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Radiation.pyx
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Radiation.pyx
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#!python
#cython: boundscheck=False
#cython: wraparound=False
#cython: initializedcheck=False
#cython: cdivision=True
cimport Grid
cimport ReferenceState
cimport PrognosticVariables
cimport DiagnosticVariables
from NetCDFIO cimport NetCDFIO_Stats
cimport ParallelMPI
cimport TimeStepping
cimport Surface
from Forcing cimport AdjustedMoistAdiabat
from Thermodynamics cimport LatentHeat
import numpy as np
cimport numpy as np
import netCDF4 as nc
from scipy.interpolate import pchip_interpolate
from libc.math cimport pow, cbrt, exp, fmin, fmax, fabs, sin
from thermodynamic_functions cimport cpm_c, exner_c
include 'parameters.pxi'
from profiles import profile_data
import math
from scipy.interpolate import pchip
import pickle as pickle
import cython
from cfsites_forcing_reader import cfreader
from cfgrid_forcing_reader import cfreader_grid
def RadiationFactory(namelist, LatentHeat LH, ParallelMPI.ParallelMPI Pa):
# if namelist specifies RRTM is to be used, this will override any case-specific radiation schemes
try:
use_rrtm = namelist['radiation']['use_RRTM']
except:
use_rrtm = False
try:
prescribe_radiation = namelist['radiation']['prescribe_radiation']
except:
prescribe_radiation = False
if use_rrtm:
return RadiationRRTM(namelist, LH, Pa)
elif prescribe_radiation:
return RadiationPrescribed(namelist, LH, Pa)
else:
casename = namelist['meta']['casename']
if casename == 'DYCOMS_RF01':
return RadiationDyCOMS_RF01(namelist)
elif casename == 'DYCOMS_RF02':
#Dycoms RF01 and RF02 use the same radiation
return RadiationDyCOMS_RF01(namelist)
elif casename == 'SMOKE':
return RadiationSmoke()
elif casename == 'CGILS':
return RadiationRRTM(namelist,LH, Pa)
elif casename == 'ZGILS':
return RadiationRRTM(namelist, LH, Pa)
elif casename == 'GCMNew':
return RadiationNone()
elif casename == 'GCMVarying':
return RadiationNone()
elif casename == 'TRMM_LBA':
return RadiationTRMM_LBA(namelist, LH, Pa)
else:
return RadiationNone()
cdef class RadiationBase:
def __init__(self):
return
cpdef initialize(self, Grid.Grid Gr, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
self.z_pencil = ParallelMPI.Pencil()
self.z_pencil.initialize(Gr, Pa, 2)
self.heating_rate = np.zeros((Gr.dims.npg,), dtype=np.double, order='c')
self.dTdt_rad = np.zeros((Gr.dims.npg,), dtype=np.double, order='c')
NS.add_profile('radiative_heating_rate', Gr, Pa)
NS.add_profile('radiative_entropy_tendency', Gr, Pa)
NS.add_profile('radiative_temperature_tendency',Gr, Pa)
NS.add_profile('dtdt_rad',Gr, Pa)
NS.add_ts('srf_lw_flux_up', Gr, Pa)
NS.add_ts('srf_lw_flux_down', Gr, Pa)
NS.add_ts('srf_sw_flux_up', Gr, Pa)
NS.add_ts('srf_sw_flux_down', Gr, Pa)
return
cpdef initialize_profiles(self, Grid.Grid Gr, ReferenceState.ReferenceState RS, DiagnosticVariables.DiagnosticVariables DV,
NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState RS,
PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV,
Surface.SurfaceBase Sur, TimeStepping.TimeStepping TS, ParallelMPI.ParallelMPI Pa):
return
cpdef stats_io(self, Grid.Grid Gr, ReferenceState.ReferenceState RS, DiagnosticVariables.DiagnosticVariables DV,
NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
cdef:
Py_ssize_t i,j,k
Py_ssize_t gw = 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 ishift, jshift, ijk
Py_ssize_t t_shift = DV.get_varshift(Gr, 'temperature')
double [:] entropy_tendency = np.zeros((Gr.dims.npg,), dtype=np.double, order='c')
double [:] tmp
# Now update entropy tendencies
with nogil:
for i in xrange(gw,imax):
ishift = i * istride
for j in xrange(gw,jmax):
jshift = j * jstride
for k in xrange(gw,kmax):
ijk = ishift + jshift + k
entropy_tendency[ijk] = self.heating_rate[ijk] * RS.alpha0_half[k] / DV.values[ijk + t_shift]
tmp = Pa.HorizontalMean(Gr, &self.heating_rate[0])
NS.write_profile('radiative_heating_rate', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMean(Gr, &entropy_tendency[0])
NS.write_profile('radiative_entropy_tendency', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMean(Gr, &self.dTdt_rad[0])
NS.write_profile('radiative_temperature_tendency', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
NS.write_profile('dtdt_rad', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
