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Forcing.pyx
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Forcing.pyx
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#!python
#cython: boundscheck=False
#cython: wraparound=False
#cython: initializedcheck=False
#cython: cdivision=True
import netCDF4 as nc
cimport Grid
cimport ReferenceState
cimport PrognosticVariables
cimport DiagnosticVariables
cimport TimeStepping
cimport ForcingGCMFixed
cimport ForcingGCMVarying
from thermodynamic_functions cimport cpm_c, pv_c, pd_c, exner_c
from entropies cimport sv_c, sd_c, s_tendency_c
import numpy as np
import cython
from libc.math cimport fabs, sin, cos, exp
from NetCDFIO cimport NetCDFIO_Stats
cimport ParallelMPI
cimport Lookup
from Thermodynamics cimport LatentHeat, ClausiusClapeyron
import pickle as pickle
from scipy.interpolate import pchip
include 'parameters.pxi'
cdef class Forcing:
def __init__(self, namelist, LatentHeat LH, ParallelMPI.ParallelMPI Pa):
casename = namelist['meta']['casename']
if casename == 'SullivanPatton':
self.scheme = ForcingSullivanPatton()
elif casename == 'Bomex' or casename == 'lifecycle_Tan2018':
self.scheme = ForcingBomex()
elif casename == 'Soares':
self.scheme = ForcingSoares()
elif casename == 'Soares_moist':
self.scheme = ForcingSoares()
elif casename == 'Gabls':
self.scheme = ForcingGabls()
elif casename == 'DYCOMS_RF01':
self.scheme = ForcingDyCOMS_RF01(casename)
elif casename == 'DYCOMS_RF02':
#Forcing for DYCOMS_RF02 is same as DYCOMS_RF01
self.scheme = ForcingDyCOMS_RF01(casename)
elif casename == 'SMOKE':
self.scheme = ForcingNone()
elif casename == 'Rico':
self.scheme = ForcingRico()
elif casename == 'StableBubble':
self.scheme = ForcingNone()
elif casename == 'SaturatedBubble':
self.scheme = ForcingNone()
elif casename == 'CGILS':
self.scheme = ForcingCGILS(namelist, Pa)
elif casename == 'ZGILS':
self.scheme = ForcingZGILS(namelist, LH, Pa)
elif casename == 'GCMVarying':
self.scheme = ForcingGCMVarying.ForcingGCMVarying(namelist, LH, Pa)
elif casename == 'GCMNew':
self.scheme = ForcingGCMFixed.ForcingGCMNew(namelist, LH, Pa)
elif casename == 'TRMM_LBA':
self.scheme = ForcingTRMM_LBA(namelist)
elif casename == 'ARM_SGP':
self.scheme = ForcingARM_SGP(namelist)
elif casename == 'GATE_III':
self.scheme = ForcingGATE_III(namelist)
else:
Pa.root_print('No focing for casename: ' + casename)
Pa.root_print('Killing simulation now!!!')
Pa.kill()
return
cpdef initialize(self, Grid.Grid Gr,ReferenceState.ReferenceState RS, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
self.scheme.initialize(Gr, RS, NS, Pa)
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState RS,
PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV, TimeStepping.TimeStepping TS,
ParallelMPI.ParallelMPI Pa):
self.scheme.update(Gr, RS, PV, DV, TS, Pa)
return
cpdef stats_io(self, Grid.Grid Gr, ReferenceState.ReferenceState RS,
PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV,
NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
self.scheme.stats_io(Gr, RS, PV, DV, NS, Pa)
return
cdef class ForcingNone:
def __init__(self):
pass
cpdef initialize(self, Grid.Grid Gr, ReferenceState.ReferenceState RS,NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState RS,
PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV, TimeStepping.TimeStepping TS,
ParallelMPI.ParallelMPI Pa):
return
cpdef stats_io(self, Grid.Grid Gr, ReferenceState.ReferenceState RS,
PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV,
NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
return
cdef class ForcingBomex:
def __init__(self):
return
cpdef initialize(self, Grid.Grid Gr,ReferenceState.ReferenceState RS, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
self.ug = np.zeros(Gr.dims.nlg[2],dtype=np.double,order='c')
self.vg = np.zeros(Gr.dims.nlg[2],dtype=np.double,order='c')
self.dtdt = np.zeros(Gr.dims.nlg[2],dtype=np.double,order='c')
self.dqtdt = np.zeros(Gr.dims.nlg[2],dtype=np.double,order='c')
self.subsidence = np.zeros(Gr.dims.nlg[2],dtype=np.double,order='c')
self.coriolis_param = 0.376e-4 #s^{-1}
cdef:
Py_ssize_t k
with nogil:
for k in xrange(Gr.dims.nlg[2]):
self.ug[k] = -10.0 + (1.8e-3)*Gr.zpl_half[k]
#Set large scale cooling
# Convert given form of tendencies (theta) to temperature tendency
if Gr.zpl_half[k] <= 1500.0:
self.dtdt[k] = (-2.0/(3600 * 24.0)) * exner_c(RS.p0_half[k]) #K/s
if Gr.zpl_half[k] > 1500.0:
self.dtdt[k] = (-2.0/(3600 * 24.0) + (Gr.zpl_half[k] - 1500.0)
* (0.0 - -2.0/(3600 * 24.0)) / (3000.0 - 1500.0)) * exner_c(RS.p0_half[k])
#Set large scale drying
if Gr.zpl_half[k] <= 300.0:
self.dqtdt[k] = -1.2e-8 #kg/(kg * s)
if Gr.zpl_half[k] > 300.0 and Gr.zpl_half[k] <= 500.0:
self.dqtdt[k] = -1.2e-8 + (Gr.zpl_half[k] - 300.0)*(0.0 - -1.2e-8)/(500.0 - 300.0) #kg/(kg * s)
#Set large scale subsidence
if Gr.zpl_half[k] <= 1500.0:
self.subsidence[k] = 0.0 + Gr.zpl_half[k]*(-0.65/100.0 - 0.0)/(1500.0 - 0.0)
