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EDMF_Environment.pyx
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EDMF_Environment.pyx
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#!python
#cython: boundscheck=False
#cython: wraparound=False
#cython: initializedcheck=False
#cython: cdivision=True
import numpy as np
import sys
import cython
include "parameters.pxi"
from EDMF_Rain cimport RainVariables
from Grid cimport Grid
from TimeStepping cimport TimeStepping
from ReferenceState cimport ReferenceState
from Variables cimport VariableDiagnostic, GridMeanVariables
from libc.math cimport fmax, fmin, sqrt, exp, erf, log, fabs
from thermodynamic_functions cimport *
from microphysics_functions cimport *
cdef class EnvironmentVariable:
def __init__(self, nz, loc, kind, name, units):
self.values = np.zeros((nz,),dtype=np.double, order='c')
self.flux = np.zeros((nz,),dtype=np.double, order='c')
if loc != 'half' and loc != 'full':
print('Invalid location setting for variable! Must be half or full')
self.loc = loc
if kind != 'scalar' and kind != 'velocity':
print ('Invalid kind setting for variable! Must be scalar or velocity')
self.kind = kind
self.name = name
self.units = units
cpdef set_bcs(self,Grid Gr):
cdef:
Py_ssize_t i,k
Py_ssize_t start_low = Gr.gw - 1
Py_ssize_t start_high = Gr.nzg - Gr.gw - 1
if self.name == 'w':
self.values[start_high] = 0.0
self.values[start_low] = 0.0
for k in xrange(1,Gr.gw):
self.values[start_high+ k] = -self.values[start_high - k ]
self.values[start_low- k] = -self.values[start_low + k ]
else:
for k in xrange(Gr.gw):
self.values[start_high + k +1] = self.values[start_high - k]
self.values[start_low - k] = self.values[start_low + 1 + k]
cdef class EnvironmentVariable_2m:
def __init__(self, nz, loc, kind, name, units):
self.values = np.zeros((nz,),dtype=np.double, order='c')
self.dissipation = np.zeros((nz,),dtype=np.double, order='c')
self.entr_gain = np.zeros((nz,),dtype=np.double, order='c')
self.detr_loss = np.zeros((nz,),dtype=np.double, order='c')
self.buoy = np.zeros((nz,),dtype=np.double, order='c')
self.press = np.zeros((nz,),dtype=np.double, order='c')
self.shear = np.zeros((nz,),dtype=np.double, order='c')
self.interdomain = np.zeros((nz,),dtype=np.double, order='c')
self.rain_src = np.zeros((nz,),dtype=np.double, order='c')
if loc != 'half':
print('Invalid location setting for variable! Must be half')
self.loc = loc
if kind != 'scalar' and kind != 'velocity':
print ('Invalid kind setting for variable! Must be scalar or velocity')
self.kind = kind
self.name = name
self.units = units
cpdef set_bcs(self,Grid Gr):
cdef:
Py_ssize_t i,k
Py_ssize_t start_low = Gr.gw - 1
Py_ssize_t start_high = Gr.nzg - Gr.gw - 1
for k in xrange(Gr.gw):
self.values[start_high + k +1] = self.values[start_high - k]
self.values[start_low - k] = self.values[start_low + 1 + k]
