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Tracers.pyx
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Tracers.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
cimport ParallelMPI
cimport TimeStepping
from NetCDFIO cimport NetCDFIO_Stats
import cython
from libc.math cimport fmax, fmin, sqrt, copysign
cimport numpy as np
import numpy as np
include "parameters.pxi"
import cython
cdef extern from "thermodynamic_functions.h":
inline double pv_c(double p0, double qt, double qv) nogil
def TracersFactory(namelist):
try:
use_tracers = namelist['tracers']['use_tracers']
except:
use_tracers = False
if use_tracers:
try:
tracer_scheme = namelist['tracers']['scheme']
if tracer_scheme == 'UpdraftTracers':
return UpdraftTracers(namelist)
elif tracer_scheme == 'PurityTracers':
return PurityTracers(namelist)
else:
print('Tracer scheme is not recognized, using TracersNone')
return TracersFactory()
except:
tracer_scheme = 'UpdraftTracers'
return UpdraftTracers(namelist)
else:
return TracersNone()
cdef class TracersNone:
def __init__(self):
return
cpdef initialize(self, Grid.Grid Gr, PrognosticVariables.PrognosticVariables PV,
DiagnosticVariables.DiagnosticVariables DV, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref, PrognosticVariables.PrognosticVariables PV,
DiagnosticVariables.DiagnosticVariables DV, TimeStepping.TimeStepping TS, ParallelMPI.ParallelMPI Pa):
return
cpdef update_cleanup(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref, PrognosticVariables.PrognosticVariables PV,
DiagnosticVariables.DiagnosticVariables DV,ParallelMPI.ParallelMPI Pa, TimeStepping.TimeStepping TS):
return
cpdef stats_io(self, Grid.Grid Gr, PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV,
TimeStepping.TimeStepping TS, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
return
cdef class UpdraftTracers:
def __init__(self, namelist):
try:
self.lcl_tracers = namelist['tracers']['use_lcl_tracers']
except:
self.lcl_tracers = False
self.index_lcl = 0
return
cpdef initialize(self, Grid.Grid Gr, PrognosticVariables.PrognosticVariables PV,
DiagnosticVariables.DiagnosticVariables DV, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
self.updraft_indicator = np.zeros((Gr.dims.npg),dtype=np.double, order='c')
# Assemble a dictionary with the tracer information
# Can be expanded for different init heights or timescales
self.tracer_dict = {}
self.tracer_dict['surface'] = {}
self.tracer_dict['surface']['c_srf_15'] = {}
self.tracer_dict['surface']['c_srf_15']['timescale'] = 15.0 * 60.0
if self.lcl_tracers:
self.tracer_dict['lcl'] = {}
self.tracer_dict['lcl']['c_lcl_15'] = {}
self.tracer_dict['lcl']['c_lcl_15']['timescale'] = 15.0 * 60.0
for var in self.tracer_dict['surface'].keys():
PV.add_variable(var, '-', var, 'tracer diagnostics' , "sym", "scalar", Pa)
if self.lcl_tracers:
for var in self.tracer_dict['lcl'].keys():
PV.add_variable(var, '-', var, 'tracer diagnostics', "sym", "scalar", Pa)
NS.add_ts('grid_lcl', Gr, Pa )
NS.add_profile('updraft_fraction', Gr, Pa, units=r'-',nice_name=r'a_u',
desc=r'updraft area fraction' )
NS.add_profile('updraft_w', Gr, Pa, units=r'm s^{-1}', nice_name=r'w_u',
desc=r'updraft vertical velocity at cell centers')
NS.add_profile('updraft_w2', Gr, Pa, units=r'm^{2} s^{-2}', nice_name=r'w^2_u',
desc=r'updraft vertical velocity square at cell centers')
NS.add_profile('updraft_u', Gr, Pa, units=r'm s^{-1}', nice_name=r'u_u',
desc=r'updraft u velocity')
NS.add_profile('updraft_u2', Gr, Pa, units=r'm^{2} s^{-2}', nice_name=r'u^2_u',
desc=r'updraft u velocity square at cell centers')
NS.add_profile('updraft_v', Gr, Pa, units=r'm s^{-1}', nice_name=r'v_u',
desc=r'updraft v velocity')
NS.add_profile('updraft_v2', Gr, Pa, units=r'm^{2} s^{-2}', nice_name=r'v^2_u',
