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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 pylab as plt
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, cos, sqrt, sin
from thermodynamic_functions cimport cpm_c
include 'parameters.pxi'
from profiles import profile_data
# import matplotlib.pyplot as plt
import sys
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
if use_rrtm:
return RadiationRRTM(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 == 'Isdac':
return RadiationIsdac()
elif casename == 'IsdacCC':
return RadiationRRTM(namelist,LH, Pa)
elif casename == 'CGILS':
return RadiationRRTM(namelist,LH, Pa)
elif casename == 'ZGILS':
return RadiationRRTM(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')
self.uflux_lw = np.zeros((Gr.dims.npg,), dtype=np.double, order='c')
self.dflux_lw = np.zeros((Gr.dims.npg,), dtype=np.double, order='c')
self.uflux_sw = np.zeros((Gr.dims.npg,), dtype=np.double, order='c')
self.dflux_sw = np.zeros((Gr.dims.npg,), dtype=np.double, order='c')
self.heating_rate_clear = np.zeros((Gr.dims.npg,), dtype=np.double, order='c')
self.uflux_lw_clear = np.zeros((Gr.dims.npg,), dtype=np.double, order='c')
self.dflux_lw_clear = np.zeros((Gr.dims.npg,), dtype=np.double, order='c')
self.uflux_sw_clear = np.zeros((Gr.dims.npg,), dtype=np.double, order='c')
self.dflux_sw_clear = 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('lw_flux_up', Gr, Pa)
NS.add_profile('lw_flux_down', Gr, Pa)
NS.add_profile('sw_flux_up', Gr, Pa)
NS.add_profile('sw_flux_down', 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)
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_profile('radiative_heating_rate_clear', Gr, Pa)
NS.add_profile('lw_flux_up_clear', Gr, Pa)
NS.add_profile('lw_flux_down_clear', Gr, Pa)
NS.add_profile('sw_flux_up_clear', Gr, Pa)
NS.add_profile('sw_flux_down_clear', Gr,Pa)
NS.add_ts('srf_lw_flux_up_clear', Gr, Pa)
NS.add_ts('srf_lw_flux_down_clear', Gr, Pa)
NS.add_ts('srf_sw_flux_up_clear', Gr, Pa)
NS.add_ts('srf_sw_flux_down_clear', Gr, Pa)
NS.add_ts('toa_lw_flux_up_clear', Gr, Pa)
NS.add_ts('toa_lw_flux_down_clear', Gr, Pa)
NS.add_ts('toa_sw_flux_up_clear', Gr, Pa)
NS.add_ts('toa_sw_flux_down_clear', Gr, Pa)
return
cpdef initialize_profiles(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref, Th, DiagnosticVariables.DiagnosticVariables DV,
Surface.SurfaceBase Sur, ParallelMPI.ParallelMPI Pa):
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref,
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 Ref, 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] * Ref.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)
tmp = Pa.HorizontalMean(Gr, &self.uflux_lw[0])
NS.write_profile('lw_flux_up', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMean(Gr, &self.dflux_lw[0])
NS.write_profile('lw_flux_down', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMean(Gr, &self.uflux_sw[0])
NS.write_profile('sw_flux_up', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMean(Gr, &self.dflux_sw[0])
NS.write_profile('sw_flux_down', 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)
NS.write_ts('toa_lw_flux_up',self.toa_lw_up, Pa ) # Units are W/m^2
NS.write_ts('toa_lw_flux_down', self.toa_lw_down, Pa)
NS.write_ts('toa_sw_flux_up', self.toa_sw_up, Pa)
NS.write_ts('toa_sw_flux_down', self.toa_sw_down, Pa)
###
tmp = Pa.HorizontalMean(Gr, &self.heating_rate_clear[0])
NS.write_profile('radiative_heating_rate_clear', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMean(Gr, &self.uflux_lw_clear[0])
NS.write_profile('lw_flux_up_clear', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMean(Gr, &self.dflux_lw_clear[0])
NS.write_profile('lw_flux_down_clear', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMean(Gr, &self.uflux_sw_clear[0])
NS.write_profile('sw_flux_up_clear', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMean(Gr, &self.dflux_sw_clear[0])
NS.write_profile('sw_flux_down_clear', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
NS.write_ts('srf_lw_flux_up_clear',self.srf_lw_up_clear, Pa ) # Units are W/m^2
NS.write_ts('srf_lw_flux_down_clear', self.srf_lw_down_clear, Pa)
NS.write_ts('srf_sw_flux_up_clear', self.srf_sw_up_clear, Pa)
NS.write_ts('srf_sw_flux_down_clear', self.srf_sw_down_clear, Pa)
NS.write_ts('toa_lw_flux_up_clear',self.toa_lw_up_clear, Pa ) # Units are W/m^2
NS.write_ts('toa_lw_flux_down_clear', self.toa_lw_down_clear, Pa)
NS.write_ts('toa_sw_flux_up_clear', self.toa_sw_up_clear, Pa)
NS.write_ts('toa_sw_flux_down_clear', self.toa_sw_down_clear, 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 Ref, Th, DiagnosticVariables.DiagnosticVariables DV,
Surface.SurfaceBase Sur, ParallelMPI.ParallelMPI Pa):