cdef class EnvironmentVariables:
def __init__(self, namelist, Grid Gr ):
cdef Py_ssize_t nz = Gr.nzg
self.Gr = Gr
self.W = EnvironmentVariable(nz, 'full', 'velocity', 'w','m/s' )
self.QT = EnvironmentVariable( nz, 'half', 'scalar', 'qt','kg/kg' )
self.QL = EnvironmentVariable( nz, 'half', 'scalar', 'ql','kg/kg' )
self.RH = EnvironmentVariable( nz, 'half', 'scalar', 'RH','%' )
if namelist['thermodynamics']['thermal_variable'] == 'entropy':
self.H = EnvironmentVariable( nz, 'half', 'scalar', 's','J/kg/K' )
elif namelist['thermodynamics']['thermal_variable'] == 'thetal':
self.H = EnvironmentVariable( nz, 'half', 'scalar', 'thetal','K' )
self.THL = EnvironmentVariable(nz, 'half', 'scalar', 'thetal', 'K')
self.T = EnvironmentVariable( nz, 'half', 'scalar', 'temperature','K' )
self.B = EnvironmentVariable( nz, 'half', 'scalar', 'buoyancy','m^2/s^3' )
self.Area = EnvironmentVariable(nz, 'half', 'scalar', 'env_area', '-')
self.cloud_fraction = EnvironmentVariable(nz, 'half', 'scalar', 'env_cloud_fraction', '-')
# TODO - the flag setting is repeated from Variables.pyx logic
if namelist['turbulence']['scheme'] == 'EDMF_PrognosticTKE':
self.calc_tke = True
else:
self.calc_tke = False
try:
self.calc_tke = namelist['turbulence']['EDMF_PrognosticTKE']['calculate_tke']
except:
pass
try:
self.calc_scalar_var = namelist['turbulence']['EDMF_PrognosticTKE']['calc_scalar_var']
except:
self.calc_scalar_var = False
print('Defaulting to non-calculation of scalar variances')
try:
self.EnvThermo_scheme = str(namelist['thermodynamics']['sgs'])
except:
self.EnvThermo_scheme = 'mean'
print('Defaulting to saturation adjustment and microphysics with respect to environmental means')
if self.calc_tke:
self.TKE = EnvironmentVariable_2m( nz, 'half', 'scalar', 'tke','m^2/s^2' )
if self.calc_scalar_var:
self.QTvar = EnvironmentVariable_2m( nz, 'half', 'scalar', 'qt_var','kg^2/kg^2' )
if namelist['thermodynamics']['thermal_variable'] == 'entropy':
self.Hvar = EnvironmentVariable_2m(nz, 'half', 'scalar', 's_var', '(J/kg/K)^2')
self.HQTcov = EnvironmentVariable_2m(nz, 'half', 'scalar', 's_qt_covar', '(J/kg/K)(kg/kg)' )
elif namelist['thermodynamics']['thermal_variable'] == 'thetal':
self.Hvar = EnvironmentVariable_2m(nz, 'half', 'scalar', 'thetal_var', 'K^2')
self.HQTcov = EnvironmentVariable_2m(nz, 'half', 'scalar', 'thetal_qt_covar', 'K(kg/kg)' )
if self.EnvThermo_scheme == 'quadrature':
if (self.calc_scalar_var == False):
sys.exit('EDMF_Environment.pyx: scalar variance has to be calculated for quadrature saturation and microphysics')
return
cpdef initialize_io(self, NetCDFIO_Stats Stats):
Stats.add_profile('env_w')
Stats.add_profile('env_qt')
Stats.add_profile('env_ql')
Stats.add_profile('env_area')
Stats.add_profile('env_temperature')
Stats.add_profile('env_RH')
if self.H.name == 's':
Stats.add_profile('env_s')