desc=r'updraft v velocity square at cell centers')
NS.add_profile('updraft_qt', Gr, Pa, units=r'kg kg^{-1}',nice_name=r'q_{t,u}',
desc=r'updraft total water specific humidity')
NS.add_profile('updraft_qt2', Gr, Pa, units=r'kg^{2} kg^{-2}',nice_name=r'q_{t,u}^2',
desc=r'updraft total water specific humidity square')
NS.add_profile('updraft_thetali', Gr, Pa, units=r'K', nice_name=r'\theta_{li,u}',
desc=r'updraft liquid ice potential temperature')
NS.add_profile('updraft_thetali2', Gr, Pa, units=r'K^2', nice_name=r'\theta_{li,u}^2',
desc=r'updraft liquid ice potential temperature square')
NS.add_profile('updraft_b', Gr, Pa, units=r'm s^{-2}', nice_name=r'b_{u}',
desc=r'updraft buoyancy (not anomaly)')
NS.add_profile('updraft_b2', Gr, Pa, units=r'm s^{-2}', nice_name=r'b_{u}^2',
desc=r'updraft buoyancy (not anomaly) square')
NS.add_profile('updraft_w_qt', Gr, Pa, units=r' m s^{-1} kg kg^{-1}', nice_name=r'(w q_t)_u',
desc=r'updraft product of w and q_t')
NS.add_profile('updraft_w_thetali', Gr, Pa, units=r' m s^{-1} K', nice_name=r'(w \theta_{li})_u',
desc=r'updraft product of w and \theta_{li}')
NS.add_profile('updraft_qt_thetali', Gr, Pa, units=r'kg kg^{-1} K', nice_name=r'(q_t \theta_{li})_u',
desc=r'updraft product of q_t and \theta_{li}')
NS.add_profile('updraft_bvf', Gr, Pa, units=r's^{-1}', nice_name=r'N_{b,u}',
desc=r'updraft buoyancy frequency')
NS.add_profile('updraft_thetarho', Gr, Pa, units=r'K', nice_name=r'\theta_{\rho,u}',
desc=r'updraft density potential temperature')
NS.add_profile('updraft_temperature', Gr, Pa, units=r'K', nice_name=r'T_{u}',
desc=r'updraft temperature')
NS.add_profile('updraft_temperature2', Gr, Pa, units=r'K^2', nice_name=r'T_{u}^2',
desc=r'updraft temperature square')
NS.add_profile('updraft_qv', Gr, Pa, units=r'kg kg^{-1}',nice_name=r'q_{v,u}',
desc=r'updraft vapor specific humidity')
NS.add_profile('updraft_qv2', Gr, Pa, units=r'kg^{2} kg^{-2}',nice_name=r'q_{v,u}^2',
desc=r'updraft vapor specific humidity square')
NS.add_profile('updraft_cloudfraction', Gr, Pa, units=r'--', nice_name= r'f_{c,u}',
desc=r'updraft cloud fraction')
NS.add_profile('env_fraction', Gr, Pa, units=r'-',nice_name=r'a_e',
desc=r'environment area fraction' )
NS.add_profile('env_w', Gr, Pa, units=r'm s^{-1}', nice_name=r'w_e',
desc=r'environment vertical velocity at cell centers')
NS.add_profile('env_w2', Gr, Pa, units=r'm^{2} s^{-2}', nice_name=r'w^2_e',
desc=r'environment vertical velocity square at cell centers')
NS.add_profile('env_u', Gr, Pa, units=r'm s^{-1}', nice_name=r'u_e',
desc=r'environment u velocity')
NS.add_profile('env_u2', Gr, Pa, units=r'm^{2} s^{-2}', nice_name=r'u^2_e',
desc=r'environment u velocity square at cell centers')
NS.add_profile('env_v', Gr, Pa, units=r'm s^{-1}', nice_name=r'v_e',
desc=r'environment v velocity')
NS.add_profile('env_v2', Gr, Pa, units=r'm^{2} s^{-2}', nice_name=r'v^2_e',
desc=r'environment v velocity square at cell centers')
NS.add_profile('env_qt', Gr, Pa, units=r'kg kg^{-1}',nice_name=r'q_{t,e}',
desc=r'environment total water specific humidity')
NS.add_profile('env_qt2', Gr, Pa, units=r'kg^{2} kg^{-2}',nice_name=r'q_{t,e}^2',
desc=r'environment total water specific humidity square')
NS.add_profile('env_thetali', Gr, Pa, units=r'K', nice_name=r'\theta_{li,e}',
desc=r'environment liquid ice potential temperature')
NS.add_profile('env_thetali2', Gr, Pa, units=r'K^2', nice_name=r'\theta_{li,e}^2',
desc=r'environment liquid ice potential temperature square')
NS.add_profile('env_b', Gr, Pa, units=r'm s^{-2}', nice_name=r'b_{e}',
desc=r'environment buoyancy (not anomaly)')
NS.add_profile('env_b2', Gr, Pa, units=r'm s^{-2}', nice_name=r'b_{e}^2',
desc=r'environment buoyancy (not anomaly) square')
