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ThermodynamicsSA.pyx
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ThermodynamicsSA.pyx
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#!python
# cython: boundscheck=False
# cython: wraparound=False
# cython: initializedcheck=False
# cython: cdivision=True
cimport numpy as np
import numpy as np
cimport Lookup
cimport ParallelMPI
cimport Grid
cimport ReferenceState
cimport DiagnosticVariables
cimport PrognosticVariables
from Thermodynamics cimport LatentHeat, ClausiusClapeyron
from thermodynamic_functions cimport thetas_c, theta_c, thetali_c
import cython
from NetCDFIO cimport NetCDFIO_Stats, NetCDFIO_Fields
from libc.math cimport fmax, fmin
cdef extern from "thermodynamics_sa.h":
inline double alpha_c(double p0, double T, double qt, double qv) nogil
void eos_c(Lookup.LookupStruct *LT, double(*lam_fp)(double), double(*L_fp)(double, double), double p0, double s, double qt, double *T, double *qv, double *ql, double *qi) nogil
void eos_update(Grid.DimStruct *dims, Lookup.LookupStruct *LT, double(*lam_fp)(double), double(*L_fp)(double, double), double *p0, double *s, double *qt, double *T,
double * qv, double * ql, double * qi, double * alpha)
void buoyancy_update_sa(Grid.DimStruct *dims, double *alpha0, double *alpha, double *buoyancy, double *wt)
void bvf_sa(Grid.DimStruct * dims, Lookup.LookupStruct * LT, double(*lam_fp)(double), double(*L_fp)(double, double), double *p0, double *T, double *qt, double *qv, double *theta_rho, double *bvf)
void thetali_update(Grid.DimStruct *dims, double (*lam_fp)(double), double (*L_fp)(double, double), double *p0, double *T, double *qt, double *ql, double *qi, double *thetali)
void clip_qt(Grid.DimStruct *dims, double *qt, double clip_value)
void compute_s(Grid.DimStruct *dims, Lookup.LookupStruct * LT, double (*lam_fp)(double), double (*L_fp)(double, double), double *p0, double *T, double *qt, double *ql, double *qi, double *s)
cdef extern from "thermodynamic_functions.h":
# Dry air partial pressure
inline double pd_c(double p0, double qt, double qv) nogil
# Water vapor partial pressure
inline double pv_c(double p0, double qt, double qv) nogil
cdef extern from "entropies.h":
# Specific entropy of dry air
inline double sd_c(double pd, double T) nogil
# Specific entropy of water vapor
inline double sv_c(double pv, double T) nogil
# Specific entropy of condensed water
inline double sc_c(double L, double T) nogil
cdef class ThermodynamicsSA:
def __init__(self, dict namelist, LatentHeat LH, ParallelMPI.ParallelMPI Par):
'''
Init method saturation adjsutment thermodynamics.
:param namelist: dictionary
:param LH: LatentHeat class instance
:param Par: ParallelMPI class instance
:return:
'''
self.L_fp = LH.L_fp
self.Lambda_fp = LH.Lambda_fp
self.CC = ClausiusClapeyron()
self.CC.initialize(namelist, LH, Par)
#Check to see if qt clipping is to be done. By default qt_clipping is on.
try:
self.do_qt_clipping = namelist['thermodynamics']['do_qt_clipping']
except:
self.do_qt_clipping = True
self.s_prognostic = True
return
cpdef initialize(self, Grid.Grid Gr, PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
'''
Initialize ThermodynamicsSA class. Adds variables to PrognocitVariables and DiagnosticVariables classes. Add
output fields to NetCDFIO_Stats.