else:
Stats.add_profile('env_thetal')
if self.calc_tke:
Stats.add_profile('env_tke')
if self.calc_scalar_var:
Stats.add_profile('env_Hvar')
Stats.add_profile('env_QTvar')
Stats.add_profile('env_HQTcov')
Stats.add_profile('env_cloud_fraction')
Stats.add_ts('env_cloud_base')
Stats.add_ts('env_cloud_top')
Stats.add_ts('env_cloud_cover')
Stats.add_ts('env_lwp')
return
cpdef io(self, NetCDFIO_Stats Stats, ReferenceState Ref):
Stats.write_profile('env_w', self.W.values[self.Gr.gw:self.Gr.nzg-self.Gr.gw])
Stats.write_profile('env_qt', self.QT.values[self.Gr.gw:self.Gr.nzg-self.Gr.gw])
Stats.write_profile('env_ql', self.QL.values[self.Gr.gw:self.Gr.nzg-self.Gr.gw])
Stats.write_profile('env_area', self.Area.values[self.Gr.gw:self.Gr.nzg-self.Gr.gw])
Stats.write_profile('env_temperature', self.T.values[self.Gr.gw:self.Gr.nzg-self.Gr.gw])
Stats.write_profile('env_RH', self.RH.values[self.Gr.gw:self.Gr.nzg-self.Gr.gw])
if self.H.name == 's':
Stats.write_profile('env_s', self.H.values[self.Gr.gw:self.Gr.nzg-self.Gr.gw])
else:
Stats.write_profile('env_thetal', self.H.values[self.Gr.gw:self.Gr.nzg-self.Gr.gw])
if self.calc_tke:
Stats.write_profile('env_tke', self.TKE.values[self.Gr.gw:self.Gr.nzg-self.Gr.gw])
if self.calc_scalar_var:
Stats.write_profile('env_Hvar', self.Hvar.values[self.Gr.gw:self.Gr.nzg-self.Gr.gw])
Stats.write_profile('env_QTvar', self.QTvar.values[self.Gr.gw:self.Gr.nzg-self.Gr.gw])
Stats.write_profile('env_HQTcov', self.HQTcov.values[self.Gr.gw:self.Gr.nzg-self.Gr.gw])
Stats.write_profile('env_cloud_fraction', self.cloud_fraction.values[self.Gr.gw : self.Gr.nzg-self.Gr.gw])
self.env_cloud_diagnostics(Ref)
# Assuming amximum overlap in environmental clouds
Stats.write_ts('env_cloud_cover', self.cloud_cover)
Stats.write_ts('env_cloud_base', self.cloud_base)
Stats.write_ts('env_cloud_top', self.cloud_top)
Stats.write_ts('env_lwp', self.lwp)
return
cpdef env_cloud_diagnostics(self, ReferenceState Ref):
cdef Py_ssize_t k
self.cloud_top = 0.
self.cloud_base = self.Gr.z_half[self.Gr.nzg - self.Gr.gw - 1]
self.cloud_cover = 0.
self.lwp = 0.
for k in xrange(self.Gr.gw, self.Gr.nzg-self.Gr.gw):
self.lwp += Ref.rho0_half[k] * self.QL.values[k] * self.Area.values[k] * self.Gr.dz
if self.QL.values[k] > 1e-8 and self.Area.values[k] > 1e-3:
self.cloud_base = fmin(self.cloud_base, self.Gr.z_half[k])
self.cloud_top = fmax(self.cloud_top, self.Gr.z_half[k])
self.cloud_cover = fmax(self.cloud_cover, self.Area.values[k] * self.cloud_fraction.values[k])
return
cdef class EnvironmentThermodynamics:
def __init__(self, namelist, Grid Gr, ReferenceState Ref, EnvironmentVariables EnvVar, RainVariables Rain):
self.Gr = Gr
self.Ref = Ref
try:
self.quadrature_order = namelist['thermodynamics']['quadrature_order']
except:
self.quadrature_order = 3
try:
self.quadrature_type = namelist['thermodynamics']['quadrature_type']