if Gr.zpl_half[k] > 1500.0 and Gr.zpl_half[k] <= 2100.0:
self.subsidence[k] = -0.65/100 + (Gr.zpl_half[k] - 1500.0)* (0.0 - -0.65/100.0)/(2100.0 - 1500.0)
#Initialize Statistical Output
NS.add_profile('thli_subsidence_tendency', Gr, Pa)
NS.add_profile('s_subsidence_tendency', Gr, Pa)
NS.add_profile('qt_subsidence_tendency', Gr, Pa)
NS.add_profile('u_subsidence_tendency', Gr, Pa)
NS.add_profile('v_subsidence_tendency', Gr, Pa)
NS.add_profile('u_coriolis_tendency', Gr, Pa)
NS.add_profile('v_coriolis_tendency',Gr, Pa)
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState RS,
PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV, TimeStepping.TimeStepping TS,
ParallelMPI.ParallelMPI Pa):
cdef:
Py_ssize_t imin = Gr.dims.gw
Py_ssize_t jmin = Gr.dims.gw
Py_ssize_t kmin = Gr.dims.gw
Py_ssize_t imax = Gr.dims.nlg[0] - Gr.dims.gw
Py_ssize_t jmax = Gr.dims.nlg[1] - Gr.dims.gw
Py_ssize_t kmax = Gr.dims.nlg[2] - Gr.dims.gw
Py_ssize_t istride = Gr.dims.nlg[1] * Gr.dims.nlg[2]
Py_ssize_t jstride = Gr.dims.nlg[2]
Py_ssize_t i,j,k,ishift,jshift,ijk
Py_ssize_t u_shift = PV.get_varshift(Gr, 'u')
Py_ssize_t v_shift = PV.get_varshift(Gr, 'v')
Py_ssize_t s_shift
Py_ssize_t qt_shift = PV.get_varshift(Gr, 'qt')
Py_ssize_t t_shift = DV.get_varshift(Gr, 'temperature')
Py_ssize_t ql_shift = DV.get_varshift(Gr,'ql')
double qt, qv, p0, t
double [:] umean = Pa.HorizontalMean(Gr, &PV.values[u_shift])
double [:] vmean = Pa.HorizontalMean(Gr, &PV.values[v_shift])
#Apply Coriolis Forcing
large_scale_p_gradient(&Gr.dims, &umean[0], &vmean[0], &PV.tendencies[u_shift],
&PV.tendencies[v_shift], &self.ug[0], &self.vg[0], self.coriolis_param, RS.u0, RS.v0)
s_shift = PV.get_varshift(Gr, 's')
#Apply large scale source terms
with nogil:
for i in xrange(imin,imax):
ishift = i * istride
for j in xrange(jmin,jmax):
jshift = j * jstride
for k in xrange(kmin,kmax):
ijk = ishift + jshift + k
p0 = RS.p0_half[k]
qt = PV.values[qt_shift + ijk]
qv = qt - DV.values[ql_shift + ijk]
t = DV.values[t_shift + ijk]
PV.tendencies[s_shift + ijk] += s_tendency_c(p0,qt,qv,t, self.dqtdt[k], self.dtdt[k])
PV.tendencies[qt_shift + ijk] += self.dqtdt[k]
apply_subsidence(&Gr.dims, &RS.rho0[0], &RS.rho0_half[0], &self.subsidence[0], &PV.values[s_shift], &PV.tendencies[s_shift])
apply_subsidence(&Gr.dims, &RS.rho0[0], &RS.rho0_half[0], &self.subsidence[0], &PV.values[qt_shift], &PV.tendencies[qt_shift])
apply_subsidence(&Gr.dims, &RS.rho0[0], &RS.rho0_half[0], &self.subsidence[0], &PV.values[u_shift], &PV.tendencies[u_shift])
apply_subsidence(&Gr.dims, &RS.rho0[0], &RS.rho0_half[0], &self.subsidence[0], &PV.values[v_shift], &PV.tendencies[v_shift])
return
cpdef stats_io(self, Grid.Grid Gr, ReferenceState.ReferenceState RS,
PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV,
NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
cdef:
Py_ssize_t u_shift = PV.get_varshift(Gr, 'u')
Py_ssize_t v_shift = PV.get_varshift(Gr, 'v')
Py_ssize_t s_shift
Py_ssize_t thli_shift
Py_ssize_t qt_shift = PV.get_varshift(Gr, 'qt')
double [:] tmp_tendency = np.zeros((Gr.dims.npg),dtype=np.double,order='c')
double [:] tmp_tendency_2 = np.zeros((Gr.dims.npg),dtype=np.double,order='c')
double [:] mean_tendency = np.empty((Gr.dims.nlg[2],),dtype=np.double,order='c')
double [:] mean_tendency_2 = np.zeros((Gr.dims.nlg[2]),dtype=np.double,order='c')
double [:] umean = Pa.HorizontalMean(Gr, &PV.values[u_shift])
double [:] vmean = Pa.HorizontalMean(Gr, &PV.values[v_shift])
#Output subsidence tendencies
#Output entropy tendency from subsidence
s_shift = PV.get_varshift(Gr, 's')
apply_subsidence(&Gr.dims,&RS.rho0[0],&RS.rho0_half[0],&self.subsidence[0],&PV.values[s_shift],
&tmp_tendency[0])
mean_tendency = Pa.HorizontalMean(Gr,&tmp_tendency[0])
NS.write_profile('s_subsidence_tendency',mean_tendency[Gr.dims.gw:-Gr.dims.gw],Pa)
tmp_tendency[:] = 0.0
#Output thetali tendency from subsidence (diagnostic source term)
thli_shift = DV.get_varshift(Gr, 'thetali')
apply_subsidence(&Gr.dims,&RS.rho0[0],&RS.rho0_half[0],&self.subsidence[0],&DV.values[thli_shift],
&tmp_tendency[0])
mean_tendency = Pa.HorizontalMean(Gr,&tmp_tendency[0])
NS.write_profile('thli_subsidence_tendency',mean_tendency[Gr.dims.gw:-Gr.dims.gw],Pa)
tmp_tendency[:] = 0.0
apply_subsidence(&Gr.dims,&RS.rho0[0],&RS.rho0_half[0],&self.subsidence[0],&PV.values[qt_shift],
&tmp_tendency[0])
mean_tendency = Pa.HorizontalMean(Gr,&tmp_tendency[0])
NS.write_profile('qt_subsidence_tendency',mean_tendency[Gr.dims.gw:-Gr.dims.gw],Pa)
tmp_tendency[:] = 0.0
apply_subsidence(&Gr.dims,&RS.rho0[0],&RS.rho0_half[0],&self.subsidence[0],&PV.values[u_shift],
&tmp_tendency[0])
mean_tendency = Pa.HorizontalMean(Gr,&tmp_tendency[0])
NS.write_profile('u_subsidence_tendency',mean_tendency[Gr.dims.gw:-Gr.dims.gw],Pa)
tmp_tendency[:] = 0.0
apply_subsidence(&Gr.dims,&RS.rho0[0],&RS.rho0_half[0],&self.subsidence[0],&PV.values[v_shift],
&tmp_tendency[0])