NS.add_profile('env_w_qt', Gr, Pa, units=r' m s^{-1} kg kg^{-1}', nice_name=r'(w q_t)_e',
desc=r'environmnet product of w and q_t')
NS.add_profile('env_w_thetali', Gr, Pa, units=r' m s^{-1} K', nice_name=r'(w \theta_{li})_e',
desc=r'environment product of w and \theta_{li}')
NS.add_profile('env_qt_thetali', Gr, Pa, units=r'kg kg^{-1} K', nice_name=r'(q_t \theta_{li})_e',
desc=r'environment product of q_t and \theta_{li}')
NS.add_profile('env_bvf', Gr, Pa, units=r's^{-1}', nice_name=r'N_{b,e}',
desc=r'environment buoyancy frequency')
NS.add_profile('env_temperature', Gr, Pa, units=r'K', nice_name=r'T_{e}',
desc=r'environment temperature')
NS.add_profile('env_temperature2', Gr, Pa, units=r'K^2', nice_name=r'T_{e}^2',
desc=r'environment temperature square')
NS.add_profile('env_qv', Gr, Pa, units=r'kg kg^{-1}',nice_name=r'q_{v,e}',
desc=r'environment vapor specific humidity')
NS.add_profile('env_qv2', Gr, Pa, units=r'kg^{2} kg^{-2}',nice_name=r'q_{v,e}^2',
desc=r'environment vapor specific humidity square')
NS.add_profile('env_cloudfraction', Gr, Pa, units=r'--', nice_name= r'f_{c,e}',
desc=r'environment cloud fraction')
NS.add_profile('env_thetarho', Gr, Pa, units=r'K', nice_name=r'\theta_{\rho,e}',
desc=r'environment density potential temperature')
if 'ql' in DV.name_index:
NS.add_profile('updraft_ql', Gr, Pa, units=r'kg kg^{-1}',nice_name=r'q_{l,u}',
desc=r'updraft liquid water specific humidity')
NS.add_profile('updraft_ql2', Gr, Pa, units=r'kg^2 kg^{-2}',nice_name=r'q_{t,u}^2',
desc=r'updraft total water specific humidity square')
NS.add_profile('env_ql', Gr, Pa, units=r'kg kg^{-1}',nice_name=r'q_{l,e}',
desc=r'environment liquid water specific humidity')
NS.add_profile('env_ql2', Gr, Pa, units=r'kg^2 kg^{-2}',nice_name=r'q_{l,u}^2',
desc=r'environment total water specific humidity square')
if 'qr' in PV.name_index:
NS.add_profile('updraft_qr', Gr, Pa, units=r'kg kg^{-1}',nice_name=r'q_{r,u}',
desc=r'updraft rain water specific humidity')
NS.add_profile('updraft_qr2', Gr, Pa, units=r'kg^2 kg^{-2}',nice_name=r'q_{r,u}^2',
desc=r'updraft rain water specific humidity square')
NS.add_profile('env_qr', Gr, Pa, units=r'kg kg^{-1}',nice_name=r'q_{r,e}',
desc=r'environment rain water specific humidity')
NS.add_profile('env_qr2', Gr, Pa, units=r'kg^2 kg^{-2}',nice_name=r'q_{r,e}^2',
desc=r'environment rain water specific humidity square')
NS.add_profile('updraft_ql_qr', Gr, Pa, units=r'kg^2 kg^{-2}', nice_name=r'(q_l q_r)_u',
desc=r'updraft product of q_l and q_r')
NS.add_profile('env_ql_qr', Gr, Pa, units=r'kg^2 kg^{-2}', nice_name=r'(q_l q_r)_e',
desc=r'environment product of q_l and q_r')
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref, PrognosticVariables.PrognosticVariables PV,
DiagnosticVariables.DiagnosticVariables DV, TimeStepping.TimeStepping TS, ParallelMPI.ParallelMPI Pa):
cdef:
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 var_shift
double tau
# Set the source term
for level in self.tracer_dict.keys():
for var in self.tracer_dict[level].keys():
var_shift = PV.get_varshift(Gr, var)
tau = self.tracer_dict[level][var]['timescale']
with nogil:
for i in xrange(Gr.dims.npg):
PV.tendencies[var_shift + i] -= fmax(PV.values[var_shift + i],0.0)/tau
#Below we assume domain uses only x-y decomposition, for the general case we should use pencils
#but we don't address that complication for now
# Set the value of the surface based tracers
for var in self.tracer_dict['surface'].keys():
var_shift = PV.get_varshift(Gr, var)
with nogil:
for i in xrange(Gr.dims.nlg[0]):
for j in xrange(Gr.dims.nlg[1]):
ijk = i * istride + j * jstride + Gr.dims.gw
PV.values[var_shift + ijk] = 1.0