:param Gr: Grid class instance
:param PV: PrognosticVariables class instance
:param DV: DiagnsoticVariables class instance
:param NS: NetCDFIO_Stats class instance
:param Pa: ParallelMPI class instance
:return:
'''
PV.add_variable('s', 'J kg^-1 K^-1', 's', 'specific entropy', "sym", "scalar", Pa)
PV.add_variable('qt', 'kg/kg', 'q_t', 'total water specific humidity', "sym", "scalar", Pa)
# Initialize class member arrays
DV.add_variables('buoyancy' ,r'ms^{-1}', r'b', 'buoyancy','sym', Pa)
DV.add_variables('alpha', r'm^3kg^-2', r'\alpha', 'specific volume', 'sym', Pa)
DV.add_variables('temperature', r'K', r'T', r'temperature', 'sym', Pa)
DV.add_variables('buoyancy_frequency', r's^-1', r'N', 'buoyancy frequencyt', 'sym', Pa)
DV.add_variables('qv', 'kg/kg', r'q_v', 'water vapor specific humidity', 'sym', Pa)
DV.add_variables('ql', 'kg/kg', r'q_l', 'liquid water specific humidity', 'sym', Pa)
DV.add_variables('qi', 'kg/kg', r'q_i', 'ice water specific humidity', 'sym', Pa)
DV.add_variables('theta_rho', 'K', r'\theta_{\rho}', 'density potential temperature', 'sym', Pa)
DV.add_variables('thetali', 'K', r'\theta_l', r'liqiud water potential temperature', 'sym', Pa)
# Add statistical output
NS.add_profile('thetas_mean', Gr, Pa)
NS.add_profile('thetas_mean2', Gr, Pa)
NS.add_profile('thetas_mean3', Gr, Pa)
NS.add_profile('thetas_max', Gr, Pa)
NS.add_profile('thetas_min', Gr, Pa)
NS.add_ts('thetas_max', Gr, Pa)
NS.add_ts('thetas_min', Gr, Pa)
NS.add_profile('theta_mean', Gr, Pa)
NS.add_profile('theta_mean2', Gr, Pa)
NS.add_profile('theta_mean3', Gr, Pa)
NS.add_profile('theta_max', Gr, Pa)
NS.add_profile('theta_min', Gr, Pa)
NS.add_ts('theta_max', Gr, Pa)
NS.add_ts('theta_min', Gr, Pa)
NS.add_profile('rh_mean', Gr, Pa)
# NS.add_profile('rh_mean2', Gr, Pa)
# NS.add_profile('rh_mean3', Gr, Pa)
NS.add_profile('rh_max', Gr, Pa)
NS.add_profile('rh_min', Gr, Pa)
# NS.add_ts('rh_max', Gr, Pa)
# NS.add_ts('rh_min', Gr, Pa)
NS.add_profile('cloud_fraction', Gr, Pa)
NS.add_profile('cloud_cum_fraction', Gr, Pa)
NS.add_ts('cloud_fraction', Gr, Pa)
NS.add_ts('cloud_mid_fraction', Gr, Pa)
NS.add_ts('cloud_threshold_fraction', Gr, Pa)
NS.add_ts('cloud_top', Gr, Pa)
NS.add_ts('cloud_base', Gr, Pa)
NS.add_ts('lwp', Gr, Pa)
NS.add_ts('iwp', Gr, Pa)
return
cpdef entropy(self, double p0, double T, double qt, double ql, double qi):
'''
Provide a python wrapper for the c function that computes the specific entropy
consistent with Pressel et al. 2015 equation (40)
:param p0: reference state pressure [Pa]
:param T: thermodynamic temperature [K]
:param qt: total water specific humidity [kg/kg]
:param ql: liquid water specific humidity [kg/kg]
:param qi: ice water specific humidity [kg/kg]
:return: moist specific entropy
'''
cdef:
double qv = qt - ql - qi
double qd = 1.0 - qt
double pd = pd_c(p0, qt, qv)
double pv = pv_c(p0, qt, qv)
double Lambda = self.Lambda_fp(T)
double L = self.L_fp(T, Lambda)
return sd_c(pd, T) * qd + sv_c(pv, T) * qt + sc_c(L, T) * (ql + qi)
cpdef alpha(self, double p0, double T, double qt, double qv):
'''
Provide a python wrapper for the C function that computes the specific volume
consistent with Pressel et al. 2015 equation (44).
