift(Gr, 'e')
for i in xrange(Gr.dims.nlg[0]):
ishift = i * Gr.dims.nlg[1] * Gr.dims.nlg[2]
for j in xrange(Gr.dims.nlg[1]):
jshift = j * Gr.dims.nlg[2]
for k in xrange(Gr.dims.nlg[2]):
ijk = ishift + jshift + k
if Gr.zp_half[k] <= 250.0:
PV.values[e_varshift + ijk] = 0.4*(1.0-Gr.zp_half[k]/250.0)**3.0
else:
PV.values[e_varshift + ijk] = 0.0
return
def InitDYCOMS_RF01(namelist,Grid.Grid Gr,PrognosticVariables.PrognosticVariables PV,
ReferenceState.ReferenceState RS, Th, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa , LatentHeat LH):
'''
Initialize the DYCOMS_RF01 case described in
Bjorn Stevens, Chin-Hoh Moeng, Andrew S. Ackerman, Christopher S. Bretherton, Andreas Chlond, Stephan de Roode,
James Edwards, Jean-Christophe Golaz, Hongli Jiang, Marat Khairoutdinov, Michael P. Kirkpatrick, David C. Lewellen,
Adrian Lock, Frank Müller, David E. Stevens, Eoin Whelan, and Ping Zhu, 2005: Evaluation of Large-Eddy Simulations
via Observations of Nocturnal Marine Stratocumulus. Mon. Wea. Rev., 133, 1443–1462.
doi: http://dx.doi.org/10.1175/MWR2930.1
:param Gr: Grid cdef extension class
:param PV: PrognosticVariables cdef extension class
:param RS: ReferenceState cdef extension class
:param Th: Thermodynamics class
:return: None
'''
# Generate Reference Profiles
RS.Pg = 1017.8 * 100.0
RS.qtg = 9.0/1000.0
RS.u0 = 7.0
RS.v0 = -5.5
# Use an exner function with values for Rd, and cp given in Stevens 2004 to compute temperature given $\theta_l$
RS.Tg = 289.0 * (RS.Pg/p_tilde)**(287.0/1015.0)
RS.initialize(Gr ,Th, NS, Pa)
#Set up $\tehta_l$ and $\qt$ profiles
cdef:
Py_ssize_t i
Py_ssize_t j
Py_ssize_t k
Py_ssize_t ijk, ishift, jshift
Py_ssize_t istride = Gr.dims.nlg[1] * Gr.dims.nlg[2]
Py_ssize_t jstride = Gr.dims.nlg[2]
Py_ssize_t u_varshift = PV.get_varshift(Gr,'u')
Py_ssize_t v_varshift = PV.get_varshift(Gr,'v')
Py_ssize_t w_varshift = PV.get_varshift(Gr,'w')
Py_ssize_t s_varshift
Py_ssize_t thli_varshift
Py_ssize_t qt_varshift = PV.get_varshift(Gr,'qt')
double [:] thetal = np.zeros((Gr.dims.nlg[2],),dtype=np.double,order='c')
double [:] qt = np.zeros((Gr.dims.nlg[2],),dtype=np.double,order='c')
Py_ssize_t e_varshift
for k in xrange(Gr.dims.nlg[2]):
if Gr.zp_half[k] <=840.0:
thetal[k] = 289.0
qt[k] = 9.0/1000.0
if Gr.zp_half[k] > 840.0:
thetal[k] = 297.5 + (Gr.zp_half[k] - 840.0)**(1.0/3.0)
qt[k] = 1.5/1000.0
def compute_thetal(p_,T_,ql_):
theta_ = T_ / (p_/p_tilde)**(287.0/1015.0)
return theta_ * exp(-2.47e6 * ql_ / (1015.0 * T_))
def sat_adjst(p_,thetal_,qt_):
'''
Use saturation adjustment scheme to compute temperature and ql given thetal and qt.