NS.write_ts('srf_lw_flux_up',self.srf_lw_up, Pa ) # Units are W/m^2
NS.write_ts('srf_lw_flux_down', self.srf_lw_down, Pa)
NS.write_ts('srf_sw_flux_up', self.srf_sw_up, Pa)
NS.write_ts('srf_sw_flux_down', self.srf_sw_down, Pa)
return
cdef class RadiationNone(RadiationBase):
def __init__(self):
return
cpdef initialize(self, Grid.Grid Gr, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
return
cpdef initialize_profiles(self, Grid.Grid Gr, ReferenceState.ReferenceState RS, DiagnosticVariables.DiagnosticVariables DV,
NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState RS,
PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV,
Surface.SurfaceBase Sur,TimeStepping.TimeStepping TS, ParallelMPI.ParallelMPI Pa):
return
cpdef stats_io(self, Grid.Grid Gr, ReferenceState.ReferenceState RS, DiagnosticVariables.DiagnosticVariables DV,
NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
return
cdef class RadiationPrescribed(RadiationBase):
def __init__(self, namelist, LatentHeat LH, ParallelMPI.ParallelMPI Pa):
self.file = str(namelist['gcm']['file'])
self.site = namelist['gcm']['site']
return
cpdef initialize(self, Grid.Grid Gr, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
self.t_tend_rad = np.zeros(Gr.dims.nlg[2],dtype=np.double,order='c')
self.s_tend_rad = np.zeros(Gr.dims.npg,dtype=np.double,order='c')
NS.add_profile('dtdt_rad', Gr, Pa)
NS.add_profile('dsdt_rad', Gr, Pa)
return
cpdef initialize_profiles(self, Grid.Grid Gr, ReferenceState.ReferenceState RS, DiagnosticVariables.DiagnosticVariables DV,
NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState RS,
PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV,
Surface.SurfaceBase Sur,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 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')
if not self.gcm_profiles_initialized:
self.gcm_profiles_initialized = True
Pa.root_print('Updating prescribed radiation')
rdr = cfreader(self.file, self.site)
self.t_tend_rad = rdr.get_interp_profile_old('tntr', Gr.zp_half)
# Now update entropy tendencies
s_shift = PV.get_varshift(Gr, 's')
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
self.s_tend_rad[ijk] = cpm_c(PV.values[ijk+qt_shift]) * self.t_tend_rad[k] / DV.values[ijk+t_shift]
PV.tendencies[s_shift + ijk] += self.s_tend_rad[ijk]
return
cpdef stats_io(self, Grid.Grid Gr, ReferenceState.ReferenceState RS, DiagnosticVariables.DiagnosticVariables DV,
NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
cdef:
Py_ssize_t gw = Gr.dims.gw
double [:] mean_tendency = np.empty((Gr.dims.nlg[2],),dtype=np.double,order='c')
NS.write_profile('dtdt_rad', self.t_tend_rad[Gr.dims.gw:-Gr.dims.gw], Pa)
mean_tendency = Pa.HorizontalMean(Gr,&self.s_tend_rad[0])
NS.write_profile('dsdt_rad',mean_tendency[Gr.dims.gw:-Gr.dims.gw],Pa)
return
cdef class RadiationDyCOMS_RF01(RadiationBase):
def __init__(self, namelist):
self.alpha_z = 1.0
self.kap = 85.0
try:
self.f0 = namelist['radiation']['dycoms_f0']
except:
self.f0 = 70.0
self.f1 = 22.0
self.divergence = 3.75e-6
return
cpdef initialize(self, Grid.Grid Gr, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
RadiationBase.initialize(self, Gr, NS, Pa)
return
cpdef initialize_profiles(self, Grid.Grid Gr, ReferenceState.ReferenceState RS, DiagnosticVariables.DiagnosticVariables DV,
NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState RS,
PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV,
Surface.SurfaceBase Sur,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 pi, i, j, k, 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 ql_shift = DV.get_varshift(Gr, 'ql')
Py_ssize_t qt_shift = PV.get_varshift(Gr, 'qt')
Py_ssize_t s_shift
Py_ssize_t thli_shift
Py_ssize_t t_shift = DV.get_varshift(Gr, 'temperature')
Py_ssize_t gw = Gr.dims.gw
double [:, :] ql_pencils = self.z_pencil.forward_double(&Gr.dims, Pa, &DV.values[ql_shift])
double [:, :] qt_pencils = self.z_pencil.forward_double(&Gr.dims, Pa, &PV.values[qt_shift])
double[:, :] f_rad = np.empty((self.z_pencil.n_local_pencils, Gr.dims.n[2] + 1), dtype=np.double, order='c')
double[:, :] f_heat = np.empty((self.z_pencil.n_local_pencils, Gr.dims.n[2]), dtype=np.double, order='c')
double q_0
double q_1
double zi
double rhoi
double dz = Gr.dims.dx[2]
double dzi = Gr.dims.dxi[2]
double[:] z = Gr.zp
double[:] rho = RS.rho0