:param p0: reference state pressure [Pa]
:param T: thermodynamic temperature [K]
:param qt: total water specific humidity [kg/kg]
:param qv: water vapor specific humidity [kg/kg]
:return: specific volume [m^3/kg]
'''
return alpha_c(p0, T, qt, qv)
cpdef eos(self, double p0, double s, double qt):
cdef:
double T, qv, qc, ql, qi, lam
eos_c(&self.CC.LT.LookupStructC, self.Lambda_fp, self.L_fp, p0, s, qt, &T, &qv, &ql, &qi)
return T, ql, qi
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState RS,
PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV):
# Get relevant variables shifts
cdef:
Py_ssize_t buoyancy_shift = DV.get_varshift(Gr, 'buoyancy')
Py_ssize_t alpha_shift = DV.get_varshift(Gr, 'alpha')
Py_ssize_t t_shift = DV.get_varshift(Gr, 'temperature')
Py_ssize_t ql_shift = DV.get_varshift(Gr, 'ql')
Py_ssize_t qi_shift = DV.get_varshift(Gr, 'qi')
Py_ssize_t qv_shift = DV.get_varshift(Gr, 'qv')
Py_ssize_t qc_shift
Py_ssize_t s_shift
Py_ssize_t qs_shift
Py_ssize_t qr_shift
Py_ssize_t qt_shift = PV.get_varshift(Gr, 'qt')
Py_ssize_t w_shift = PV.get_varshift(Gr, 'w')
Py_ssize_t bvf_shift = DV.get_varshift(Gr, 'buoyancy_frequency')
Py_ssize_t thr_shift = DV.get_varshift(Gr, 'theta_rho')
Py_ssize_t thl_shift = DV.get_varshift(Gr, 'thetali')
'''Apply qt clipping if requested. Defaults to on. Call this before other thermodynamic routines. Note that this
changes the values in the qt array directly. Perhaps we should eventually move this to the timestepping function
so that the output statistics correctly reflect clipping.
'''
if self.do_qt_clipping:
clip_qt(&Gr.dims, &PV.values[qt_shift], 1e-11)
s_shift = PV.get_varshift(Gr, 's')
eos_update(&Gr.dims, &self.CC.LT.LookupStructC, self.Lambda_fp, self.L_fp, &RS.p0_half[0],
&PV.values[s_shift], &PV.values[qt_shift], &DV.values[t_shift], &DV.values[qv_shift], &DV.values[ql_shift],
&DV.values[qi_shift], &DV.values[alpha_shift])
buoyancy_update_sa(&Gr.dims, &RS.alpha0_half[0], &DV.values[alpha_shift], &DV.values[buoyancy_shift], &PV.tendencies[w_shift])
bvf_sa(&Gr.dims, &self.CC.LT.LookupStructC, self.Lambda_fp, self.L_fp, &RS.p0_half[0], &DV.values[t_shift], &PV.values[qt_shift], &DV.values[qv_shift], &DV.values[thr_shift], &DV.values[bvf_shift])
thetali_update(&Gr.dims,self.Lambda_fp, self.L_fp, &RS.p0_half[0], &DV.values[t_shift], &PV.values[qt_shift], &DV.values[ql_shift], &DV.values[qi_shift], &DV.values[thl_shift])
return
cpdef get_pv_star(self, t):
return self.CC.LT.fast_lookup(t)
cpdef get_lh(self, t):
cdef double lam = self.Lambda_fp(t)
return self.L_fp(t, lam)
cpdef write_fields(self, Grid.Grid Gr, ReferenceState.ReferenceState RS,
PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV, NetCDFIO_Fields NF, ParallelMPI.ParallelMPI Pa):
cdef:
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 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 count
Py_ssize_t s_shift = PV.get_varshift(Gr, 's')
Py_ssize_t qt_shift = PV.get_varshift(Gr, 'qt')
double[:] data = np.empty((Gr.dims.npl,), dtype=np.double, order='c')
# Add entropy potential temperature to 3d fields
with nogil:
count = 0
for i in range(imin, imax):
ishift = i * istride
for j in range(jmin, jmax):
jshift = j * jstride
for k in range(kmin, kmax):
ijk = ishift + jshift + k