mean_tendency = Pa.HorizontalMean(Gr,&tmp_tendency[0])
NS.write_profile('v_subsidence_tendency',mean_tendency[Gr.dims.gw:-Gr.dims.gw],Pa)
#Output Coriolis tendencies
tmp_tendency[:] = 0.0
large_scale_p_gradient(&Gr.dims, &umean[0], &vmean[0], &tmp_tendency[0],
&tmp_tendency_2[0], &self.ug[0], &self.vg[0], self.coriolis_param, RS.u0, RS.v0)
mean_tendency = Pa.HorizontalMean(Gr,&tmp_tendency[0])
mean_tendency_2 = Pa.HorizontalMean(Gr,&tmp_tendency_2[0])
NS.write_profile('u_coriolis_tendency',mean_tendency[Gr.dims.gw:-Gr.dims.gw],Pa)
NS.write_profile('v_coriolis_tendency',mean_tendency_2[Gr.dims.gw:-Gr.dims.gw],Pa)
return
cdef class ForcingSoares:
def __init__(self):
return
cpdef initialize(self, Grid.Grid Gr, ReferenceState.ReferenceState RS, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
# self.ug = np.ones(Gr.dims.nlg[2],dtype=np.double, order='c') #m/s
# self.vg = np.zeros(Gr.dims.nlg[2],dtype=np.double, order='c') #m/s
# self.coriolis_param = 0.0 #s^{-1}
# NS.add_profile('u_coriolis_tendency', Gr, Pa)
# NS.add_profile('v_coriolis_tendency',Gr, Pa)
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState RS,
PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV, TimeStepping.TimeStepping TS,
ParallelMPI.ParallelMPI Pa):
return
cpdef stats_io(self, Grid.Grid Gr, ReferenceState.ReferenceState RS,
PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV,
NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
return
cdef class ForcingSullivanPatton:
def __init__(self):
return
cpdef initialize(self, Grid.Grid Gr,ReferenceState.ReferenceState RS, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
self.ug = np.ones(Gr.dims.nlg[2],dtype=np.double, order='c') #m/s
self.vg = np.zeros(Gr.dims.nlg[2],dtype=np.double, order='c') #m/s
self.coriolis_param = 1.0e-4 #s^{-1}
NS.add_profile('u_coriolis_tendency', Gr, Pa)
NS.add_profile('v_coriolis_tendency',Gr, Pa)
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState RS,
PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV, TimeStepping.TimeStepping TS,
ParallelMPI.ParallelMPI Pa):
cdef:
Py_ssize_t u_shift = PV.get_varshift(Gr, 'u')
Py_ssize_t v_shift = PV.get_varshift(Gr, 'v')
coriolis_force(&Gr.dims,&PV.values[u_shift],&PV.values[v_shift],&PV.tendencies[u_shift],
&PV.tendencies[v_shift],&self.ug[0], &self.vg[0],self.coriolis_param, RS.u0, RS.v0 )
return
cpdef stats_io(self, Grid.Grid Gr, ReferenceState.ReferenceState RS,
PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV,
NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
cdef:
Py_ssize_t u_shift = PV.get_varshift(Gr, 'u')
Py_ssize_t v_shift = PV.get_varshift(Gr, 'v')
double [:] tmp_tendency = np.zeros((Gr.dims.npg),dtype=np.double,order='c')
double [:] tmp_tendency_2 = np.zeros((Gr.dims.npg),dtype=np.double,order='c')
double [:] mean_tendency = np.empty((Gr.dims.nlg[2],),dtype=np.double,order='c')
double [:] mean_tendency_2 = np.zeros((Gr.dims.nlg[2]),dtype=np.double,order='c')
#Only need to output coriolis_forcing
coriolis_force(&Gr.dims,&PV.values[u_shift],&PV.values[v_shift],&tmp_tendency[0],
&tmp_tendency_2[0],&self.ug[0], &self.vg[0],self.coriolis_param, RS.u0, RS.v0)
mean_tendency = Pa.HorizontalMean(Gr,&tmp_tendency[0])
mean_tendency_2 = Pa.HorizontalMean(Gr,&tmp_tendency_2[0])
NS.write_profile('u_coriolis_tendency',mean_tendency[Gr.dims.gw:-Gr.dims.gw],Pa)
NS.write_profile('v_coriolis_tendency',mean_tendency_2[Gr.dims.gw:-Gr.dims.gw],Pa)
return
cdef class ForcingGabls:
def __init__(self):
return
cpdef initialize(self, Grid.Grid Gr,ReferenceState.ReferenceState RS, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
self.ug = np.ones(Gr.dims.nlg[2],dtype=np.double,order='c') * 8.0
self.vg = np.zeros(Gr.dims.nlg[2],dtype=np.double,order='c')
self.coriolis_param = 1.39e-4 #s^{-1}
NS.add_profile('u_coriolis_tendency', Gr, Pa)
NS.add_profile('v_coriolis_tendency',Gr, Pa)
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState RS,
PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV, TimeStepping.TimeStepping TS,
ParallelMPI.ParallelMPI Pa):
cdef:
Py_ssize_t u_shift = PV.get_varshift(Gr, 'u')
Py_ssize_t v_shift = PV.get_varshift(Gr, 'v')
coriolis_force(&Gr.dims,&PV.values[u_shift],&PV.values[v_shift],&PV.tendencies[u_shift],
&PV.tendencies[v_shift],&self.ug[0], &self.vg[0],self.coriolis_param, RS.u0, RS.v0 )
return
cpdef stats_io(self, Grid.Grid Gr, ReferenceState.ReferenceState RS,
PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV,
NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
cdef:
Py_ssize_t u_shift = PV.get_varshift(Gr, 'u')
Py_ssize_t v_shift = PV.get_varshift(Gr, 'v')
double [:] tmp_tendency = np.zeros((Gr.dims.npg),dtype=np.double,order='c')
double [:] tmp_tendency_2 = np.zeros((Gr.dims.npg),dtype=np.double,order='c')
double [:] mean_tendency = np.empty((Gr.dims.npg,),dtype=np.double,order='c')
double [:] mean_tendency_2 = np.zeros((Gr.dims.npg),dtype=np.double,order='c')