double[:] rho_half = RS.rho0_half
double cbrt_z = 0
with nogil:
for pi in xrange(self.z_pencil.n_local_pencils):
# Compute zi (level of 8.0 g/kg isoline of qt)
for k in xrange(Gr.dims.n[2]):
if qt_pencils[pi, k] > 8e-3:
zi = z[gw + k]
rhoi = rho_half[gw + k]
# Now compute the third term on RHS of Stevens et al 2005
# (equation 3)
f_rad[pi, 0] = 0.0
for k in xrange(Gr.dims.n[2]):
if z[gw + k] >= zi:
cbrt_z = cbrt(z[gw + k] - zi)
f_rad[pi, k + 1] = rhoi * cpd * self.divergence * self.alpha_z * (pow(cbrt_z,4) / 4.0
+ zi * cbrt_z)
else:
f_rad[pi, k + 1] = 0.0
# Compute the second term on RHS of Stevens et al. 2005
# (equation 3)
q_1 = 0.0
f_rad[pi, 0] += self.f1 * exp(-q_1)
for k in xrange(1, Gr.dims.n[2] + 1):
q_1 += self.kap * \
rho_half[gw + k - 1] * ql_pencils[pi, k - 1] * Gr.dims.dzpl_half[gw+k-1]
f_rad[pi, k] += self.f1 * exp(-q_1)
# Compute the first term on RHS of Stevens et al. 2005
# (equation 3)
q_0 = 0.0
f_rad[pi, Gr.dims.n[2]] += self.f0 * exp(-q_0)
for k in xrange(Gr.dims.n[2] - 1, -1, -1):
q_0 += self.kap * rho_half[gw + k] * ql_pencils[pi, k] * Gr.dims.dzpl_half[gw+k]
f_rad[pi, k] += self.f0 * exp(-q_0)
for k in xrange(Gr.dims.n[2]):
f_heat[pi, k] = - \
(f_rad[pi, k + 1] - f_rad[pi, k]) * dzi * Gr.dims.imet_half[k] / rho_half[k]
# Now transpose the flux pencils
self.z_pencil.reverse_double(&Gr.dims, Pa, f_heat, &self.heating_rate[0])
# Now update entropy tendencies
s_shift = PV.get_varshift(Gr, 's')
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
PV.tendencies[
s_shift + ijk] += self.heating_rate[ijk] / DV.values[ijk + t_shift]
self.dTdt_rad[ijk] = self.heating_rate[ijk] / cpm_c(PV.values[ijk + qt_shift])
return
cpdef stats_io(self, Grid.Grid Gr, ReferenceState.ReferenceState RS, DiagnosticVariables.DiagnosticVariables DV,
NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
RadiationBase.stats_io(self, Gr, RS, DV, NS, Pa)
return
cdef class RadiationSmoke(RadiationBase):
'''
Radiation 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.
'''
def __init__(self):
self.f0 = 60.0
self.kap = 0.02
return
cpdef initialize(self, Grid.Grid Gr, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
RadiationBase.initialize(self, Gr, NS, Pa)
return
cpdef initialize_profiles(self, Grid.Grid Gr, ReferenceState.ReferenceState RS, DiagnosticVariables.DiagnosticVariables DV,
NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState RS,
PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV,
Surface.SurfaceBase Sur, 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 pi, i, j, k, 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 s_shift = PV.get_varshift(Gr, 's')
Py_ssize_t t_shift = DV.get_varshift(Gr, 'temperature')
Py_ssize_t smoke_shift = PV.get_varshift(Gr, 'smoke')
Py_ssize_t gw = Gr.dims.gw
double [:, :] smoke_pencils = self.z_pencil.forward_double(&Gr.dims, Pa, &PV.values[smoke_shift])
double[:, :] f_rad = np.zeros((self.z_pencil.n_local_pencils, Gr.dims.n[2] + 1), dtype=np.double, order='c')
double[:, :] f_heat = np.zeros((self.z_pencil.n_local_pencils, Gr.dims.n[2]), dtype=np.double, order='c')
double q_0
double zi
double rhoi
double dz = Gr.dims.dx[2]
double dzi = Gr.dims.dxi[2]
double[:] z = Gr.zp
double[:] rho = RS.rho0
double[:] rho_half = RS.rho0_half
double cbrt_z = 0
Py_ssize_t kk
with nogil:
for pi in xrange(self.z_pencil.n_local_pencils):
q_0 = 0.0
f_rad[pi, Gr.dims.n[2]] = self.f0 * exp(-q_0)
for k in xrange(Gr.dims.n[2] - 1, -1, -1):
q_0 += self.kap * rho_half[gw + k] * smoke_pencils[pi, k] * Gr.dims.dzpl_half[gw+k]
f_rad[pi, k] = self.f0 * exp(-q_0)
for k in xrange(Gr.dims.n[2]):
f_heat[pi, k] = - \
(f_rad[pi, k + 1] - f_rad[pi, k]) * dzi * Gr.dims.imet_half[k] / rho_half[k]
# Now transpose the flux pencils
self.z_pencil.reverse_double(&Gr.dims, Pa, f_heat, &self.heating_rate[0])
# Now update entropy tendencies
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
PV.tendencies[
s_shift + ijk] += self.heating_rate[ijk] / DV.values[ijk + t_shift] * RS.alpha0_half[k]
self.dTdt_rad[ijk] = self.heating_rate[ijk] / cpd * RS.alpha0_half[k]
return
cpdef stats_io(self, Grid.Grid Gr, ReferenceState.ReferenceState RS, DiagnosticVariables.DiagnosticVariables DV,
NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
RadiationBase.stats_io(self, Gr, RS, DV, NS, Pa)
return
# Note: the RRTM modules are compiled in the 'RRTMG' directory:
cdef extern:
void c_rrtmg_lw_init(double *cpdair)
void c_rrtmg_lw (
int *ncol ,int *nlay ,int *icld ,int *idrv ,
double *play ,double *plev ,double *tlay ,double *tlev ,double *tsfc ,
double *h2ovmr ,double *o3vmr ,double *co2vmr ,double *ch4vmr ,double *n2ovmr ,double *o2vmr,
double *cfc11vmr,double *cfc12vmr,double *cfc22vmr,double *ccl4vmr ,double *emis ,
int *inflglw ,int *iceflglw,int *liqflglw,double *cldfr ,
double *taucld ,double *cicewp ,double *cliqwp ,double *reice ,double *reliq ,
double *tauaer ,
double *uflx ,double *dflx ,double *hr ,double *uflxc ,double *dflxc, double *hrc,
double *duflx_dt,double *duflxc_dt )
void c_rrtmg_sw_init(double *cpdair)
void c_rrtmg_sw (int *ncol ,int *nlay ,int *icld ,int *iaer ,
double *play ,double *plev ,double *tlay ,double *tlev ,double *tsfc ,
double *h2ovmr ,double *o3vmr ,double *co2vmr ,double *ch4vmr ,double *n2ovmr ,double *o2vmr,
double *asdir ,double *asdif ,double *aldir ,double *aldif ,
double *coszen ,double *adjes ,int *dyofyr ,double *scon ,
int *inflgsw ,int *iceflgsw,int *liqflgsw,double *cldfr ,
double *taucld ,double *ssacld ,double *asmcld ,double *fsfcld ,
double *cicewp ,double *cliqwp ,double *reice ,double *reliq ,
double *tauaer ,double *ssaaer ,double *asmaer ,double *ecaer ,
double *swuflx ,double *swdflx ,double *swhr ,double *swuflxc ,double *swdflxc ,double *swhrc)
cdef class RadiationRRTM(RadiationBase):
def __init__(self, namelist, LatentHeat LH, ParallelMPI.ParallelMPI Pa):
# Required for surface energy budget calculations, can also be used for stats io
self.srf_lw_down = 0.0
self.srf_sw_down = 0.0
self.srf_lw_up = 0.0
self.srf_sw_up = 0.0
self.srf_lw_down_clr = 0.0
self.srf_sw_down_clr = 0.0
self.srf_lw_up_clr = 0.0
self.srf_sw_up_clr = 0.0
self.toa_lw_down = 0.0
self.toa_sw_down = 0.0
self.toa_lw_up = 0.0
self.toa_sw_up = 0.0
self.toa_lw_down_clr = 0.0
self.toa_sw_down_clr = 0.0
self.toa_lw_up_clr = 0.0
self.toa_sw_up_clr = 0.0
casename = namelist['meta']['casename']
self.modified_adiabat = False
self.read_file = False
self.time_varying = False
if casename == 'SHEBA':
self.profile_name = 'sheba'
elif casename == 'DYCOMS_RF01':
self.profile_name = 'cgils_ctl_s12'
elif casename == 'CGILS':
loc = namelist['meta']['CGILS']['location']
is_p2 = namelist['meta']['CGILS']['P2']
if is_p2:
self.profile_name = 'cgils_p2_s'+str(loc)
else:
self.profile_name = 'cgils_ctl_s'+str(loc)
elif casename == 'ZGILS':
loc = namelist['meta']['ZGILS']['location']
self.profile_name = 'cgils_ctl_s'+str(loc)
self.modified_adiabat = True
self.reference_profile = AdjustedMoistAdiabat(namelist, LH, Pa)
self.Tg_adiabat = 295.0
self.Pg_adiabat = 1000.0e2
self.RH_adiabat = 0.3
elif casename == 'GCMNew' or casename=='GCMVarying':
self.read_file = True
self.file = str(namelist['gcm']['file'])
try:
self.griddata = namelist['gcm']['griddata']
except:
self.griddata = False
if self.griddata:
self.lat = namelist['gcm']['lat']
self.lon = namelist['gcm']['lon']
else:
self.site = namelist['gcm']['site']
else:
Pa.root_print('RadiationRRTM: Case ' + casename + ' has no known extension profile')
Pa.kill()
if casename=='GCMVarying':
self.time_varying = True
try:
self.forcing_frequency = namelist['forcing']['frequency']
except:
self.forcing_frequency = 6.0
# Namelist options related to the profile extension
try:
self.n_buffer = namelist['radiation']['RRTM']['buffer_points']
except:
self.n_buffer = 0
try:
self.stretch_factor = namelist['radiation']['RRTM']['stretch_factor']
except:
self.stretch_factor = 1.0
try:
self.patch_pressure = namelist['radiation']['RRTM']['patch_pressure']
except:
self.patch_pressure = 1000.00*100.0
# Namelist options related to gas concentrations
try:
self.co2_factor = namelist['radiation']['RRTM']['co2_factor']
except:
self.co2_factor = 1.0
try:
self.h2o_factor = namelist['radiation']['RRTM']['h2o_factor']
except:
self.h2o_factor = 1.0
# Namelist options related to insolation
try:
self.dyofyr = namelist['radiation']['RRTM']['dyofyr']
except:
self.dyofyr = 0
try:
self.adjes = namelist['radiation']['RRTM']['adjes']
except:
Pa.root_print('Insolation adjustive factor not set so RadiationRRTM takes default value: adjes = 0.5 (12 hour of daylight).')
self.adjes = 0.5
try:
self.scon = namelist['radiation']['RRTM']['solar_constant']
except:
Pa.root_print('Solar Constant not set so RadiationRRTM takes default value: scon = 1360.0 .')