except:
self.quadrature_type = 'gaussian'
if EnvVar.H.name == 's':
self.t_to_prog_fp = t_to_entropy_c
self.prog_to_t_fp = eos_first_guess_entropy
elif EnvVar.H.name == 'thetal':
self.t_to_prog_fp = t_to_thetali_c
self.prog_to_t_fp = eos_first_guess_thetal
self.qt_dry = np.zeros(self.Gr.nzg, dtype=np.double, order='c')
self.th_dry = np.zeros(self.Gr.nzg, dtype=np.double, order='c')
self.t_cloudy = np.zeros(self.Gr.nzg, dtype=np.double, order ='c')
self.qv_cloudy = np.zeros(self.Gr.nzg, dtype=np.double, order ='c')
self.qt_cloudy = np.zeros(self.Gr.nzg, dtype=np.double, order='c')
self.th_cloudy = np.zeros(self.Gr.nzg, dtype=np.double, order='c')
self.Hvar_rain_dt = np.zeros(self.Gr.nzg, dtype=np.double, order='c')
self.QTvar_rain_dt = np.zeros(self.Gr.nzg, dtype=np.double, order='c')
self.HQTcov_rain_dt = np.zeros(self.Gr.nzg, dtype=np.double, order='c')
self.prec_source_qt = np.zeros(self.Gr.nzg, dtype=np.double, order='c')
self.prec_source_h = np.zeros(self.Gr.nzg, dtype=np.double, order='c')
return
cdef void update_EnvVar(self, Py_ssize_t k, EnvironmentVariables EnvVar,
double T, double H, double qt, double ql,
double rho) nogil :
EnvVar.T.values[k] = T
EnvVar.THL.values[k] = H
EnvVar.H.values[k] = H
EnvVar.QT.values[k] = qt
EnvVar.QL.values[k] = ql
EnvVar.B.values[k] = buoyancy_c(self.Ref.rho0_half[k], rho)
EnvVar.RH.values[k] = relative_humidity_c(self.Ref.p0_half[k], qt , ql , 0.0, T)
return
cdef void update_EnvRain_sources(self, Py_ssize_t k, EnvironmentVariables EnvVar,
double qr_src, double thl_rain_src) nogil:
self.prec_source_qt[k] = -qr_src * EnvVar.Area.values[k]
self.prec_source_h[k] = thl_rain_src * EnvVar.Area.values[k]
return
cdef void update_cloud_dry(self, Py_ssize_t k, EnvironmentVariables EnvVar,
double T, double th, double qt, double ql,
double qv) nogil :
if ql > 0.0:
EnvVar.cloud_fraction.values[k] = 1.0
self.th_cloudy[k] = th
self.t_cloudy[k] = T
self.qt_cloudy[k] = qt
self.qv_cloudy[k] = qv
else:
EnvVar.cloud_fraction.values[k] = 0.
self.th_dry[k] = th
self.qt_dry[k] = qt
return
cdef void saturation_adjustment(self, EnvironmentVariables EnvVar):
cdef:
Py_ssize_t k
Py_ssize_t gw = self.Gr.gw
eos_struct sa
mph_struct mph
double rho
with nogil:
for k in xrange(gw, self.Gr.nzg-gw):
sa = eos(self.t_to_prog_fp, self.prog_to_t_fp,
self.Ref.p0_half[k], EnvVar.QT.values[k],
EnvVar.H.values[k]
)
EnvVar.T.values[k] = sa.T
EnvVar.QL.values[k] = sa.ql
rho = rho_c(self.Ref.p0_half[k], EnvVar.T.values[k],
EnvVar.QT.values[k],
EnvVar.QT.values[k] - EnvVar.QL.values[k]
)
EnvVar.B.values[k] = buoyancy_c(self.Ref.rho0_half[k], rho)
self.update_cloud_dry(k, EnvVar,
EnvVar.T.values[k], EnvVar.THL.values[k],
EnvVar.QT.values[k], EnvVar.QL.values[k],
EnvVar.QT.values[k] - EnvVar.QL.values[k]
)
return
cdef void sgs_mean(self, EnvironmentVariables EnvVar, RainVariables Rain, double dt):