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref,
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 Ref, DiagnosticVariables.DiagnosticVariables DV,
NetCDFIO_Stats NS, ParallelMPI.ParallelMPI 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 Ref, Th, DiagnosticVariables.DiagnosticVariables DV,
Surface.SurfaceBase Sur, ParallelMPI.ParallelMPI Pa):
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref,
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 = PV.get_varshift(Gr, 's')
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.z
double[:] rho = Ref.rho0
double[:] rho_half = Ref.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] * dz
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] * dz
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 / 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]
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 Ref, DiagnosticVariables.DiagnosticVariables DV,
NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
RadiationBase.stats_io(self, Gr, Ref, 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 Ref, Th, DiagnosticVariables.DiagnosticVariables DV,
Surface.SurfaceBase Sur, ParallelMPI.ParallelMPI Pa):
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref,
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.z
double[:] rho = Ref.rho0
double[:] rho_half = Ref.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] * dz
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 / 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] * Ref.alpha0_half[k]
self.dTdt_rad[ijk] = self.heating_rate[ijk] / cpd * Ref.alpha0_half[k]
return
cpdef stats_io(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref, DiagnosticVariables.DiagnosticVariables DV,
NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
RadiationBase.stats_io(self, Gr, Ref, DV, NS, Pa)
return
cdef class RadiationIsdac(RadiationBase):
def __init__(self):
self.kap = 170.0
self.f0 = 72.0
self.f1 = 15.0
return
cpdef initialize(self, Grid.Grid Gr, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
RadiationBase.initialize(self, Gr, NS, Pa)
self.radiative_flux = np.zeros(Gr.dims.npg, dtype=np.double, order='c')
NS.add_profile('radiative_flux',Gr, Pa)
return
cpdef initialize_profiles(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref, Th, DiagnosticVariables.DiagnosticVariables DV,
Surface.SurfaceBase Sur, ParallelMPI.ParallelMPI Pa):
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref,
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 = PV.get_varshift(Gr, 's')
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.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 [:] heating_rate = self.heating_rate[:]
double [:] radiative_flux = self.radiative_flux[:]
double q_0
double q_1
double dz = Gr.dims.dx[2]
double dzi = Gr.dims.dxi[2]
double[:] z = Gr.z
double[:] rho = Ref.rho0
double[:] rho_half = Ref.rho0_half
with nogil:
for pi in xrange(self.z_pencil.n_local_pencils):
# 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] * dz
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] * dz
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 / rho_half[k]
# Now transpose the flux pencils
self.z_pencil.reverse_double(& Gr.dims, Pa, f_heat, & heating_rate[0])
self.z_pencil.reverse_double(& Gr.dims, Pa, f_rad[:,:-1], & radiative_flux[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] += 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 Ref, DiagnosticVariables.DiagnosticVariables DV,
NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
RadiationBase.stats_io(self, Gr, Ref, DV, NS, Pa)
cdef double [:] tmp
tmp[:] = 0.0
tmp = Pa.HorizontalMean(Gr, &self.radiative_flux[0])
NS.write_profile('radiative_flux',tmp[Gr.dims.gw:-Gr.dims.gw],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.toa_lw_down = 0.0
self.toa_sw_down = 0.0
self.toa_lw_up = 0.0
self.toa_sw_up = 0.0
casename = namelist['meta']['casename']
self.modified_adiabat = False
if casename == 'Sheba':
self.profile_name = 'arctic'
elif casename == 'Isdac':
self.profile_name = 'arctic'
elif casename == 'IsdacCC':
self.profile_name = 'arctic'
elif casename == 'Mpace':
self.profile_name = 'arctic'
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
else:
Pa.root_print('RadiationRRTM: Case ' + casename + ' has no known extension profile')
Pa.kill()
# 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_init = namelist['radiation']['RRTM']['dyofyr']
except:
self.dyofyr_init = 0
self.dyofyr = self.dyofyr_init
#Adjes is not used if 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.daily_mean_sw = namelist['radiation']['RRTM']['daily_mean_sw']
except:
print('Use daily mean sw in RRTM!')