self.scon = 1360.0
try:
self.toa_sw = namelist['radiation']['RRTM']['toa_sw']
except:
Pa.root_print('TOA shortwave not set so RadiationRRTM takes default value: toa_sw = 420.0 .')
self.toa_sw = 420.0
try:
self.coszen = namelist['radiation']['RRTM']['coszen']
except:
if (self.toa_sw > 0.0):
self.coszen = self.toa_sw / (self.scon * self.adjes)
else:
Pa.root_print('Mean Daytime cos(SZA) not set so RadiationRRTM takes default value: coszen = 2.0/pi .')
self.coszen = 2.0/pi
try:
self.time_varying_coszen = namelist['radiation']['RRTM']['time_varying_coszen']
except:
self.time_varying_coszen = False
if self.read_file:
if self.griddata:
rdr = cfreader_grid(self.file, self.lat, self.lon)
else:
rdr = cfreader(self.file, self.site)
self.toa_sw = rdr.get_timeseries_mean('rsdt')
if not self.time_varying_coszen:
self.coszen = rdr.get_value('coszen')
self.adjes = self.toa_sw / (self.scon * self.coszen)
try:
self.adif = namelist['radiation']['RRTM']['adif']
except:
Pa.root_print('Surface diffusive albedo not set so RadiationRRTM takes default value: adif = 0.06 .')
self.adif = 0.06
try:
self.adir = namelist['radiation']['RRTM']['adir']
except:
if (self.coszen > 0.0):
self.adir = (.026/(self.coszen**1.7+.065) + (.15*(self.coszen-0.10)*(self.coszen-0.50)*(self.coszen-1.00)))
else:
self.adir = 0.0
Pa.root_print('Surface direct albedo not set so RadiationRRTM computes value: adif = %5.4f .'%(self.adir))
try:
self.clear_sky = namelist['radiation']['RRTM']['clear_sky']
except:
self.clear_sky = False
try:
self.uniform_reliq = namelist['radiation']['RRTM']['uniform_reliq']
except:
Pa.root_print('uniform_reliq not set so RadiationRRTM takes default value: uniform_reliq = False.')
self.uniform_reliq = False
try:
self.radiation_frequency = namelist['radiation']['RRTM']['frequency']
except:
Pa.root_print('radiation_frequency not set so RadiationRRTM takes default value: radiation_frequency = 0.0 (compute at every step).')
self.radiation_frequency = 0.0
self.next_radiation_calculate = 0.0
return
cpdef initialize(self, Grid.Grid Gr, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
self.lw_flux_up = np.zeros((Gr.dims.npg,), dtype=np.double, order='c')
self.lw_flux_up_clr = np.zeros((Gr.dims.npg,), dtype=np.double, order='c')
self.lw_flux_down = np.zeros((Gr.dims.npg,), dtype=np.double, order='c')
self.lw_flux_down_clr = np.zeros((Gr.dims.npg,), dtype=np.double, order='c')
self.sw_flux_up = np.zeros((Gr.dims.npg,), dtype=np.double, order='c')
self.sw_flux_up_clr = np.zeros((Gr.dims.npg,), dtype=np.double, order='c')
self.sw_flux_down = np.zeros((Gr.dims.npg,), dtype=np.double, order='c')
self.sw_flux_down_clr = np.zeros((Gr.dims.npg,), dtype=np.double, order='c')
NS.add_profile('lw_flux_up', Gr, Pa )
NS.add_profile('lw_flux_up_clr', Gr, Pa )
NS.add_profile('lw_flux_down', Gr, Pa)
NS.add_profile('lw_flux_down_clr', Gr, Pa)
NS.add_profile('sw_flux_up', Gr, Pa)
NS.add_profile('sw_flux_up_clr', Gr, Pa)
NS.add_profile('sw_flux_down', Gr, Pa)
NS.add_profile('sw_flux_down_clr', Gr, Pa)
NS.add_ts('toa_lw_flux_up', Gr, Pa)
NS.add_ts('toa_lw_flux_down', Gr, Pa)
NS.add_ts('toa_sw_flux_up', Gr, Pa)
NS.add_ts('toa_sw_flux_down', Gr, Pa)
NS.add_ts('srf_lw_flux_up_clr', Gr, Pa)
NS.add_ts('srf_lw_flux_down_clr', Gr, Pa)
NS.add_ts('srf_sw_flux_up_clr', Gr, Pa)
NS.add_ts('srf_sw_flux_down_clr', Gr, Pa)
NS.add_ts('toa_lw_flux_up_clr', Gr, Pa)
NS.add_ts('toa_lw_flux_down_clr', Gr, Pa)
NS.add_ts('toa_sw_flux_up_clr', Gr, Pa)
NS.add_ts('toa_sw_flux_down_clr', Gr, Pa)
RadiationBase.initialize(self, Gr, NS, Pa)
self.radiation_initialized = False
self.t_indx = 0
return
cpdef initialize_profiles(self, Grid.Grid Gr, ReferenceState.ReferenceState RS, DiagnosticVariables.DiagnosticVariables DV,
NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
cdef:
Py_ssize_t qv_shift = DV.get_varshift(Gr, 'qv')
Py_ssize_t t_shift = DV.get_varshift(Gr, 'temperature')
double [:,:] qv_pencils = self.z_pencil.forward_double(&Gr.dims, Pa, &DV.values[qv_shift])
double [:,:] t_pencils = self.z_pencil.forward_double(&Gr.dims, Pa, &DV.values[t_shift])
Py_ssize_t nz = Gr.dims.n[2]
Py_ssize_t gw = Gr.dims.gw
Py_ssize_t i,k
Py_ssize_t n_adiabat
double [:] pressures_adiabat
# Construct the extension of the profiles, including a blending region between the given profile and LES domain (if desired)
if self.modified_adiabat:
# pressures = profile_data[self.profile_name]['pressure'][:]
pressures = np.arange(25*100, 1015*100, 10*100)
pressures = np.array(pressures[::-1], dtype=np.double)
n_adiabat = np.shape(pressures)[0]