cdef:
Py_ssize_t k
Py_ssize_t gw = self.Gr.gw
eos_struct sa
mph_struct mph
if EnvVar.H.name != 'thetal':
sys.exit('EDMF_Environment: rain source terms are defined for thetal as model variable')
with nogil:
for k in xrange(gw,self.Gr.nzg-gw):
# condensation
sa = eos(
self.t_to_prog_fp, self.prog_to_t_fp, self.Ref.p0_half[k],
EnvVar.QT.values[k], EnvVar.H.values[k]
)
# autoconversion and accretion
mph = microphysics_rain_src(
Rain.rain_model,
EnvVar.QT.values[k],
sa.ql,
Rain.Env_QR.values[k],
EnvVar.Area.values[k],
sa.T,
self.Ref.p0_half[k],
self.Ref.rho0_half[k],
dt
)
self.update_EnvVar(k, EnvVar, sa.T, mph.thl, mph.qt, mph.ql, mph.rho)
self.update_cloud_dry(k, EnvVar, sa.T, mph.th, mph.qt, mph.ql, mph.qv)
self.update_EnvRain_sources(k, EnvVar, mph.qr_src, mph.thl_rain_src)
return
cdef void sgs_quadrature(self, EnvironmentVariables EnvVar, RainVariables Rain, double dt):
a, w = np.polynomial.hermite.hermgauss(self.quadrature_order)
#TODO - remember you output source terms multipierd by dt (bec. of instanteneous autoconcv)
#TODO - add tendencies for GMV H, QT and QR due to rain
#TODO - if we start using eos_smpl for the updrafts calculations
# we can get rid of the two categories for outer and inner quad. points
cdef:
Py_ssize_t gw = self.Gr.gw
Py_ssize_t k, m_q, m_h
double [:] abscissas = a
double [:] weights = w
# arrays for storing quadarature points and ints for labeling items in the arrays
# a python dict would be nicer, but its 30% slower than this (for python 2.7. It might not be the case for python 3)
double[:] inner_env, outer_env, inner_src, outer_src
int i_ql, i_T, i_thl, i_rho, i_cf, i_qt_cld, i_qt_dry, i_T_cld, i_T_dry, i_rf
int i_SH_qt, i_Sqt_H, i_SH_H, i_Sqt_qt, i_Sqt, i_SH
int env_len = 10
int src_len = 6
double h_hat, qt_hat, sd_h, sd_q, corr, mu_h_star, sigma_h_star, qt_var, sd2_hq, sd_cond_h_q
double sqpi_inv = 1.0/sqrt(pi)
double sqrt2 = sqrt(2.0)
double sd_q_lim
double epsilon
eos_struct sa
mph_struct mph
epsilon = 10e-14 #np.finfo(np.float).eps
if EnvVar.H.name != 'thetal':
sys.exit('EDMF_Environment: rain source terms are only defined for thetal as model variable')
# initialize the quadrature points and their labels
inner_env = np.zeros(env_len, dtype=np.double, order='c')
outer_env = np.zeros(env_len, dtype=np.double, order='c')
inner_src = np.zeros(src_len, dtype=np.double, order='c')
outer_src = np.zeros(src_len, dtype=np.double, order='c')
i_ql, i_T, i_thl, i_rho, i_cf, i_qt_cld, i_qt_dry, i_T_cld, i_T_dry, i_rf = range(env_len)
i_SH_qt, i_Sqt_H, i_SH_H, i_Sqt_qt, i_Sqt, i_SH = range(src_len)
for k in xrange(gw, self.Gr.nzg-gw):
if (EnvVar.QTvar.values[k] > epsilon and EnvVar.Hvar.values[k] > epsilon and fabs(EnvVar.HQTcov.values[k]) > epsilon
and EnvVar.QT.values[k] > epsilon and sqrt(EnvVar.QTvar.values[k]) < EnvVar.QT.values[k]) :