data[count] = thetas_c(PV.values[s_shift + ijk], PV.values[qt_shift + ijk])
count += 1
NF.add_field('thetas')
NF.write_field('thetas', data)
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 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 imin = 0
Py_ssize_t jmin = 0
Py_ssize_t kmin = 0
Py_ssize_t imax = Gr.dims.nlg[0]
Py_ssize_t jmax = Gr.dims.nlg[1]
Py_ssize_t kmax = Gr.dims.nlg[2]
Py_ssize_t count
Py_ssize_t s_shift
Py_ssize_t qt_shift = PV.get_varshift(Gr, 'qt')
double[:] data = np.empty((Gr.dims.npg,), dtype=np.double, order='c')
double[:] tmp
#If entropy is not a prognostic variable get it from the diagnostic variable class
s_shift = PV.get_varshift(Gr, 's')
# Ouput profiles of thetas
with nogil:
count = 0
for i in range(imin, imax):
ishift = i * istride
for j in range(jmin, jmax):
jshift = j * jstride
for k in range(kmin, kmax):
ijk = ishift + jshift + k
data[count] = thetas_c(PV.values[s_shift + ijk], PV.values[qt_shift + ijk])
count += 1
# Compute and write mean
tmp = Pa.HorizontalMean(Gr, &data[0])
NS.write_profile('thetas_mean', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
# Compute and write mean of squres
tmp = Pa.HorizontalMeanofSquares(Gr, &data[0], &data[0])
NS.write_profile('thetas_mean2', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
# Compute and write mean of cubes
tmp = Pa.HorizontalMeanofCubes(Gr, &data[0], &data[0], &data[0])
NS.write_profile('thetas_mean3', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
# Compute and write maxes
tmp = Pa.HorizontalMaximum(Gr, &data[0])
NS.write_profile('thetas_max', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
NS.write_ts('thetas_max', np.amax(tmp[Gr.dims.gw:-Gr.dims.gw]), Pa)
# Compute and write mins
tmp = Pa.HorizontalMinimum(Gr, &data[0])
NS.write_profile('thetas_min', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
NS.write_ts('thetas_min', np.amin(tmp[Gr.dims.gw:-Gr.dims.gw]), Pa)
#Output profiles of theta (dry potential temperature)
cdef:
Py_ssize_t t_shift = DV.get_varshift(Gr, 'temperature')
with nogil:
count = 0
for i in range(imin, imax):
ishift = i * istride
for j in range(jmin, jmax):
jshift = j * jstride
for k in range(kmin, kmax):
ijk = ishift + jshift + k
data[count] = theta_c(RS.p0_half[k], DV.values[t_shift + ijk])
count += 1
# Compute and write mean
tmp = Pa.HorizontalMean(Gr, &data[0])
NS.write_profile('theta_mean', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
# Compute and write mean of squres
tmp = Pa.HorizontalMeanofSquares(Gr, &data[0], &data[0])
NS.write_profile('theta_mean2', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
# Compute and write mean of cubes
tmp = Pa.HorizontalMeanofCubes(Gr, &data[0], &data[0], &data[0])
NS.write_profile('theta_mean3', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
# Compute and write maxes
tmp = Pa.HorizontalMaximum(Gr, &data[0])
NS.write_profile('theta_max', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
NS.write_ts('theta_max', np.amax(tmp[Gr.dims.gw:-Gr.dims.gw]), Pa)
# Compute and write mins
tmp = Pa.HorizontalMinimum(Gr, &data[0])
NS.write_profile('theta_min', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
NS.write_ts('theta_min', np.amin(tmp[Gr.dims.gw:-Gr.dims.gw]), Pa)
cdef:
Py_ssize_t qv_shift = DV.get_varshift(Gr,'qv')
double pv_star, pv