self.reference_profile.initialize(Pa, pressures, n_adiabat, self.Pg_adiabat, self.Tg_adiabat, self.RH_adiabat)
temperatures =np.array( self.reference_profile.temperature)
vapor_mixing_ratios = np.array(self.reference_profile.rv)
elif self.read_file:
if self.griddata:
rdr = cfreader_grid(self.file, self.lat, self.lon)
else:
rdr = cfreader(self.file, self.site)
pressures = rdr.get_profile_mean('pfull')
temperatures = rdr.get_profile_mean('ta')
vapor_mixing_ratios = rdr.get_profile_mean('hus')
else:
pressures = profile_data[self.profile_name]['pressure'][:]
temperatures = profile_data[self.profile_name]['temperature'][:]
vapor_mixing_ratios = profile_data[self.profile_name]['vapor_mixing_ratio'][:]
# Sanity check that patch_pressure < minimum LES domain pressure
dp = np.abs(RS.p0_half_global[nz + gw -1] - RS.p0_half_global[nz + gw -2])
self.patch_pressure = np.minimum(self.patch_pressure, RS.p0_half_global[nz + gw -1] - dp )
#n_profile = len(pressures[pressures<=self.patch_pressure]) # nprofile = # of points in the fixed profile to use
# above syntax tends to cause problems so use a more robust way
n_profile = 0
for pressure in pressures:
if pressure <= self.patch_pressure:
n_profile += 1
self.n_ext = n_profile + self.n_buffer # n_ext = total # of points to add to LES domain (buffer portion + fixed profile portion)
# Create the space for the extensions (to be tacked on to top of LES pencils)
# we declare these as class members in case we want to modify the buffer zone during run time
# i.e. if there is some drift to top of LES profiles
self.p_ext = np.zeros((self.n_ext,),dtype=np.double)
self.t_ext = np.zeros((self.n_ext,),dtype=np.double)
self.rv_ext = np.zeros((self.n_ext,),dtype=np.double)
cdef Py_ssize_t count = 0
for k in xrange(len(pressures)-n_profile, len(pressures)):
self.p_ext[self.n_buffer+count] = pressures[k]
self.t_ext[self.n_buffer+count] = temperatures[k]
self.rv_ext[self.n_buffer+count] = vapor_mixing_ratios[k]
count += 1
# Now create the buffer zone
if self.n_buffer > 0:
dp = np.abs(RS.p0_half_global[nz + gw -1] - RS.p0_half_global[nz + gw -2])
self.p_ext[0] = RS.p0_half_global[nz + gw -1] - dp
for i in range(1,self.n_buffer):
self.p_ext[i] = self.p_ext[i-1] - (i+1.0)**self.stretch_factor * dp
# Sanity check the buffer zone
if self.p_ext[self.n_buffer-1] < self.p_ext[self.n_buffer]:
Pa.root_print('Radiation buffer zone extends too far')
Pa.kill()
# Pressures of "data" points for interpolation, must be INCREASING pressure
xi = np.array([self.p_ext[self.n_buffer+1],self.p_ext[self.n_buffer],RS.p0_half_global[nz + gw -1],RS.p0_half_global[nz + gw -2] ],dtype=np.double)
# interpolation for temperature
ti = np.array([self.t_ext[self.n_buffer+1],self.t_ext[self.n_buffer], t_pencils[0,nz-1],t_pencils[0,nz-2] ], dtype = np.double)
# interpolation for vapor mixing ratio
rv_m2 = qv_pencils[0, nz-2]/ (1.0 - qv_pencils[0, nz-2])
rv_m1 = qv_pencils[0,nz-1]/(1.0-qv_pencils[0,nz-1])
ri = np.array([self.rv_ext[self.n_buffer+1],self.rv_ext[self.n_buffer], rv_m1, rv_m2 ], dtype = np.double)
for i in xrange(self.n_buffer):
self.rv_ext[i] = pchip_interpolate(xi, ri, self.p_ext[i] )
self.t_ext[i] = pchip_interpolate(xi,ti, self.p_ext[i])
#--- Plotting to evaluate implementation of buffer zone
#--- Comment out when not running locally
for i in xrange(Gr.dims.nlg[2]):
qv_pencils[0,i] = qv_pencils[0, i]/ (1.0 - qv_pencils[0, i])
#
# Plotting to evaluate implementation of buffer zone
# plt.figure(1)
# plt.plot(self.rv_ext,self.p_ext,'or')
# plt.plot(vapor_mixing_ratios, pressures)
# plt.plot(qv_pencils[0,:], RS.p0_half_global[gw:-gw],'ob')
# plt.gca().invert_yaxis()
# plt.figure(2)
# plt.plot(self.t_ext,self.p_ext,'-or')
# plt.plot(temperatures,pressures)
# plt.plot(t_pencils[0,:], RS.p0_half_global[gw:-gw],'-ob')
# plt.gca().invert_yaxis()
# plt.show()
#---END Plotting to evaluate implementation of buffer zone
self.p_full = np.zeros((self.n_ext+nz,), dtype=np.double)
self.pi_full = np.zeros((self.n_ext+1+nz,),dtype=np.double)
self.p_full[0:nz] = RS.p0_half_global[gw:nz+gw]
self.p_full[nz:]=self.p_ext[:]
self.pi_full[0:nz] = RS.p0_global[gw:nz+gw]
for i in range(nz,self.n_ext+nz):
self.pi_full[i] = (self.p_full[i] + self.p_full[i-1]) * 0.5
self.pi_full[self.n_ext + nz] = 2.0 * self.p_full[self.n_ext + nz -1 ] - self.pi_full[self.n_ext + nz -1]
# try to get ozone
try:
o3_trace = profile_data[self.profile_name]['o3_vmr'][:] # O3 VMR (from SRF to TOP)
o3_pressure = profile_data[self.profile_name]['pressure'][:]/100.0 # Pressure (from SRF to TOP) in hPa