if self.quadrature_type == 'log-normal':
# Lognormal parameters (mu, sd) from mean and variance
sd_q = sqrt(log(EnvVar.QTvar.values[k]/EnvVar.QT.values[k]/EnvVar.QT.values[k] + 1.0))
sd_h = sqrt(log(EnvVar.Hvar.values[k]/EnvVar.H.values[k]/EnvVar.H.values[k] + 1.0))
# Enforce Schwarz's inequality
corr = fmax(fmin(EnvVar.HQTcov.values[k]/sqrt(EnvVar.Hvar.values[k]*EnvVar.QTvar.values[k]),1.0),-1.0)
sd2_hq = log(corr*sqrt(EnvVar.Hvar.values[k]*EnvVar.QTvar.values[k])
/EnvVar.H.values[k]/EnvVar.QT.values[k] + 1.0)
sd_cond_h_q = sqrt(fmax(sd_h*sd_h - sd2_hq*sd2_hq/sd_q/sd_q, 0.0))
mu_q = log(EnvVar.QT.values[k]*EnvVar.QT.values[k]/sqrt(
EnvVar.QT.values[k]*EnvVar.QT.values[k] + EnvVar.QTvar.values[k]))
mu_h = log(EnvVar.H.values[k]*EnvVar.H.values[k]/sqrt(
EnvVar.H.values[k]*EnvVar.H.values[k] + EnvVar.Hvar.values[k]))
else:
sd_q = sqrt(EnvVar.QTvar.values[k])
sd_h = sqrt(EnvVar.Hvar.values[k])
corr = fmax(fmin(EnvVar.HQTcov.values[k]/fmax(sd_h*sd_q, 1e-13),1.0),-1.0)
# limit sd_q to prevent negative qt_hat
sd_q_lim = (1e-10 - EnvVar.QT.values[k])/(sqrt2 * abscissas[0])
# walking backwards to assure your q_t will not be smaller than 1e-10
# TODO - check
# TODO - change 1e-13 and 1e-10 to some epislon
sd_q = fmin(sd_q, sd_q_lim)
qt_var = sd_q * sd_q
sigma_h_star = sqrt(fmax(1.0-corr*corr,0.0)) * sd_h
# zero outer quadrature points
for idx in range(env_len):
outer_env[idx] = 0.0
for idx in range(src_len):
outer_src[idx] = 0.0
for m_q in xrange(self.quadrature_order):
if self.quadrature_type == 'log-normal':
qt_hat = exp(mu_q + sqrt2 * sd_q * abscissas[m_q])
mu_h_star = mu_h + sd2_hq/sd_q/sd_q*(log(qt_hat)-mu_q)
else:
qt_hat = EnvVar.QT.values[k] + sqrt2 * sd_q * abscissas[m_q]
mu_h_star = EnvVar.H.values[k] + sqrt2 * corr * sd_h * abscissas[m_q]
# zero inner quadrature points
for idx in range(env_len):
inner_env[idx] = 0.0
for idx in range(src_len):
inner_src[idx] = 0.0
for m_h in xrange(self.quadrature_order):
if self.quadrature_type == 'log-normal':
h_hat = exp(mu_h_star + sqrt2 * sd_cond_h_q * abscissas[m_h])
else:
h_hat = sqrt2 * sigma_h_star * abscissas[m_h] + mu_h_star
with nogil:
# condensation
sa = eos(
self.t_to_prog_fp, self.prog_to_t_fp,
self.Ref.p0_half[k], qt_hat, h_hat
)
# autoconversion and accretion
mph = microphysics_rain_src(
Rain.rain_model,
qt_hat,
sa.ql,
Rain.Env_QR.values[k],
EnvVar.Area.values[k],
sa.T,
self.Ref.p0_half[k],
self.Ref.rho0_half[k],
dt
)
# environmental variables
inner_env[i_ql] += mph.ql * weights[m_h] * sqpi_inv
inner_env[i_T] += sa.T * weights[m_h] * sqpi_inv
inner_env[i_thl] += mph.thl * weights[m_h] * sqpi_inv
inner_env[i_rho] += mph.rho * weights[m_h] * sqpi_inv
# rain area fraction
if mph.qr_src > 0.0:
inner_env[i_rf] += weights[m_h] * sqpi_inv
# cloudy/dry categories for buoyancy in TKE
if mph.ql > 0.0:
inner_env[i_cf] += weights[m_h] * sqpi_inv