#Only need to output coriolis_forcing
coriolis_force(&Gr.dims,&PV.values[u_shift],&PV.values[v_shift],&tmp_tendency[0],
&tmp_tendency_2[0],&self.ug[0], &self.vg[0],self.coriolis_param, RS.u0, RS.v0)
mean_tendency = Pa.HorizontalMean(Gr,&tmp_tendency[0])
mean_tendency_2 = Pa.HorizontalMean(Gr,&tmp_tendency_2[0])
NS.write_profile('u_coriolis_tendency',mean_tendency[Gr.dims.gw:-Gr.dims.gw],Pa)
NS.write_profile('v_coriolis_tendency',mean_tendency_2[Gr.dims.gw:-Gr.dims.gw],Pa)
return
cdef class ForcingDyCOMS_RF01:
def __init__(self,casename):
self.divergence = 3.75e-6
self.coriolis_param = 2.0 * omega * sin(31.5 * pi / 180.0 )
if casename == 'DYCOMS_RF02':
self.rf02_flag = True
else:
self.rf02_flag = False
return
cpdef initialize(self, Grid.Grid Gr,ReferenceState.ReferenceState RS, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
cdef:
Py_ssize_t k
self.subsidence = np.empty((Gr.dims.nlg[2]),dtype=np.double, order='c')
self.ug = np.empty((Gr.dims.nlg[2]),dtype=np.double, order='c')
self.vg = np.empty((Gr.dims.nlg[2]),dtype=np.double, order='c')
if self.rf02_flag:
with nogil:
for k in range(Gr.dims.nlg[2]):
self.subsidence[k] = -Gr.zpl_half[k] * self.divergence
self.ug[k] = 3.0 + 4.3*Gr.zpl_half[k]/1000.0
self.vg[k] = -9.0 + 5.6 * Gr.zpl_half[k]/1000.0
else:
with nogil:
for k in range(Gr.dims.nlg[2]):
self.subsidence[k] = -Gr.zpl_half[k] * self.divergence
self.ug[k] = 7.0
self.vg[k] = -5.5
#Initialize Statistical Output
NS.add_profile('thli_subsidence_tendency', Gr, Pa)
NS.add_profile('s_subsidence_tendency', Gr, Pa)
NS.add_profile('qt_subsidence_tendency', Gr, Pa)
NS.add_profile('u_subsidence_tendency', Gr, Pa)
NS.add_profile('v_subsidence_tendency', Gr, Pa)
NS.add_profile('u_coriolis_tendency', Gr, Pa)
NS.add_profile('v_coriolis_tendency',Gr, Pa)
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState RS,
PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV, TimeStepping.TimeStepping TS,
ParallelMPI.ParallelMPI Pa):
cdef:
Py_ssize_t u_shift = PV.get_varshift(Gr, 'u')
Py_ssize_t v_shift = PV.get_varshift(Gr, 'v')
Py_ssize_t s_shift
Py_ssize_t qt_shift = PV.get_varshift(Gr,'qt')
s_shift = PV.get_varshift(Gr, 's')
apply_subsidence(&Gr.dims,&RS.rho0[0],&RS.rho0_half[0],&self.subsidence[0],&PV.values[s_shift],&PV.tendencies[s_shift])
apply_subsidence(&Gr.dims,&RS.rho0[0],&RS.rho0_half[0],&self.subsidence[0],&PV.values[qt_shift],&PV.tendencies[qt_shift])
apply_subsidence(&Gr.dims,&RS.rho0[0],&RS.rho0_half[0],&self.subsidence[0],&PV.values[u_shift],&PV.tendencies[u_shift])
apply_subsidence(&Gr.dims,&RS.rho0[0],&RS.rho0_half[0],&self.subsidence[0],&PV.values[v_shift],&PV.tendencies[v_shift])
coriolis_force(&Gr.dims,&PV.values[u_shift],&PV.values[v_shift],&PV.tendencies[u_shift],
&PV.tendencies[v_shift],&self.ug[0], &self.vg[0],self.coriolis_param, RS.u0, RS.v0 )
return
cpdef stats_io(self, Grid.Grid Gr, ReferenceState.ReferenceState RS,
PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV,
NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
cdef:
Py_ssize_t u_shift = PV.get_varshift(Gr, 'u')
Py_ssize_t v_shift = PV.get_varshift(Gr, 'v')
Py_ssize_t s_shift
Py_ssize_t thli_shift
Py_ssize_t qt_shift = PV.get_varshift(Gr, 'qt')
double [:] tmp_tendency = np.zeros((Gr.dims.npg),dtype=np.double,order='c')
double [:] tmp_tendency_2 = np.zeros((Gr.dims.npg),dtype=np.double,order='c')
double [:] mean_tendency = np.empty((Gr.dims.nlg[2],),dtype=np.double,order='c')
double [:] mean_tendency_2 = np.zeros((Gr.dims.nlg[2]),dtype=np.double,order='c')
double [:] umean = Pa.HorizontalMean(Gr, &PV.values[u_shift])
double [:] vmean = Pa.HorizontalMean(Gr, &PV.values[v_shift])
#Output subsidence tendencies
#Output entropy tendency from subsidence
s_shift = PV.get_varshift(Gr, 's')
apply_subsidence(&Gr.dims,&RS.rho0[0],&RS.rho0_half[0],&self.subsidence[0],&PV.values[s_shift],
&tmp_tendency[0])
mean_tendency = Pa.HorizontalMean(Gr,&tmp_tendency[0])
NS.write_profile('s_subsidence_tendency',mean_tendency[Gr.dims.gw:-Gr.dims.gw],Pa)
tmp_tendency[:] = 0.0
#Output thetali tendency from subsidence (diagnostic source term)
thli_shift = DV.get_varshift(Gr, 'thetali')
apply_subsidence(&Gr.dims,&RS.rho0[0],&RS.rho0_half[0],&self.subsidence[0],&DV.values[thli_shift],
&tmp_tendency[0])
mean_tendency = Pa.HorizontalMean(Gr,&tmp_tendency[0])
NS.write_profile('thli_subsidence_tendency',mean_tendency[Gr.dims.gw:-Gr.dims.gw],Pa)
tmp_tendency[:] = 0.0
apply_subsidence(&Gr.dims,&RS.rho0[0],&RS.rho0_half[0],&self.subsidence[0],&PV.values[qt_shift],
&tmp_tendency[0])
mean_tendency = Pa.HorizontalMean(Gr,&tmp_tendency[0])
NS.write_profile('qt_subsidence_tendency',mean_tendency[Gr.dims.gw:-Gr.dims.gw],Pa)
tmp_tendency[:] = 0.0
apply_subsidence(&Gr.dims,&RS.rho0[0],&RS.rho0_half[0],&self.subsidence[0],&PV.values[u_shift],
&tmp_tendency[0])
mean_tendency = Pa.HorizontalMean(Gr,&tmp_tendency[0])
NS.write_profile('u_subsidence_tendency',mean_tendency[Gr.dims.gw:-Gr.dims.gw],Pa)