# can't do simple interpolation... Need to conserve column path !!!
use_o3in = True
except:
try:
o3_trace = profile_data[self.profile_name]['o3_mmr'][:]*28.97/47.9982 # O3 MR converted to VMR
o3_pressure = profile_data[self.profile_name]['pressure'][:]/100.0 # Pressure (from SRF to TOP) in hPa
# can't do simple interpolation... Need to conserve column path !!!
use_o3in = True
except:
Pa.root_print('O3 profile not set so default RRTM profile will be used.')
use_o3in = False
#Initialize rrtmg_lw and rrtmg_sw
cdef double cpdair = np.float64(cpd)
c_rrtmg_lw_init(&cpdair)
c_rrtmg_sw_init(&cpdair)
# Read in trace gas data
lw_input_file = './RRTMG/lw/data/rrtmg_lw.nc'
lw_gas = nc.Dataset(lw_input_file, "r")
lw_pressure = np.asarray(lw_gas.variables['Pressure'])
lw_absorber = np.asarray(lw_gas.variables['AbsorberAmountMLS'])
lw_absorber = np.where(lw_absorber>2.0, np.zeros_like(lw_absorber), lw_absorber)
lw_ngas = lw_absorber.shape[1]
lw_np = lw_absorber.shape[0]
# 9 Gases: O3, CO2, CH4, N2O, O2, CFC11, CFC12, CFC22, CCL4
# From rad_driver.f90, lines 546 to 552
trace = np.zeros((9,lw_np),dtype=np.double,order='F')
for i in xrange(lw_ngas):
gas_name = ''.join(lw_gas.variables['AbsorberNames'][i,:])
if 'O3' in gas_name:
trace[0,:] = lw_absorber[:,i].reshape(1,lw_np)
elif 'CO2' in gas_name:
trace[1,:] = lw_absorber[:,i].reshape(1,lw_np)*self.co2_factor
elif 'CH4' in gas_name:
trace[2,:] = lw_absorber[:,i].reshape(1,lw_np)
elif 'N2O' in gas_name:
trace[3,:] = lw_absorber[:,i].reshape(1,lw_np)
elif 'O2' in gas_name:
trace[4,:] = lw_absorber[:,i].reshape(1,lw_np)
elif 'CFC11' in gas_name:
trace[5,:] = lw_absorber[:,i].reshape(1,lw_np)
elif 'CFC12' in gas_name:
trace[6,:] = lw_absorber[:,i].reshape(1,lw_np)
elif 'CFC22' in gas_name:
trace[7,:] = lw_absorber[:,i].reshape(1,lw_np)
elif 'CCL4' in gas_name:
trace[8,:] = lw_absorber[:,i].reshape(1,lw_np)
# From rad_driver.f90, lines 585 to 620
trpath = np.zeros((nz + self.n_ext + 1, 9),dtype=np.double,order='F')
# plev = self.pi_full[:]/100.0
for i in xrange(1, nz + self.n_ext + 1):
trpath[i,:] = trpath[i-1,:]
if (self.pi_full[i-1]/100.0 > lw_pressure[0]):
trpath[i,:] = trpath[i,:] + (self.pi_full[i-1]/100.0 - np.max((self.pi_full[i]/100.0,lw_pressure[0])))/g*trace[:,0]
for m in xrange(1,lw_np):
plow = np.min((self.pi_full[i-1]/100.0,np.max((self.pi_full[i]/100.0, lw_pressure[m-1]))))
pupp = np.min((self.pi_full[i-1]/100.0,np.max((self.pi_full[i]/100.0, lw_pressure[m]))))
if (plow > pupp):
pmid = 0.5*(plow+pupp)
wgtlow = (pmid-lw_pressure[m])/(lw_pressure[m-1]-lw_pressure[m])
wgtupp = (lw_pressure[m-1]-pmid)/(lw_pressure[m-1]-lw_pressure[m])
trpath[i,:] = trpath[i,:] + (plow-pupp)/g*(wgtlow*trace[:,m-1] + wgtupp*trace[:,m])
if (self.pi_full[i]/100.0 < lw_pressure[lw_np-1]):
trpath[i,:] = trpath[i,:] + (np.min((self.pi_full[i-1]/100.0,lw_pressure[lw_np-1]))-self.pi_full[i]/100.0)/g*trace[:,lw_np-1]
tmpTrace = np.zeros((nz + self.n_ext,9),dtype=np.double,order='F')
for i in xrange(9):
for k in xrange(nz + self.n_ext):
tmpTrace[k,i] = g*100.0/(self.pi_full[k]-self.pi_full[k+1])*(trpath[k+1,i]-trpath[k,i])
if use_o3in == False:
self.o3vmr = np.array(tmpTrace[:,0],dtype=np.double, order='F')
else:
# o3_trace, o3_pressure
trpath_o3 = np.zeros(nz + self.n_ext+1, dtype=np.double, order='F')