:param p: pressure [Pa]
:param thetal: liquid water potential temperature [K]
:param qt: total water specific humidity
:return: T, ql
'''
#Compute temperature
t_1 = thetal_ * (p_/p_tilde)**(287.0/1015.0)
#Compute saturation vapor pressure
pv_star_1 = Th.get_pv_star(t_1)
#Compute saturation mixing ratio
qs_1 = qv_star_c(p_,qt_,pv_star_1)
if qt_ <= qs_1:
#If not saturated return temperature and ql = 0.0
return t_1, 0.0
else:
ql_1 = qt_ - qs_1
f_1 = thetal_ - compute_thetal(p_,t_1,ql_1)
t_2 = t_1 + 2.47e6*ql_1/1015.0
pv_star_2 = Th.get_pv_star(t_2)
qs_2 = qv_star_c(p_,qt_,pv_star_2)
ql_2 = qt_ - qs_2
while fabs(t_2 - t_1) >= 1e-9:
pv_star_2 = Th.get_pv_star(t_2)
qs_2 = qv_star_c(p_,qt_,pv_star_2)
ql_2 = qt_ - qs_2
f_2 = thetal_ - compute_thetal(p_, t_2, ql_2)
t_n = t_2 - f_2 * (t_2 - t_1)/(f_2 - f_1)
t_1 = t_2
t_2 = t_n
f_1 = f_2
return t_2, ql_2
#Generate initial perturbations (here we are generating more than we need)
np.random.seed(Pa.rank)
cdef double [:] theta_pert = np.random.random_sample(Gr.dims.npg)
cdef double theta_pert_
if 's' in PV.name_index:
s_varshift = PV.get_varshift(Gr, 's')
for i in xrange(Gr.dims.nlg[0]):
ishift = istride * i
for j in xrange(Gr.dims.nlg[1]):
jshift = jstride * j
for k in xrange(Gr.dims.nlg[2]):
ijk = ishift + jshift + k
PV.values[ijk + u_varshift] = 0.0
PV.values[ijk + v_varshift] = 0.0
PV.values[ijk + w_varshift] = 0.0
PV.values[ijk + qt_varshift] = qt[k]
#Now set the entropy prognostic variable including a potential temperature perturbation
if Gr.zp_half[k] < 200.0:
theta_pert_ = (theta_pert[ijk] - 0.5)* 0.1
else:
theta_pert_ = 0.0
T,ql = sat_adjst(RS.p0_half[k],thetal[k] + theta_pert_,qt[k])
PV.values[ijk + s_varshift] = Th.entropy(RS.p0_half[k], T, qt[k], ql, 0.0)
else:
thli_varshift = PV.get_varshift(Gr, 'thli')
for i in xrange(Gr.dims.nlg[0]):
ishift = istride * i
for j in xrange(Gr.dims.nlg[1]):
jshift = jstride * j
for k in xrange(Gr.dims.nlg[2]):
ijk = ishift + jshift + k
PV.values[ijk + u_varshift] = 0.0
PV.values[ijk + v_varshift] = 0.0
PV.values[ijk + w_varshift] = 0.0
PV.values[ijk + qt_varshift] = qt[k]
if Gr.zp_half[k] < 200.0:
theta_pert_ = (theta_pert[ijk] - 0.5)* 0.1
else:
theta_pert_ = 0.0
PV.values[ijk + thli_varshift] = thetal[k] + theta_pert_
if 'e' in PV.name_index:
e_varshift = PV.get_varshift(Gr, 'e')
for i in xrange(Gr.dims.nlg[0]):
ishift = i * Gr.dims.nlg[1] * Gr.dims.nlg[2]
for j in xrange(Gr.dims.nlg[1]):
jshift = j * Gr.dims.nlg[2]
for k in xrange(Gr.dims.nlg[2]):
ijk = ishift + jshift + k
if Gr.zp_half[k] < 200.0:
PV.values[e_varshift + ijk] = 0.0
return
def InitDYCOMS_RF02(namelist,Grid.Grid Gr,PrognosticVariables.PrognosticVariables PV,
ReferenceState.ReferenceState RS, Th, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa, LatentHeat LH ):
# Generate Reference Profiles
RS.Pg = 1017.8 * 100.0
RS.qtg = 9.0/1000.0
RS.u0 = 5.0
RS.v0 = -5.5
cdef double cp_ref = 1004.0
cdef double L_ref = 2.5e6
# Use an exner function with values for Rd, and cp given in Stevens 2004 to compute temperature given $\theta_l$
RS.Tg = 288.3 * (RS.Pg/p_tilde)**(287.0/cp_ref)
RS.initialize(Gr ,Th, NS, Pa)
#Set up $\tehta_l$ and $\qt$ profiles
cdef:
Py_ssize_t i
Py_ssize_t j
Py_ssize_t k
Py_ssize_t ijk, ishift, jshift
Py_ssize_t istride = Gr.dims.nlg[1] * Gr.dims.nlg[2]