tmp_tendency[:] = 0.0
apply_subsidence(&Gr.dims,&RS.rho0[0],&RS.rho0_half[0],&self.subsidence[0],&PV.values[v_shift],
&tmp_tendency[0])
mean_tendency = Pa.HorizontalMean(Gr,&tmp_tendency[0])
NS.write_profile('v_subsidence_tendency',mean_tendency[Gr.dims.gw:-Gr.dims.gw],Pa)
#Output Coriolis tendencies
tmp_tendency[:] = 0.0
large_scale_p_gradient(&Gr.dims, &umean[0], &vmean[0], &tmp_tendency[0],
&tmp_tendency_2[0], &self.ug[0], &self.vg[0], self.coriolis_param, RS.u0, RS.v0)
coriolis_force(&Gr.dims,&PV.values[u_shift],&PV.values[v_shift],&tmp_tendency[0],
&tmp_tendency_2[0],&self.ug[0], &self.vg[0],self.coriolis_param, RS.u0, RS.v0)
mean_tendency = Pa.HorizontalMean(Gr,&tmp_tendency[0])
mean_tendency_2 = Pa.HorizontalMean(Gr,&tmp_tendency_2[0])
NS.write_profile('u_coriolis_tendency',mean_tendency[Gr.dims.gw:-Gr.dims.gw],Pa)
NS.write_profile('v_coriolis_tendency',mean_tendency_2[Gr.dims.gw:-Gr.dims.gw],Pa)
return
cdef class ForcingRico:
def __init__(self):
latitude = 18.0 # degrees
self.coriolis_param = 2.0 * omega * sin(latitude * pi / 180.0 )
self.momentum_subsidence = 0
return
cpdef initialize(self, Grid.Grid Gr,ReferenceState.ReferenceState RS, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
cdef Py_ssize_t k
self.subsidence = np.empty((Gr.dims.nlg[2]),dtype=np.double, order='c')
self.ug = np.empty(Gr.dims.nlg[2],dtype=np.double,order='c')
self.vg = np.empty(Gr.dims.nlg[2],dtype=np.double,order='c')
self.dtdt = np.ones(Gr.dims.nlg[2],dtype=np.double,order='c') * -2.5/86400.0 #Here this is theta forcing
self.dqtdt = np.zeros(Gr.dims.nlg[2],dtype=np.double,order='c')
# Convert given theta forcing to temperature forcing
with nogil:
for k in range(Gr.dims.nlg[2]):
self.dtdt[k] = self.dtdt[k] * exner_c(RS.p0_half[k])
if Gr.zpl_half[k] <= 2260.0:
self.subsidence[k] = -(0.005/2260.0) * Gr.zpl_half[k]
else:
self.subsidence[k] = -0.005
if Gr.zpl_half[k]<=2980.0:
self.dqtdt[k] = (-1.0 + 1.3456/2980.0 * Gr.zpl_half[k])/86400.0/1000.0
else:
self.dqtdt[k] = 0.3456/86400.0/1000.0
self.ug[k] = -9.9 + 2.0e-3 * Gr.zpl_half[k]
self.vg[k] = -3.8
NS.add_profile('u_coriolis_tendency', Gr, Pa)
NS.add_profile('v_coriolis_tendency',Gr, Pa)
NS.add_profile('s_subsidence_tendency', Gr, Pa)
NS.add_profile('qt_subsidence_tendency', Gr, Pa)
if self.momentum_subsidence == 1:
NS.add_profile('u_subsidence_tendency', Gr, Pa)
NS.add_profile('v_subsidence_tendency', Gr, Pa)
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState RS,
PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV, TimeStepping.TimeStepping TS,
ParallelMPI.ParallelMPI Pa):
cdef:
Py_ssize_t imin = Gr.dims.gw
Py_ssize_t jmin = Gr.dims.gw
Py_ssize_t kmin = Gr.dims.gw
Py_ssize_t imax = Gr.dims.nlg[0] - Gr.dims.gw
Py_ssize_t jmax = Gr.dims.nlg[1] - Gr.dims.gw
Py_ssize_t kmax = Gr.dims.nlg[2] - Gr.dims.gw
Py_ssize_t istride = Gr.dims.nlg[1] * Gr.dims.nlg[2]
Py_ssize_t jstride = Gr.dims.nlg[2]
Py_ssize_t i, j, k, ishift, jshift, ijk
Py_ssize_t u_shift = PV.get_varshift(Gr, 'u')
Py_ssize_t v_shift = PV.get_varshift(Gr, 'v')
Py_ssize_t s_shift = PV.get_varshift(Gr, 's')
Py_ssize_t qt_shift = PV.get_varshift(Gr,'qt')
Py_ssize_t t_shift = DV.get_varshift(Gr, 'temperature')
Py_ssize_t ql_shift = DV.get_varshift(Gr,'ql')
double qt, qv, p0, t
apply_subsidence(&Gr.dims,&RS.rho0[0],&RS.rho0_half[0],&self.subsidence[0],&PV.values[s_shift],&PV.tendencies[s_shift])
apply_subsidence(&Gr.dims,&RS.rho0[0],&RS.rho0_half[0],&self.subsidence[0],&PV.values[qt_shift],&PV.tendencies[qt_shift])
if self.momentum_subsidence == 1:
apply_subsidence(&Gr.dims,&RS.rho0[0],&RS.rho0_half[0],&self.subsidence[0],&PV.values[u_shift],&PV.tendencies[u_shift])
apply_subsidence(&Gr.dims,&RS.rho0[0],&RS.rho0_half[0],&self.subsidence[0],&PV.values[v_shift],&PV.tendencies[v_shift])
#Apply large scale source terms
with nogil:
for i in xrange(imin,imax):
ishift = i * istride
for j in xrange(jmin,jmax):
jshift = j * jstride
for k in xrange(kmin,kmax):
ijk = ishift + jshift + k
p0 = RS.p0_half[k]
qt = PV.values[qt_shift + ijk]
qv = qt - DV.values[ql_shift + ijk]
t = DV.values[t_shift + ijk]
PV.tendencies[s_shift + ijk] += s_tendency_c(p0, qt, qv, t, self.dqtdt[k], self.dtdt[k])
PV.tendencies[qt_shift + ijk] += self.dqtdt[k]
coriolis_force(&Gr.dims,&PV.values[u_shift],&PV.values[v_shift],&PV.tendencies[u_shift],
&PV.tendencies[v_shift],&self.ug[0], &self.vg[0],self.coriolis_param, RS.u0, RS.v0 )
return
cpdef stats_io(self, Grid.Grid Gr, ReferenceState.ReferenceState RS,
PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV,
NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
return
cdef class ForcingCGILS:
def __init__(self, namelist, ParallelMPI.ParallelMPI Pa):
try:
self.loc = namelist['meta']['CGILS']['location']
except:
Pa.root_print('Must provide a CGILS location (6/11/12) in namelist')
Pa.kill()
try:
self.is_p2 = namelist['meta']['CGILS']['P2']
except:
Pa.root_print('Must specify if CGILS run is perturbed')
Pa.kill()