# plev = self.pi_full/100.0
o3_np = o3_trace.shape[0]
for i in xrange(1, nz + self.n_ext+1):
trpath_o3[i] = trpath_o3[i-1]
if (self.pi_full[i-1]/100.0 > o3_pressure[0]):
trpath_o3[i] = trpath_o3[i] + (self.pi_full[i-1]/100.0 - np.max((self.pi_full[i]/100.0,o3_pressure[0])))/g*o3_trace[0]
for m in xrange(1,o3_np):
plow = np.min((self.pi_full[i-1]/100.0,np.max((self.pi_full[i]/100.0, o3_pressure[m-1]))))
pupp = np.min((self.pi_full[i-1]/100.0,np.max((self.pi_full[i]/100.0, o3_pressure[m]))))
if (plow > pupp):
pmid = 0.5*(plow+pupp)
wgtlow = (pmid-o3_pressure[m])/(o3_pressure[m-1]-o3_pressure[m])
wgtupp = (o3_pressure[m-1]-pmid)/(o3_pressure[m-1]-o3_pressure[m])
trpath_o3[i] = trpath_o3[i] + (plow-pupp)/g*(wgtlow*o3_trace[m-1] + wgtupp*o3_trace[m])
if (self.pi_full[i]/100.0 < o3_pressure[o3_np-1]):
trpath_o3[i] = trpath_o3[i] + (np.min((self.pi_full[i-1]/100.0,o3_pressure[o3_np-1]))-self.pi_full[i]/100.0)/g*o3_trace[o3_np-1]
tmpTrace_o3 = np.zeros( nz + self.n_ext, dtype=np.double, order='F')
for k in xrange(nz + self.n_ext):
tmpTrace_o3[k] = g *100.0/(self.pi_full[k]-self.pi_full[k+1])*(trpath_o3[k+1]-trpath_o3[k])
self.o3vmr = np.array(tmpTrace_o3[:],dtype=np.double, order='F')
self.co2vmr = np.array(tmpTrace[:,1],dtype=np.double, order='F')
self.ch4vmr = np.array(tmpTrace[:,2],dtype=np.double, order='F')
self.n2ovmr = np.array(tmpTrace[:,3],dtype=np.double, order='F')
self.o2vmr = np.array(tmpTrace[:,4],dtype=np.double, order='F')
self.cfc11vmr = np.array(tmpTrace[:,5],dtype=np.double, order='F')
self.cfc12vmr = np.array(tmpTrace[:,6],dtype=np.double, order='F')
self.cfc22vmr = np.array( tmpTrace[:,7],dtype=np.double, order='F')
self.ccl4vmr = np.array(tmpTrace[:,8],dtype=np.double, order='F')
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState RS,
PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV,
Surface.SurfaceBase Sur, TimeStepping.TimeStepping TS,
ParallelMPI.ParallelMPI Pa):
if not self.radiation_initialized or int(TS.t // (3600.0 * self.forcing_frequency)) > self.t_indx and self.time_varying:
self.t_indx = int(TS.t // (3600.0 * self.forcing_frequency))
self.radiation_initialized = True
Pa.root_print('Updating Time Varying Radiation Parameters')
if self.griddata:
rdr = cfreader_grid(self.file, self.lat, self.lon)
else:
rdr = cfreader(self.file, self.site)
self.toa_sw = rdr.get_timeseries_mean('rsdt', instant=True, t_idx=self.t_indx)
self.adjes = self.toa_sw / (self.scon * self.coszen)
if self.time_varying_coszen:
self.adjes = 1.0
self.coszen = self.toa_sw / (self.scon * self.adjes)
Pa.root_print('Finished Updating Time Varying Radiation Parameters')
if TS.rk_step == 0:
if self.radiation_frequency <= 0.0:
self.update_RRTM(Gr, RS, PV, DV,Sur, Pa)
elif TS.t >= self.next_radiation_calculate:
self.update_RRTM(Gr, RS, PV, DV, Sur, Pa)
self.next_radiation_calculate = (TS.t//self.radiation_frequency + 1.0) * self.radiation_frequency
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 i, j, k, 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 s_shift = PV.get_varshift(Gr, 's')
Py_ssize_t t_shift = DV.get_varshift(Gr, 'temperature')
Py_ssize_t qt_shift = PV.get_varshift(Gr, 'qt')
# Now update entropy tendencies
with nogil:
for i in xrange(imin, imax):
ishift = i * istride
for j in xrange(jmin, jmax):