Py_ssize_t jstride = Gr.dims.nlg[2]
Py_ssize_t u_varshift = PV.get_varshift(Gr,'u')
Py_ssize_t v_varshift = PV.get_varshift(Gr,'v')
Py_ssize_t w_varshift = PV.get_varshift(Gr,'w')
Py_ssize_t s_varshift = PV.get_varshift(Gr,'s')
Py_ssize_t qt_varshift = PV.get_varshift(Gr,'qt')
double [:] thetal = np.zeros((Gr.dims.nlg[2],),dtype=np.double,order='c')
double [:] qt = np.zeros((Gr.dims.nlg[2],),dtype=np.double,order='c')
double [:] u = np.zeros((Gr.dims.nlg[2],),dtype=np.double,order='c')
double [:] v = np.zeros((Gr.dims.nlg[2],),dtype=np.double,order='c')
for k in xrange(Gr.dims.nlg[2]):
if Gr.zp_half[k] <=795.0:
thetal[k] = 288.3
qt[k] = 9.45/1000.0
if Gr.zp_half[k] > 795.0:
thetal[k] = 295.0 + (Gr.zp_half[k] - 795.0)**(1.0/3.0)
qt[k] = (5.0 - 3.0 * (1.0 - np.exp(-(Gr.zp_half[k] - 795.0)/500.0)))/1000.0
v[k] = -9.0 + 5.6 * Gr.zp_half[k]/1000.0 - RS.v0
u[k] = 3.0 + 4.3*Gr.zp_half[k]/1000.0 - RS.u0
def compute_thetal(p_,T_,ql_):
theta_ = T_ / (p_/p_tilde)**(287.0/cp_ref)
return theta_ * exp(-L_ref * ql_ / (cp_ref * T_))
def sat_adjst(p_,thetal_,qt_):
'''
Use saturation adjustment scheme to compute temperature and ql given thetal and qt.
:param p: pressure [Pa]
:param thetal: liquid water potential temperature [K]
:param qt: total water specific humidity
:return: T, ql
'''
#Compute temperature
t_1 = thetal_ * (p_/p_tilde)**(287.0/cp_ref)
#Compute saturation vapor pressure
pv_star_1 = Th.get_pv_star(t_1)
#Compute saturation mixing ratio
qs_1 = qv_star_c(p_,qt_,pv_star_1)
if qt_ <= qs_1:
#If not saturated return temperature and ql = 0.0
return t_1, 0.0
else:
ql_1 = qt_ - qs_1
f_1 = thetal_ - compute_thetal(p_,t_1,ql_1)
t_2 = t_1 + L_ref*ql_1/cp_ref
pv_star_2 = Th.get_pv_star(t_2)
qs_2 = qv_star_c(p_,qt_,pv_star_2)
ql_2 = qt_ - qs_2
while fabs(t_2 - t_1) >= 1e-9:
pv_star_2 = Th.get_pv_star(t_2)
qs_2 = qv_star_c(p_,qt_,pv_star_2)
ql_2 = qt_ - qs_2
f_2 = thetal_ - compute_thetal(p_, t_2, ql_2)
t_n = t_2 - f_2 * (t_2 - t_1)/(f_2 - f_1)
t_1 = t_2
t_2 = t_n
f_1 = f_2
return t_2, ql_2
#Generate initial perturbations (here we are generating more than we need)
np.random.seed(Pa.rank)
cdef double [:] theta_pert = np.random.random_sample(Gr.dims.npg)
cdef double theta_pert_
for i in xrange(Gr.dims.nlg[0]):
ishift = istride * i
for j in xrange(Gr.dims.nlg[1]):
jshift = jstride * j
for k in xrange(Gr.dims.nlg[2]):
ijk = ishift + jshift + k
PV.values[ijk + u_varshift] = u[k]
PV.values[ijk + v_varshift] = v[k]
PV.values[ijk + w_varshift] = 0.0
PV.values[ijk + qt_varshift] = qt[k]
#Now set the entropy prognostic variable including a potential temperature perturbation
if Gr.zp_half[k] < 795.0:
theta_pert_ = (theta_pert[ijk] - 0.5)* 0.1
else:
theta_pert_ = 0.0
T,ql = sat_adjst(RS.p0_half[k],thetal[k] + theta_pert_,qt[k])
PV.values[ijk + s_varshift] = Th.entropy(RS.p0_half[k], T, qt[k], ql, 0.0)
return
def InitSmoke(namelist,Grid.Grid Gr,PrognosticVariables.PrognosticVariables PV,
ReferenceState.ReferenceState RS, Th, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa, LatentHeat LH ):
'''
Initialization for the smoke cloud case
Bretherton, C. S., and coauthors, 1999:
An intercomparison of radiatively- driven entrainment and turbulence in a smoke cloud,
as simulated by different numerical models. Quart. J. Roy. Meteor. Soc., 125, 391-423. Full text copy.