# Find surface thermodynamic properties to compute LCL (lifting condensation level)
# Ref: M. G. Lawrence, "The relationship between relative humidity and the dew point
# temperature in moist air: A simple conversion and applications", Bull. Am. Meteorol. Soc., 86, 225-233, 2005
cdef:
double [:] T_dew = np.zeros(Gr.dims.nl[0]*Gr.dims.nl[1],dtype=np.double,order='c')
double [:] z_lcl = np.zeros(Gr.dims.nl[0]*Gr.dims.nl[1],dtype=np.double,order='c')
Py_ssize_t t_shift, qv_shift, qt_shift
Py_ssize_t count = 0
double B1 = 243.04 # C
double A1 = 17.625 # no units
double C1 = 610.94 # Pa
double CtoK = 273.15 # additive celsius to kelvin conversion factor
double vapor_pressure
double nxny_i =1.0/( Gr.dims.n[0] * Gr.dims.n[1])
double lcl_, lcl
if self.lcl_tracers:
t_shift = DV.get_varshift(Gr,'temperature')
qt_shift = PV.get_varshift(Gr, 'qt')
qv_shift = DV.get_varshift(Gr,'qv')
with nogil:
for i in xrange(Gr.dims.gw, Gr.dims.nlg[0]-Gr.dims.gw):
for j in xrange(Gr.dims.gw, Gr.dims.nlg[1]-Gr.dims.gw):
ijk = i * istride + j * jstride + Gr.dims.gw
vapor_pressure = pv_c(Ref.p0_half[Gr.dims.gw], PV.values[qt_shift + ijk], DV.values[qv_shift + ijk])
T_dew[count] = B1 * log(vapor_pressure/C1)/(A1-log(vapor_pressure/C1)) + CtoK
z_lcl[count] = 125.0 * (DV.values[t_shift+ijk] - T_dew[count])
count += 1
lcl_ = np.sum(z_lcl) * nxny_i
lcl = Pa.domain_scalar_sum(lcl_)
for k in xrange(Gr.dims.nlg[2]-Gr.dims.gw, Gr.dims.gw-1, -1):
if Gr.zl_half[k] < lcl:
self.index_lcl = k + 1
break
for var in self.tracer_dict['lcl'].keys():
var_shift = PV.get_varshift(Gr, var)
with nogil:
for i in xrange( Gr.dims.nlg[0]):
for j in xrange( Gr.dims.nlg[1]):
ijk = i * istride + j * jstride + self.index_lcl
PV.values[var_shift + ijk] = 1.0
return
cpdef update_cleanup(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref, PrognosticVariables.PrognosticVariables PV,
DiagnosticVariables.DiagnosticVariables DV,ParallelMPI.ParallelMPI Pa, TimeStepping.TimeStepping TS):
cdef:
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 var_shift
#Below we assume domain uses only x-y decomposition, for the general case we should use pencils
#but we don't address that complication for now
# Set the value of the surface based tracers
for var in self.tracer_dict['surface'].keys():
var_shift = PV.get_varshift(Gr, var)
with nogil:
for i in xrange(Gr.dims.nlg[0]):
for j in xrange(Gr.dims.nlg[1]):
ijk = i * istride + j * jstride + Gr.dims.gw
PV.tendencies[var_shift + ijk] = 0.0
for k in xrange( Gr.dims.nlg[2]):
ijk = i * istride + j * jstride + k
PV.values[var_shift + ijk] = fmax(PV.values[var_shift + ijk],0.0)
if self.lcl_tracers:
for var in self.tracer_dict['lcl'].keys():
var_shift = PV.get_varshift(Gr, var)
with nogil:
for i in xrange( Gr.dims.nlg[0]):
for j in xrange( Gr.dims.nlg[1]):
ijk = i * istride + j * jstride + self.index_lcl
PV.tendencies[var_shift + ijk] = 0.0
for k in xrange( Gr.dims.nlg[2]):
ijk = i * istride + j * jstride + k
PV.values[var_shift + ijk] = fmax(PV.values[var_shift + ijk],0.0)
return
cpdef stats_io(self, Grid.Grid Gr, PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV,
TimeStepping.TimeStepping TS, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
cdef:
Py_ssize_t u_shift = PV.get_varshift(Gr,'u')
Py_ssize_t v_shift = PV.get_varshift(Gr,'v')
Py_ssize_t w_shift = PV.get_varshift(Gr,'w')
Py_ssize_t q_shift = PV.get_varshift(Gr,'qt')
Py_ssize_t c_shift = PV.get_varshift(Gr,'c_srf_15')
Py_ssize_t b_shift = DV.get_varshift(Gr, 'buoyancy')
Py_ssize_t t_shift = DV.get_varshift(Gr, 'temperature')
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 qv_shift = DV.get_varshift(Gr, 'qv')
Py_ssize_t ql_shift, th_shift, qr_shift
double [:] cloudfraction = np.zeros((Gr.dims.npg),dtype=np.double, order='c')
double [:] tracer_normed = np.zeros((Gr.dims.npg),dtype=np.double, order='c')