try:
self.is_ctl_omega = namelist['meta']['CGILS']['CTL_omega']
except:
self.is_ctl_omega = False
if self.is_p2:
Pa.root_print('ForcingCGILS: Assuming perturbed omega is used')
if self.loc == 12:
self.z_relax = 1200.0
self.z_relax_plus = 1500.0
elif self.loc == 11:
self.z_relax = 2500.0
self.z_relax_plus = 3000.0
elif self.loc == 6:
self.z_relax = 4000.0
self.z_relax_plus = 4800.0
self.tau_inverse = 1.0/(60.0*60.0) # inverse of max nudging timescale, 1 hr, for all cases
self.tau_vel_inverse = 1.0/(10.0*60.0) # nudging timescale of horizontal winds
return
cpdef initialize(self, Grid.Grid Gr, ReferenceState.ReferenceState RS, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
self.dtdt = np.zeros(Gr.dims.nlg[2],dtype=np.double,order='c')
self.dqtdt = np.zeros(Gr.dims.nlg[2],dtype=np.double,order='c')
self.subsidence = np.zeros(Gr.dims.nlg[2],dtype=np.double,order='c')
if self.is_p2:
file = './CGILSdata/p2k_s'+str(self.loc)+'.nc'
else:
file = './CGILSdata/ctl_s'+str(self.loc)+'.nc'
data = nc.Dataset(file, 'r')
pressure = data.variables['lev'][:]
divq_data = data.variables['divq'][0,:,0,0]
divT_data = data.variables['divT'][0,:,0,0]
omega_data = data.variables['omega'][0,:,0,0]
temperature_data = data.variables['T'][0,:,0,0]
q_data = data.variables['q'][0,:,0,0]
u_data = data.variables['u'][0,:,0,0]
v_data = data.variables['v'][0,:,0,0]
Ps = data.variables['Ps'][0,0,0]
n_data = np.shape(pressure)[0] - 1
data.close()
if self.is_p2 and self.is_ctl_omega:
file = './CGILSdata/ctl_s'+str(self.loc)+'.nc'
data = nc.Dataset(file, 'r')
omega_data = data.variables['omega'][0,:,0,0]
data.close()
# Now we need to use interpolation to get the forcing at the LES grid.
# We need to extrapolate the data arrays to the surface
omega_right = (omega_data[n_data-1] - omega_data[n_data])/(pressure[n_data-1]-pressure[n_data])*(Ps-pressure[n_data]) + omega_data[n_data]
omega_data = np.append(omega_data,omega_right)
divq_right = (divq_data[n_data-1] - divq_data[n_data])/(pressure[n_data-1]-pressure[n_data])*(Ps-pressure[n_data]) + divq_data[n_data]
divq_data = np.append(divq_data,divq_right)
divT_right = (divT_data[n_data-1] - divT_data[n_data])/(pressure[n_data-1]-pressure[n_data])*(Ps-pressure[n_data]) + divT_data[n_data]
divT_data = np.append(divT_data,divT_right)
temperature_right = (temperature_data[n_data-1] - temperature_data[n_data])/(pressure[n_data-1]-pressure[n_data])*(Ps-pressure[n_data]) + temperature_data[n_data]
temperature_data = np.append(temperature_data, temperature_right)
q_right = (q_data[n_data-1] - q_data[n_data])/(pressure[n_data-1]-pressure[n_data])*(Ps-pressure[n_data]) + q_data[n_data]
q_data = np.append(q_data, q_right)
u_right = (u_data[n_data-1] - u_data[n_data])/(pressure[n_data-1]-pressure[n_data])*(Ps-pressure[n_data]) + u_data[n_data]
u_data = np.append(u_data,u_right)
v_right = (v_data[n_data-1] - v_data[n_data])/(pressure[n_data-1]-pressure[n_data])*(Ps-pressure[n_data]) + v_data[n_data]
v_data = np.append(v_data, v_right)
pressure = np.append(pressure, Ps)
# interpolate the subsidence profile from the data (below we convert from dp--> dz)
self.subsidence = np.array(np.interp(RS.p0_half, pressure, omega_data),dtype=np.double, order='c')
# interpolate large scale advection forcings
self.dqtdt = np.array(np.interp(RS.p0_half, pressure, divq_data),dtype=np.double, order='c')
self.dtdt = np.array(np.interp(RS.p0_half, pressure, divT_data),dtype=np.double, order='c')
# interpolate the reference profiles
self.nudge_qt = np.array(np.interp(RS.p0_half, pressure, q_data),dtype=np.double, order='c')
self.nudge_temperature = np.array(np.interp(RS.p0_half, pressure, temperature_data),dtype=np.double, order='c')
self.nudge_u = np.array(np.interp(RS.p0_half, pressure, u_data),dtype=np.double, order='c')
self.nudge_v = np.array(np.interp(RS.p0_half, pressure, v_data),dtype=np.double, order='c')
# Initialize arrays for the nudging related source terms
self.source_qt_floor = np.zeros(Gr.dims.nlg[2],dtype=np.double,order='c')
self.source_qt_nudge = np.zeros(Gr.dims.nlg[2],dtype=np.double,order='c')
self.source_t_nudge = np.zeros(Gr.dims.nlg[2],dtype=np.double,order='c')
self.source_u_nudge = np.zeros(Gr.dims.nlg[2],dtype=np.double,order='c')
self.source_v_nudge = np.zeros(Gr.dims.nlg[2],dtype=np.double,order='c')
self.source_s_nudge = np.zeros(Gr.dims.npg,dtype=np.double,order='c')
self.s_ls_adv = np.zeros(Gr.dims.npg,dtype=np.double,order='c')
# convert subsidence velocity to physical space
cdef Py_ssize_t k
with nogil:
for k in xrange(Gr.dims.nlg[2]):
self.subsidence[k] = -self.subsidence[k]*RS.alpha0_half[k]/g
# Obtain the moisture floor and find the max index corresponding to z <= 1300 m
self.qt_floor = np.interp(1300.0, Gr.zpl_half, self.nudge_qt)
for k in range(Gr.dims.gw, Gr.dims.nlg[2]-Gr.dims.gw):
if Gr.zpl_half[k] > 1300.0:
break
self.floor_index = k