:param Gr:
:param PV:
:param RS:
:param Th:
:param NS:
:param Pa:
:return:
'''
RS.Pg = 1000.0 * 100.0
RS.qtg = 0.0
RS.u0 = 0.0
RS.v0 = 0.0
RS.Tg = 288.0
RS.initialize(Gr ,Th, NS, Pa)
#Get the variable number for each of the velocity components
np.random.seed(Pa.rank)
cdef:
Py_ssize_t u_varshift = PV.get_varshift(Gr, 'u')
Py_ssize_t v_varshift = PV.get_varshift(Gr, 'v')
Py_ssize_t w_varshift = PV.get_varshift(Gr, 'w')
Py_ssize_t s_varshift = PV.get_varshift(Gr, 's')
Py_ssize_t smoke_varshift = PV.get_varshift(Gr, 'smoke')
Py_ssize_t i,j,k
Py_ssize_t ishift, jshift, e_varshift
Py_ssize_t ijk
double [:] theta = np.empty((Gr.dims.nlg[2]), dtype=np.double, order='c')
double [:] smoke = np.empty((Gr.dims.nlg[2]), dtype=np.double, order='c')
double t
#Generate initial perturbations (here we are generating more than we need)
cdef double [:] theta_pert = np.random.random_sample(Gr.dims.npg)
cdef double theta_pert_
for k in xrange(Gr.dims.nlg[2]):
if Gr.zp_half[k] <= 687.5:
theta[k] = 288.0
smoke[k] = 1.0
elif Gr.zp_half[k] >= 687.5 and Gr.zp_half[k] <= 712.5:
theta[k] = 288.0 + (Gr.zp_half[k] - 687.5) * 0.28
smoke[k] = 1.0 - 0.04 * (Gr.zp_half[k] - 687.5)
print k, Gr.zp_half[k], smoke[k]
else:
theta[k] = 295.0 + (Gr.zp_half[k] - 712.5) * 1e-4
smoke[k] = 0.0
cdef double [:] p0 = RS.p0_half
#Now loop and set the initial condition
for i in xrange(Gr.dims.nlg[0]):
ishift = i * Gr.dims.nlg[1] * Gr.dims.nlg[2]
for j in xrange(Gr.dims.nlg[1]):
jshift = j * Gr.dims.nlg[2]
for k in xrange(Gr.dims.nlg[2]):
ijk = ishift + jshift + k
PV.values[u_varshift + ijk] = 0.0 - RS.u0
PV.values[v_varshift + ijk] = 0.0 - RS.v0
PV.values[w_varshift + ijk] = 0.0
#Now set the entropy prognostic variable including a potential temperature perturbation
if Gr.zp_half[k] < 700.0:
theta_pert_ = (theta_pert[ijk] - 0.5)* 0.1
else:
theta_pert_ = 0.0
t = (theta[k] + theta_pert_)*exner_c(RS.p0_half[k])
PV.values[s_varshift + ijk] = Th.entropy(RS.p0_half[k],t,0.0,0.0,0.0)
PV.values[smoke_varshift + ijk] = smoke[k]
if 'e' in PV.name_index:
e_varshift = PV.get_varshift(Gr, 'e')
for i in xrange(Gr.dims.nlg[0]):
ishift = i * Gr.dims.nlg[1] * Gr.dims.nlg[2]
for j in xrange(Gr.dims.nlg[1]):
jshift = j * Gr.dims.nlg[2]
for k in xrange(Gr.dims.nlg[2]):
ijk = ishift + jshift + k
if Gr.zp_half[k] < 700.0:
PV.values[e_varshift + ijk] = 0.1
else:
PV.values[e_varshift + ijk] = 0.0
return
def InitRico(namelist,Grid.Grid Gr,PrognosticVariables.PrognosticVariables PV,
ReferenceState.ReferenceState RS, Th, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa, LatentHeat LH ):
#First generate the reference profiles