double [:] env_indicator = np.zeros((Gr.dims.npg),dtype=np.double, order='c')
double [:] u_half = np.zeros((Gr.dims.npg),dtype=np.double, order='c')
double [:] v_half = np.zeros((Gr.dims.npg),dtype=np.double, order='c')
double [:] w_half = np.zeros((Gr.dims.npg),dtype=np.double, order='c')
double [:] mean = Pa.HorizontalMean(Gr, &PV.values[c_shift])
double [:] mean_square = Pa.HorizontalMeanofSquares(Gr, &PV.values[c_shift], &PV.values[c_shift])
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
if self.lcl_tracers:
NS.write_ts('grid_lcl',Gr.zl_half[self.index_lcl], Pa)
if 'ql' in DV.name_index:
ql_shift = DV.get_varshift(Gr,'ql')
self.get_cloud_heights(Gr, DV, Pa)
print('cloud base, height ', self.cloud_base, self.cloud_top)
updraft_indicator_sc_w_ql(&Gr.dims, &PV.values[c_shift], &tracer_normed[0], &mean[0], &mean_square[0],
&PV.values[w_shift],&DV.values[ql_shift], &Gr.z_half[0], self.cloud_base, self.cloud_top)
# updraft_indicator_sc_w(&Gr.dims, &PV.values[c_shift], &tracer_normed[0], &mean[0], &mean_square[0], &PV.values[w_shift])
else:
updraft_indicator_sc_w(&Gr.dims, &PV.values[c_shift], &tracer_normed[0], &mean[0], &mean_square[0], &PV.values[w_shift])
with nogil:
for i in range(Gr.dims.nlg[0]):
ishift = i * istride
for j in range(Gr.dims.nlg[1]):
jshift = j * jstride
for k in range(Gr.dims.nlg[2]):
ijk = ishift + jshift + k
if tracer_normed[ijk] >= 1.0:
self.updraft_indicator[ijk] = 1.0
else:
self.updraft_indicator[ijk] = 0.0
env_indicator[ijk] = 1.0 - self.updraft_indicator[ijk]
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
u_half[ijk] = 0.5 * (PV.values[u_shift+ijk-istride] + PV.values[u_shift+ijk])
v_half[ijk] = 0.5 * (PV.values[v_shift+ijk-jstride] + PV.values[v_shift+ijk])
w_half[ijk] = 0.5 * (PV.values[w_shift+ijk-1] + PV.values[w_shift+ijk])
tmp = Pa.HorizontalMean(Gr, &self.updraft_indicator[0])
NS.write_profile('updraft_fraction', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanConditional(Gr, &w_half[0], &self.updraft_indicator[0])
NS.write_profile('updraft_w', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanofSquaresConditional(Gr, &w_half[0], &w_half[0], &self.updraft_indicator[0])
NS.write_profile('updraft_w2', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanConditional(Gr, &u_half[0], &self.updraft_indicator[0])
NS.write_profile('updraft_u', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanofSquaresConditional(Gr, &u_half[0], &u_half[0], &self.updraft_indicator[0])
NS.write_profile('updraft_u2', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanConditional(Gr, &v_half[0], &self.updraft_indicator[0])
NS.write_profile('updraft_v', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanofSquaresConditional(Gr,&v_half[0], &v_half[0], &self.updraft_indicator[0])
NS.write_profile('updraft_v2', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanConditional(Gr, &PV.values[q_shift], &self.updraft_indicator[0])
NS.write_profile('updraft_qt', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanofSquaresConditional(Gr, &PV.values[q_shift], &PV.values[q_shift], &self.updraft_indicator[0])
NS.write_profile('updraft_qt2', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
if 'thetali' in DV.name_index:
th_shift = DV.get_varshift(Gr, 'thetali')
else:
th_shift = DV.get_varshift(Gr, 'theta')
tmp = Pa.HorizontalMeanConditional(Gr, &DV.values[th_shift], &self.updraft_indicator[0])
NS.write_profile('updraft_thetali', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanofSquaresConditional(Gr, &DV.values[th_shift], &DV.values[th_shift], &self.updraft_indicator[0])
NS.write_profile('updraft_thetali2', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanConditional(Gr, &DV.values[b_shift], &self.updraft_indicator[0])