# Ouput profiles of relative humidity
with nogil:
count = 0
for i in range(imin, imax):
ishift = i * istride
for j in range(jmin, jmax):
jshift = j * jstride
for k in range(kmin, kmax):
ijk = ishift + jshift + k
pv_star = self.CC.LT.fast_lookup(DV.values[t_shift + ijk])
pv = pv_c(RS.p0_half[k], PV.values[qt_shift+ijk], DV.values[qv_shift+ijk])
data[count] = pv/pv_star
count += 1
# Compute and write mean
tmp = Pa.HorizontalMean(Gr, &data[0])
NS.write_profile('rh_mean', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
# Compute and write mean of squres
# tmp = Pa.HorizontalMeanofSquares(Gr, &data[0], &data[0])
# NS.write_profile('rh_mean2', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
#
# # Compute and write mean of cubes
# tmp = Pa.HorizontalMeanofCubes(Gr, &data[0], &data[0], &data[0])
# NS.write_profile('rh_mean3', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
# Compute and write maxes
tmp = Pa.HorizontalMaximum(Gr, &data[0])
NS.write_profile('rh_max', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
# NS.write_ts('rh_max', np.amax(tmp[Gr.dims.gw:-Gr.dims.gw]), Pa)
# Compute and write mins
tmp = Pa.HorizontalMinimum(Gr, &data[0])
NS.write_profile('rh_min', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
# NS.write_ts('rh_min', np.amin(tmp[Gr.dims.gw:-Gr.dims.gw]), Pa)
#Output profiles of thetali (liquid-ice potential temperature)
# Compute additional stats
self.liquid_stats(Gr, RS, PV, DV, NS, Pa)
return
cpdef liquid_stats(self, Grid.Grid Gr, ReferenceState.ReferenceState RS, PrognosticVariables.PrognosticVariables PV,
DiagnosticVariables.DiagnosticVariables DV, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
cdef:
Py_ssize_t kmin = 0
Py_ssize_t kmax = Gr.dims.n[2]
Py_ssize_t gw = Gr.dims.gw
Py_ssize_t pi, k
ParallelMPI.Pencil z_pencil = ParallelMPI.Pencil()
Py_ssize_t ql_shift = DV.get_varshift(Gr, 'ql')
Py_ssize_t qi_shift = DV.get_varshift(Gr, 'qi')
double[:, :] ql_pencils
double[:, :] qi_pencils
# Cloud indicator
double[:] ci
double[:] ci_threshold
double[:] ci_mid
double[:] ci_cum
double cb
double ct
# Weighted sum of local cloud indicator
double ci_weighted_sum = 0.0
double ci_threshold_weighted_sum = 0.0
double ci_mid_weighted_sum = 0.0
double ci_cum_weighted_sum = 0.0
double mean_divisor = np.double(Gr.dims.n[0] * Gr.dims.n[1])
double dz = Gr.dims.dx[2]
double[:] lwp
double[:] iwp
double lwp_weighted_sum = 0.0
double iwp_weighted_sum = 0.0
double[:] cf_profile = np.zeros((Gr.dims.n[2]), dtype=np.double, order='c')
double[:] cf_cum_profile = np.zeros((Gr.dims.n[2]), dtype=np.double, order='c')
# Initialize the z-pencil
z_pencil.initialize(Gr, Pa, 2)
ql_pencils = z_pencil.forward_double( &Gr.dims, Pa, &DV.values[ql_shift])
qi_pencils = z_pencil.forward_double( &Gr.dims, Pa, &DV.values[qi_shift])
# Compute cloud fraction profile
with nogil:
for pi in xrange(z_pencil.n_local_pencils):
for k in xrange(kmin, kmax):
if ql_pencils[pi, k] + qi_pencils[pi, k] > 0.0:
cf_profile[k] += 1.0 / mean_divisor
cf_profile = Pa.domain_vector_sum(cf_profile, Gr.dims.n[2])
NS.write_profile('cloud_fraction', cf_profile, Pa)
# Compute all or nothing cloud fraction
ci = np.empty((z_pencil.n_local_pencils), dtype=np.double, order='c')
with nogil:
for pi in xrange(z_pencil.n_local_pencils):
for k in xrange(kmin, kmax):