RS.Pg = 1.0154e5 #Pressure at ground
RS.Tg = 299.8 #Temperature at ground
pvg = Th.get_pv_star(RS.Tg)
RS.qtg = eps_v * pvg/(RS.Pg - pvg) #Total water mixing ratio at surface = qsat
RS.initialize(Gr, Th, NS, Pa)
#Get the variable number for each of the velocity components
np.random.seed(Pa.rank)
cdef:
Py_ssize_t u_varshift = PV.get_varshift(Gr,'u')
Py_ssize_t v_varshift = PV.get_varshift(Gr,'v')
Py_ssize_t w_varshift = PV.get_varshift(Gr,'w')
Py_ssize_t s_varshift = PV.get_varshift(Gr,'s')
Py_ssize_t qt_varshift = PV.get_varshift(Gr,'qt')
Py_ssize_t i,j,k
Py_ssize_t ishift, jshift
Py_ssize_t ijk, e_varshift
double temp
double qt_
double [:] theta = np.empty((Gr.dims.nlg[2]),dtype=np.double,order='c')
double [:] qt = np.empty((Gr.dims.nlg[2]),dtype=np.double,order='c')
double [:] u = np.empty((Gr.dims.nlg[2]),dtype=np.double,order='c')
double [:] v = np.empty((Gr.dims.nlg[2]),dtype=np.double,order='c')
Py_ssize_t count
theta_pert = (np.random.random_sample(Gr.dims.npg )-0.5)*0.1
qt_pert = (np.random.random_sample(Gr.dims.npg )-0.5) * 2.5e-5
for k in xrange(Gr.dims.nlg[2]):
#Set Thetal profile
if Gr.zp_half[k] <= 740.0:
theta[k] = 297.9
else:
theta[k] = 297.9 + (317.0-297.9)/(4000.0-740.0)*(Gr.zp_half[k] - 740.0)
#Set qt profile
if Gr.zp_half[k] <= 740.0:
qt[k] = 16.0 + (13.8 - 16.0)/740.0 * Gr.zp_half[k]
elif Gr.zp_half[k] > 740.0 and Gr.zp_half[k] <= 3260.0:
qt[k] = 13.8 + (2.4 - 13.8)/(3260.0-740.0) * (Gr.zp_half[k] - 740.0)
else:
qt[k] = 2.4 + (1.8-2.4)/(4000.0-3260.0)*(Gr.zp_half[k] - 3260.0)
#Change units to kg/kg
qt[k]/= 1000.0
#Set u profile
u[k] = -9.9 + 2.0e-3 * Gr.zp_half[k]
#set v profile
v[k] = -3.8
#Set velocities for Galilean transformation
RS.v0 = -3.8
RS.u0 = 0.5 * (np.amax(u)+np.amin(u))
#Now loop and set the initial condition
#First set the velocities
count = 0
for i in xrange(Gr.dims.nlg[0]):
ishift = i * Gr.dims.nlg[1] * Gr.dims.nlg[2]
for j in xrange(Gr.dims.nlg[1]):
jshift = j * Gr.dims.nlg[2]
for k in xrange(Gr.dims.nlg[2]):
ijk = ishift + jshift + k
PV.values[u_varshift + ijk] = u[k] - RS.u0
PV.values[v_varshift + ijk] = v[k] - RS.v0
PV.values[w_varshift + ijk] = 0.0
if Gr.zp_half[k] <= 740.0:
temp = (theta[k] + (theta_pert[count])) * exner_c(RS.p0_half[k])
qt_ = qt[k]+qt_pert[count]
else:
temp = (theta[k]) * exner_c(RS.p0_half[k])
qt_ = qt[k]
PV.values[s_varshift + ijk] = Th.entropy(RS.p0_half[k],temp,qt_,0.0,0.0)
PV.values[qt_varshift + ijk] = qt_
count += 1
if 'e' in PV.name_index:
e_varshift = PV.get_varshift(Gr, 'e')
for i in xrange(Gr.dims.nlg[0]):
ishift = i * Gr.dims.nlg[1] * Gr.dims.nlg[2]
for j in xrange(Gr.dims.nlg[1]):
jshift = j * Gr.dims.nlg[2]