NS.write_profile('updraft_b', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanofSquaresConditional(Gr, &DV.values[b_shift], &DV.values[b_shift], &self.updraft_indicator[0])
NS.write_profile('updraft_b2', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanofSquaresConditional(Gr, &w_half[0], &PV.values[q_shift], &self.updraft_indicator[0])
NS.write_profile('updraft_w_qt', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanofSquaresConditional(Gr, &w_half[0], &DV.values[th_shift], &self.updraft_indicator[0])
NS.write_profile('updraft_w_thetali', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanofSquaresConditional(Gr, &DV.values[th_shift], &PV.values[q_shift], &self.updraft_indicator[0])
NS.write_profile('updraft_qt_thetali', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanConditional(Gr, &DV.values[t_shift], &self.updraft_indicator[0])
NS.write_profile('updraft_temperature', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanofSquaresConditional(Gr, &DV.values[t_shift], &DV.values[t_shift], &self.updraft_indicator[0])
NS.write_profile('updraft_temperature2', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanConditional(Gr, &DV.values[qv_shift], &self.updraft_indicator[0])
NS.write_profile('updraft_qv', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanofSquaresConditional(Gr, &DV.values[qv_shift], &DV.values[qv_shift], &self.updraft_indicator[0])
NS.write_profile('updraft_qv2', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanConditional(Gr, &DV.values[thr_shift], &self.updraft_indicator[0])
NS.write_profile('updraft_thetarho', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanConditional(Gr, &DV.values[bvf_shift], &self.updraft_indicator[0])
NS.write_profile('updraft_bvf', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMean(Gr, &env_indicator[0])
NS.write_profile('env_fraction', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanConditional(Gr, &w_half[0], &env_indicator[0])
NS.write_profile('env_w', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanofSquaresConditional(Gr, &w_half[0], &w_half[0], &env_indicator[0])
NS.write_profile('env_w2', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanConditional(Gr, &PV.values[u_shift], &env_indicator[0])
NS.write_profile('env_u', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanofSquaresConditional(Gr, &PV.values[u_shift], &PV.values[u_shift], &env_indicator[0])
NS.write_profile('env_u2', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanConditional(Gr, &PV.values[v_shift], &env_indicator[0])
NS.write_profile('env_v', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanofSquaresConditional(Gr, &PV.values[v_shift], &PV.values[v_shift], &env_indicator[0])
NS.write_profile('env_v2', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanConditional(Gr, &PV.values[q_shift], &env_indicator[0])
NS.write_profile('env_qt', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanofSquaresConditional(Gr, &PV.values[q_shift], &PV.values[q_shift], &env_indicator[0])
NS.write_profile('env_qt2', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanConditional(Gr, &DV.values[th_shift], &env_indicator[0])
NS.write_profile('env_thetali', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanofSquaresConditional(Gr, &DV.values[th_shift], &DV.values[th_shift], &env_indicator[0])
NS.write_profile('env_thetali2', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanConditional(Gr, &DV.values[b_shift], &env_indicator[0])
NS.write_profile('env_b', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanofSquaresConditional(Gr, &DV.values[b_shift], &DV.values[b_shift], &env_indicator[0])
NS.write_profile('env_b2', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanofSquaresConditional(Gr,&w_half[0], &PV.values[q_shift], &env_indicator[0])