self.daily_mean_sw = True
if self.daily_mean_sw:
try:
self.coszen = namelist['radiation']['RRTM']['coszen']
except:
Pa.root_print('Mean Daytime cos(SZA) not set so RadiationRRTM takes default value: coszen = 2.0/pi .')
self.coszen = 2.0/pi
else:
try:
self.latitude = namelist['radiation']['RRTM']['latitude']
self.longitude = namelist['radiation']['RRTM']['longitude']
self.hourz_init = namelist['radiation']['RRTM']['hourz']
self.coszen = cos_sza(self.dyofyr_init, self.hourz_init, self.latitude, self.longitude)
print('Calculated cos(sza) based on time and location, cos(sza) = %5.4f .'%(self.coszen))
except:
print('Hourz, latitude, or longitude not set. Cannot calculate RRTM SW. Exit now!')
sys.exit()
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.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.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
#For IsdacCC simulations, initial profiles need to be justified correspondingly
try:
self.IsdacCC_dT = namelist['initial']['dSST'] + namelist['initial']['dTi'] - 5.0
print('IsdacCC case: RRTM profiles are shifted according to %2.2f temperature change.'%(self.IsdacCC_dT))
except:
self.IsdacCC_dT = 0.0
return
cpdef initialize(self, Grid.Grid Gr, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
RadiationBase.initialize(self, Gr, NS, Pa)
#Initialize rrtmg_lw and rrtmg_sw
cdef double cpdair = np.float64(cpd)
c_rrtmg_lw_init(&cpdair)
c_rrtmg_sw_init(&cpdair)
cdef:
Py_ssize_t nz = Gr.dims.n[2]
Py_ssize_t n_pencils = self.z_pencil.n_local_pencils
self.uflx_lw_pencils = np.zeros((n_pencils, nz), dtype=np.double, order='c')
self.dflx_lw_pencils = np.zeros((n_pencils, nz), dtype=np.double, order='c')
self.uflx_sw_pencils = np.zeros((n_pencils, nz), dtype=np.double, order='c')
self.dflx_sw_pencils = np.zeros((n_pencils, nz), dtype=np.double, order='c')
return
cpdef initialize_profiles(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref, Th, DiagnosticVariables.DiagnosticVariables DV,
Surface.SurfaceBase Sur, 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)
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(Ref.p0_half_global[nz + gw -1] - Ref.p0_half_global[nz + gw -2])
self.patch_pressure = np.minimum(self.patch_pressure, Ref.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
if self.IsdacCC_dT == 0.0:
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
else:
specific_humidity = vapor_mixing_ratios / (1.0 + vapor_mixing_ratios)
for k in xrange(len(pressures)-n_profile, len(pressures)):
self.p_ext[self.n_buffer+count] = pressures[k]
qt_new = get_humidity(temperatures[k], specific_humidity[k], pressures[k], temperatures[k]+self.IsdacCC_dT, Th)
self.t_ext[self.n_buffer+count] = temperatures[k] + self.IsdacCC_dT
self.rv_ext[self.n_buffer+count] = qt_new / (1.0 - qt_new)
count += 1
# Now create the buffer zone
if self.n_buffer > 0:
dp = np.abs(Ref.p0_half_global[nz + gw -1] - Ref.p0_half_global[nz + gw -2])
self.p_ext[0] = Ref.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],Ref.p0_half_global[nz + gw -1],Ref.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,:], Ref.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,:], Ref.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] = Ref.p0_half_global[gw:nz+gw]
self.p_full[nz:]=self.p_ext[:]
self.pi_full[0:nz] = Ref.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
# 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])