if ql_pencils[pi, k] + qi_pencils[pi, k] > 0.0:
ci[pi] = 1.0
break
else:
ci[pi] = 0.0
for pi in xrange(z_pencil.n_local_pencils):
ci_weighted_sum += ci[pi]
ci_weighted_sum /= mean_divisor
ci_weighted_sum = Pa.domain_scalar_sum(ci_weighted_sum)
NS.write_ts('cloud_fraction', ci_weighted_sum, Pa)
# 062119[ZS]: Compute all or nothing cloud fraction for qc > 1.e-5
ci_threshold = np.empty((z_pencil.n_local_pencils), dtype=np.double, order='c')
with nogil:
for pi in xrange(z_pencil.n_local_pencils):
for k in xrange(kmin, kmax):
if (ql_pencils[pi, k] + qi_pencils[pi, k] > 1.0e-5):
ci_threshold[pi] = 1.0
break
else:
ci_threshold[pi] = 0.0
for pi in xrange(z_pencil.n_local_pencils):
ci_threshold_weighted_sum += ci_threshold[pi]
ci_threshold_weighted_sum /= mean_divisor
ci_threshold_weighted_sum = Pa.domain_scalar_sum(ci_threshold_weighted_sum)
NS.write_ts('cloud_threshold_fraction', ci_threshold_weighted_sum, Pa)
# 062019[ZS]: Compute all or nothing cloud fraction below 8km
ci_mid = np.empty((z_pencil.n_local_pencils), dtype=np.double, order='c')
with nogil:
for pi in xrange(z_pencil.n_local_pencils):
for k in xrange(kmin, kmax):
if (ql_pencils[pi, k] + qi_pencils[pi, k] > 0.0) and (Gr.zp_half[gw + k]<=8000.0):
ci_mid[pi] = 1.0
break
else:
ci_mid[pi] = 0.0
for pi in xrange(z_pencil.n_local_pencils):
ci_mid_weighted_sum += ci_mid[pi]
ci_mid_weighted_sum /= mean_divisor
ci_mid_weighted_sum = Pa.domain_scalar_sum(ci_mid_weighted_sum)
NS.write_ts('cloud_mid_fraction', ci_mid_weighted_sum, Pa)
# 062019[ZS]: Compute cumulative cloud fraction profile
ci_cum = np.empty((z_pencil.n_local_pencils), dtype=np.double, order='c')
ci_cum[:] = 0.0
with nogil:
for k in xrange(kmin, kmax):
for pi in xrange(z_pencil.n_local_pencils):
if (ql_pencils[pi, k] + qi_pencils[pi, k] > 0.0):
ci_cum[pi] = 1.0
for pi in xrange(z_pencil.n_local_pencils):
ci_cum_weighted_sum += ci_cum[pi]
ci_cum_weighted_sum /= mean_divisor
cf_cum_profile[k] = ci_cum_weighted_sum
cf_cum_profile = Pa.domain_vector_sum(cf_cum_profile, Gr.dims.n[2])
NS.write_profile('cloud_cum_fraction', cf_cum_profile, Pa)
# Compute cloud top and cloud base height
cb = 99999.9
ct = -99999.9
with nogil:
for pi in xrange(z_pencil.n_local_pencils):
for k in xrange(kmin, kmax):
if ql_pencils[pi, k] + qi_pencils[pi, k] > 0.0:
cb = fmin(cb, Gr.zp_half[gw + k])
ct = fmax(ct, Gr.zp_half[gw + k])
cb = Pa.domain_scalar_min(cb)
ct = Pa.domain_scalar_max(ct)
NS.write_ts('cloud_base', cb, Pa)
NS.write_ts('cloud_top', ct, Pa)
# Compute liquid water path
lwp = np.empty((z_pencil.n_local_pencils), dtype=np.double, order='c')
iwp = np.empty((z_pencil.n_local_pencils), dtype=np.double, order='c')
with nogil:
for pi in xrange(z_pencil.n_local_pencils):
lwp[pi] = 0.0
iwp[pi] = 0.0
for k in xrange(kmin, kmax):
lwp[pi] += RS.rho0_half[k] * ql_pencils[pi, k] * dz * Gr.dims.met_half[k]
iwp[pi] += RS.rho0_half[k] * qi_pencils[pi, k] * dz * Gr.dims.met_half[k]
for pi in xrange(z_pencil.n_local_pencils):
lwp_weighted_sum += lwp[pi]
iwp_weighted_sum += iwp[pi]
lwp_weighted_sum /= mean_divisor
iwp_weighted_sum /= mean_divisor
lwp_weighted_sum = Pa.domain_scalar_sum(lwp_weighted_sum)
iwp_weighted_sum = Pa.domain_scalar_sum(iwp_weighted_sum)
NS.write_ts('lwp', lwp_weighted_sum, Pa)
NS.write_ts('iwp', iwp_weighted_sum, Pa)
return