# initialize the inverse timescale coefficient arrays for nudging
self.gamma_zhalf = np.zeros((Gr.dims.nlg[2]),dtype=np.double,order='c')
self.gamma_z = np.zeros((Gr.dims.nlg[2]), dtype=np.double, order='c')
with nogil:
for k in range(Gr.dims.nlg[2]):
if Gr.zpl_half[k] < self.z_relax:
self.gamma_zhalf[k] = 0.0
elif Gr.zpl_half[k] > self.z_relax_plus:
self.gamma_zhalf[k] = self.tau_inverse
else:
self.gamma_zhalf[k] = 0.5*self.tau_inverse * (1.0 - cos(pi* (Gr.zpl_half[k]-self.z_relax)/(self.z_relax_plus-self.z_relax)))
if Gr.zpl[k] < self.z_relax:
self.gamma_z[k] = 0.0
elif Gr.zpl[k] > self.z_relax_plus:
self.gamma_z[k] = self.tau_inverse
else:
self.gamma_z[k] = 0.5*self.tau_inverse * (1.0 - cos(pi* (Gr.zpl[k]-self.z_relax)/(self.z_relax_plus-self.z_relax)))
#Initialize Statistical Output
NS.add_profile('s_subsidence_tendency', Gr, Pa)
NS.add_profile('qt_subsidence_tendency', Gr, Pa)
NS.add_profile('qt_floor_nudging', Gr, Pa)
NS.add_profile('qt_ref_nudging', Gr, Pa)
NS.add_profile('temperature_ref_nudging', Gr, Pa)
NS.add_profile('s_ref_nudging', Gr, Pa)
NS.add_profile('s_ls_adv', Gr, Pa)
# Add profiles of fixed forcing profiles that do not depend on time
NS.add_reference_profile('subsidence_velocity', Gr, Pa)
NS.write_reference_profile('subsidence_velocity', self.subsidence[Gr.dims.gw:-Gr.dims.gw], Pa)
NS.add_reference_profile('temperature_ls_adv', Gr, Pa)
NS.write_reference_profile('temperature_ls_adv', self.dtdt[Gr.dims.gw:-Gr.dims.gw], Pa)
NS.add_reference_profile('qt_ls_adv', Gr, Pa)
NS.write_reference_profile('qt_ls_adv', self.dqtdt[Gr.dims.gw:-Gr.dims.gw], Pa)
NS.add_reference_profile('u_ref_profile', Gr, Pa)
NS.write_reference_profile('u_ref_profile', self.nudge_u[Gr.dims.gw:-Gr.dims.gw], Pa)
NS.add_reference_profile('v_ref_profile', Gr, Pa)
NS.write_reference_profile('v_ref_profile', self.nudge_v[Gr.dims.gw:-Gr.dims.gw], Pa)
NS.add_reference_profile('temperature_ref_profile', Gr, Pa)
NS.write_reference_profile('temperature_ref_profile', self.nudge_temperature[Gr.dims.gw:-Gr.dims.gw], Pa)
NS.add_reference_profile('qt_ref_profile', Gr, Pa)
NS.write_reference_profile('qt_ref_profile', self.nudge_qt[Gr.dims.gw:-Gr.dims.gw], Pa)
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState RS,
PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV, TimeStepping.TimeStepping TS,
ParallelMPI.ParallelMPI Pa):
cdef:
Py_ssize_t imin = Gr.dims.gw
Py_ssize_t jmin = Gr.dims.gw
Py_ssize_t kmin = Gr.dims.gw
Py_ssize_t imax = Gr.dims.nlg[0] - Gr.dims.gw
Py_ssize_t jmax = Gr.dims.nlg[1] - Gr.dims.gw
Py_ssize_t kmax = Gr.dims.nlg[2] - Gr.dims.gw
Py_ssize_t istride = Gr.dims.nlg[1] * Gr.dims.nlg[2]
Py_ssize_t jstride = Gr.dims.nlg[2]
Py_ssize_t i,j,k,ishift,jshift,ijk
Py_ssize_t u_shift = PV.get_varshift(Gr, 'u')
Py_ssize_t v_shift = PV.get_varshift(Gr, 'v')
Py_ssize_t s_shift = PV.get_varshift(Gr, 's')
Py_ssize_t qt_shift = PV.get_varshift(Gr, 'qt')
Py_ssize_t t_shift = DV.get_varshift(Gr, 'temperature')
Py_ssize_t ql_shift = DV.get_varshift(Gr,'ql')
double qt, qv, p0, t, qt_floor_nudge
double [:] qtmean = Pa.HorizontalMean(Gr, &PV.values[qt_shift])
double [:] tmean = Pa.HorizontalMean(Gr, &DV.values[t_shift])
double [:] umean = Pa.HorizontalMean(Gr, &PV.values[u_shift])
double [:] vmean = Pa.HorizontalMean(Gr, &PV.values[v_shift])
# Apply subsidence
apply_subsidence(&Gr.dims, &RS.rho0[0], &RS.rho0_half[0], &self.subsidence[0], &PV.values[s_shift], &PV.tendencies[s_shift])
apply_subsidence(&Gr.dims, &RS.rho0[0], &RS.rho0_half[0], &self.subsidence[0], &PV.values[qt_shift], &PV.tendencies[qt_shift])
# Calculate nudging
with nogil:
for k in xrange(kmin, kmax):
# Nudge s, qt to reference profiles in free troposphere
self.source_t_nudge[k] = -(tmean[k] - self.nudge_temperature[k]) * self.gamma_z[k]
self.source_qt_nudge[k]= -(qtmean[k] - self.nudge_qt[k]) * self.gamma_z[k]
# Nudge mean wind profiles through entire depth
self.source_u_nudge[k] = -(umean[k] + RS.u0 - self.nudge_u[k]) * self.tau_vel_inverse
self.source_v_nudge[k] = -(vmean[k] + RS.v0 - self.nudge_v[k]) * self.tau_vel_inverse
# Moisture floor nudging for S12 case
if self.loc == 12:
with nogil:
for k in xrange(kmin, self.floor_index):
if qtmean[k] < self.qt_floor:
self.source_qt_floor[k] = -(qtmean[k] - self.qt_floor) * self.tau_inverse
else:
self.source_qt_floor[k] = 0.0
cdef double total_qt_source
cdef double total_t_source
#Apply large scale source terms
with nogil:
for i in xrange(imin,imax):
ishift = i * istride
for j in xrange(jmin,jmax):
jshift = j * jstride
for k in xrange(kmin,kmax):
ijk = ishift + jshift + k
p0 = RS.p0_half[k]
qt = PV.values[qt_shift + ijk]
qv = qt - DV.values[ql_shift + ijk]
t = DV.values[t_shift + ijk]
total_qt_source = self.dqtdt[k] + self.source_qt_floor[k] + self.source_qt_nudge[k]
total_t_source = self.dtdt[k] + self.source_t_nudge[k]
PV.tendencies[s_shift + ijk] += s_tendency_c(p0, qt, qv, t, total_qt_source, total_t_source)
PV.tendencies[qt_shift + ijk] += total_qt_source
PV.tendencies[u_shift + ijk] += self.source_u_nudge[k]