inner_env[i_qt_cld] += mph.qt * weights[m_h] * sqpi_inv
inner_env[i_T_cld] += sa.T * weights[m_h] * sqpi_inv
else:
inner_env[i_qt_dry] += mph.qt * weights[m_h] * sqpi_inv
inner_env[i_T_dry] += sa.T * weights[m_h] * sqpi_inv
# products for variance and covariance source terms
inner_src[i_Sqt] += -mph.qr_src * weights[m_h] * sqpi_inv
inner_src[i_SH] += mph.thl_rain_src * weights[m_h] * sqpi_inv
inner_src[i_Sqt_H] += -mph.qr_src * mph.thl * weights[m_h] * sqpi_inv
inner_src[i_Sqt_qt] += -mph.qr_src * mph.qt * weights[m_h] * sqpi_inv
inner_src[i_SH_H] += mph.thl_rain_src * mph.thl * weights[m_h] * sqpi_inv
inner_src[i_SH_qt] += mph.thl_rain_src * mph.qt * weights[m_h] * sqpi_inv
for idx in range(env_len):
outer_env[idx] += inner_env[idx] * weights[m_q] * sqpi_inv
for idx in range(src_len):
outer_src[idx] += inner_src[idx] * weights[m_q] * sqpi_inv
# update environmental variables
self.update_EnvVar(k, EnvVar, outer_env[i_T], outer_env[i_thl],\
outer_env[i_qt_cld] + outer_env[i_qt_dry],\
outer_env[i_ql], outer_env[i_rho])
self.update_EnvRain_sources(k, EnvVar, -outer_src[i_Sqt], outer_src[i_SH])
# update cloudy/dry variables for buoyancy in TKE
EnvVar.cloud_fraction.values[k] = outer_env[i_cf]
self.qt_dry[k] = outer_env[i_qt_dry]
self.th_dry[k] = theta_c(self.Ref.p0_half[k], outer_env[i_T_dry])
self.t_cloudy[k] = outer_env[i_T_cld]
self.qv_cloudy[k] = outer_env[i_qt_cld] - outer_env[i_ql]
self.qt_cloudy[k] = outer_env[i_qt_cld]
self.th_cloudy[k] = theta_c(self.Ref.p0_half[k], outer_env[i_T_cld])
# update var/covar rain sources
self.Hvar_rain_dt[k] = outer_src[i_SH_H] - outer_src[i_SH] * EnvVar.H.values[k]
self.QTvar_rain_dt[k] = outer_src[i_Sqt_qt] - outer_src[i_Sqt] * EnvVar.QT.values[k]
self.HQTcov_rain_dt[k] = outer_src[i_SH_qt] - outer_src[i_SH] * EnvVar.QT.values[k] + \
outer_src[i_Sqt_H] - outer_src[i_Sqt] * EnvVar.H.values[k]
else:
# if variance and covariance are zero do the same as in SA_mean
sa = eos(
self.t_to_prog_fp, self.prog_to_t_fp,
self.Ref.p0_half[k], EnvVar.QT.values[k],
EnvVar.H.values[k]
)
mph = microphysics_rain_src(
Rain.rain_model,
EnvVar.QT.values[k],
sa.ql,
Rain.Env_QR.values[k],
EnvVar.Area.values[k],
sa.T,
self.Ref.p0_half[k],
self.Ref.rho0_half[k],
dt
)
self.update_EnvVar(k, EnvVar, sa.T, mph.thl, mph.qt, mph.ql, mph.rho)
self.update_EnvRain_sources(k, EnvVar, mph.qr_src, mph.thl_rain_src)
self.update_cloud_dry(k, EnvVar, sa.T, mph.th, mph.qt, mph.ql, mph.qv)
self.Hvar_rain_dt[k] = 0.
self.QTvar_rain_dt[k] = 0.
self.HQTcov_rain_dt[k] = 0.
return
cpdef microphysics(self, EnvironmentVariables EnvVar, RainVariables Rain, double dt):
if EnvVar.EnvThermo_scheme == 'mean':
self.sgs_mean(EnvVar, Rain, dt)
elif EnvVar.EnvThermo_scheme == 'quadrature':
self.sgs_quadrature(EnvVar, Rain, dt)
else:
sys.exit('EDMF_Environment: Unrecognized EnvThermo_scheme. Possible options: mean, quadrature')
return