PV.tendencies[v_shift + ijk] += self.source_v_nudge[k]
self.source_s_nudge[ijk] = s_tendency_c(p0, qt, qv, t, self.source_qt_nudge[k] + self.source_qt_floor[k],
self.source_t_nudge[k] )
self.s_ls_adv[ijk]= s_tendency_c(p0, qt, qv, t, self.dqtdt[k], self.dtdt[k])
return
cpdef stats_io(self, Grid.Grid Gr, ReferenceState.ReferenceState RS,
PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV,
NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
cdef:
Py_ssize_t u_shift = PV.get_varshift(Gr, 'u')
Py_ssize_t v_shift = PV.get_varshift(Gr, 'v')
Py_ssize_t s_shift = PV.get_varshift(Gr, 's')
Py_ssize_t qt_shift = PV.get_varshift(Gr, 'qt')
double [:] tmp_tendency = np.zeros((Gr.dims.npg),dtype=np.double,order='c')
double [:] tmp_tendency_2 = np.zeros((Gr.dims.npg),dtype=np.double,order='c')
double [:] mean_tendency = np.empty((Gr.dims.nlg[2],),dtype=np.double,order='c')
double [:] mean_tendency_2 = np.zeros((Gr.dims.nlg[2]),dtype=np.double,order='c')
double [:] umean = Pa.HorizontalMean(Gr, &PV.values[u_shift])
double [:] vmean = Pa.HorizontalMean(Gr, &PV.values[v_shift])
#Output subsidence tendencies
apply_subsidence(&Gr.dims,&RS.rho0[0],&RS.rho0_half[0],&self.subsidence[0],&PV.values[s_shift],
&tmp_tendency[0])
mean_tendency = Pa.HorizontalMean(Gr,&tmp_tendency[0])
NS.write_profile('s_subsidence_tendency',mean_tendency[Gr.dims.gw:-Gr.dims.gw],Pa)
tmp_tendency[:] = 0.0
apply_subsidence(&Gr.dims,&RS.rho0[0],&RS.rho0_half[0],&self.subsidence[0],&PV.values[qt_shift],
&tmp_tendency[0])
mean_tendency = Pa.HorizontalMean(Gr,&tmp_tendency[0])
NS.write_profile('qt_subsidence_tendency',mean_tendency[Gr.dims.gw:-Gr.dims.gw],Pa)
mean_tendency = Pa.HorizontalMean(Gr,&self.source_s_nudge[0])
NS.write_profile('s_ref_nudging',mean_tendency[Gr.dims.gw:-Gr.dims.gw],Pa)
mean_tendency = Pa.HorizontalMean(Gr,&self.s_ls_adv[0])
NS.write_profile('s_ls_adv',mean_tendency[Gr.dims.gw:-Gr.dims.gw],Pa)
NS.write_profile('qt_floor_nudging',self.source_qt_floor[Gr.dims.gw:-Gr.dims.gw],Pa)
NS.write_profile('qt_ref_nudging',self.source_qt_nudge[Gr.dims.gw:-Gr.dims.gw],Pa)
NS.write_profile('temperature_ref_nudging',self.source_t_nudge[Gr.dims.gw:-Gr.dims.gw],Pa)
return
cdef class ForcingZGILS:
def __init__(self, namelist, LatentHeat LH, ParallelMPI.ParallelMPI Pa):
try:
self.loc = namelist['meta']['ZGILS']['location']
except:
Pa.root_print('FORCING: Must provide a ZGILS location (6/11/12) in namelist')
Pa.kill()
if self.loc == 12:
self.divergence = 6.0e-6
self.coriolis_param = 2.0 * omega * sin(34.5/180.0*pi)
elif self.loc == 11:
self.divergence = 3.5e-6
self.coriolis_param = 2.0 * omega * sin(31.5/180.0*pi)
elif self.loc == 6:
self.divergence = 2.0e-6
self.coriolis_param = 2.0 * omega * sin(16.5/180.0*pi)
else:
Pa.root_print('FORCING: Unrecognized ZGILS location ' + str(self.loc))
Pa.kill()
self.t_adv_max = -1.2/86400.0 # K/s BL tendency of temperature due to horizontal advection
self.qt_adv_max = -0.6e-3/86400.0 # kg/kg/s BL tendency of qt due to horizontal advection
self.tau_relax_inverse = 1.0/86400.0 # relaxation time scale = 24 h
self.alpha_h = 1.2 # threshold ratio for determining BL height
# Initialize the reference profiles classe
self.forcing_ref = AdjustedMoistAdiabat(namelist, LH, Pa)
self.CC = ClausiusClapeyron()
self.CC.initialize(namelist, LH, Pa)
return
cpdef initialize(self, Grid.Grid Gr,ReferenceState.ReferenceState RS, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
self.ug = np.zeros(Gr.dims.nlg[2],dtype=np.double,order='c')
self.vg = np.zeros(Gr.dims.nlg[2],dtype=np.double,order='c')
self.dtdt = np.zeros(Gr.dims.nlg[2],dtype=np.double,order='c')
self.dqtdt = np.zeros(Gr.dims.nlg[2],dtype=np.double,order='c')
self.subsidence = np.zeros(Gr.dims.nlg[2],dtype=np.double,order='c')
self.source_rh_nudge = np.zeros(Gr.dims.nlg[2],dtype=np.double,order='c')
self.source_qt_nudge = np.zeros(Gr.dims.nlg[2],dtype=np.double,order='c')
self.source_t_nudge = np.zeros(Gr.dims.nlg[2],dtype=np.double,order='c')
self.source_s_nudge = np.zeros(Gr.dims.npg,dtype=np.double,order='c')
self.s_ls_adv = np.zeros(Gr.dims.npg,dtype=np.double,order='c')
# compute the reference profiles for forcing/nudging
cdef double Pg_parcel = 1000.0e2
cdef double Tg_parcel = 295.0
cdef double RH_ref = 0.3
self.forcing_ref.initialize(Pa, RS.p0_half[:], Gr.dims.nlg[2],
Pg_parcel, Tg_parcel, RH_ref)
cdef:
Py_ssize_t k
double sub_factor = self.divergence/(RS.Pg*RS.Pg)/g
# initialize the profiles of geostrophic velocity, subsidence, and large scale advection
with nogil:
for k in xrange(Gr.dims.nlg[2]):
self.ug[k] = min(-10.0 + (-7.0-(-10.0))/(750.0e2-1000.0e2)*(RS.p0_half[k]-1000.0e2),-4.0)
self.subsidence[k]= sub_factor * (RS.p0_half[k] - RS.Pg) * RS.p0_half[k] * RS.p0_half[k] * RS.alpha0_half[k]
#Set large scale cooling
if RS.p0_half[k] > 900.0e2:
self.dtdt[k] = self.t_adv_max
self.dqtdt[k] = self.qt_adv_max
elif RS.p0_half[k]< 800.0e2:
self.dtdt[k] = 0.0