NS.write_profile('env_w_qt', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanofSquaresConditional(Gr, &w_half[0], &DV.values[th_shift], &env_indicator[0])
NS.write_profile('env_w_thetali', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanofSquaresConditional(Gr, &DV.values[th_shift], &PV.values[q_shift], &env_indicator[0])
NS.write_profile('env_qt_thetali', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanConditional(Gr, &DV.values[t_shift], &env_indicator[0])
NS.write_profile('env_temperature', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanofSquaresConditional(Gr, &DV.values[t_shift], &DV.values[t_shift], &env_indicator[0])
NS.write_profile('env_temperature2', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanConditional(Gr, &DV.values[qv_shift], &env_indicator[0])
NS.write_profile('env_qv', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanofSquaresConditional(Gr, &DV.values[qv_shift], &DV.values[qv_shift], &env_indicator[0])
NS.write_profile('env_qv2', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanConditional(Gr, &DV.values[thr_shift], &env_indicator[0])
NS.write_profile('env_thetarho', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanConditional(Gr, &DV.values[bvf_shift], &env_indicator[0])
NS.write_profile('env_bvf', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
if 'ql' in DV.name_index:
ql_shift = DV.get_varshift(Gr, 'ql')
tmp = Pa.HorizontalMeanConditional(Gr, &DV.values[ql_shift], &self.updraft_indicator[0])
NS.write_profile('updraft_ql', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanofSquaresConditional(Gr, &DV.values[ql_shift], &DV.values[ql_shift], &self.updraft_indicator[0])
NS.write_profile('updraft_ql2', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanConditional(Gr, &DV.values[ql_shift], &env_indicator[0])
NS.write_profile('env_ql', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanofSquaresConditional(Gr, &DV.values[ql_shift], &DV.values[ql_shift], &env_indicator[0])
NS.write_profile('env_ql2', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
with nogil:
for i in range(Gr.dims.nlg[0]):
ishift = i * istride
for j in range(Gr.dims.nlg[1]):
jshift = j * jstride
for k in range(Gr.dims.nlg[2]):
ijk = ishift + jshift + k
if DV.values[ql_shift+ ijk] >= ql_threshold:
cloudfraction[ijk] = 1.0
tmp = Pa.HorizontalMeanConditional(Gr, &cloudfraction[0], &self.updraft_indicator[0])
NS.write_profile('updraft_cloudfraction', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanConditional(Gr, &cloudfraction[0], &env_indicator[0])
NS.write_profile('env_cloudfraction', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
if 'qr' in PV.name_index:
qr_shift = PV.get_varshift(Gr, 'qr')
tmp = Pa.HorizontalMeanConditional(Gr, &PV.values[qr_shift], &self.updraft_indicator[0])
NS.write_profile('updraft_qr', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanofSquaresConditional(Gr, &PV.values[qr_shift], &PV.values[qr_shift], &self.updraft_indicator[0])
NS.write_profile('updraft_qr2', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanConditional(Gr, &PV.values[qr_shift], &env_indicator[0])
NS.write_profile('env_qr', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanofSquaresConditional(Gr, &PV.values[qr_shift], &PV.values[qr_shift], &env_indicator[0])
NS.write_profile('env_qr2', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanofSquaresConditional(Gr, &DV.values[ql_shift], &PV.values[qr_shift], &self.updraft_indicator[0])
NS.write_profile('updraft_ql_qr', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
tmp = Pa.HorizontalMeanofSquaresConditional(Gr, &DV.values[ql_shift], &PV.values[qr_shift], &env_indicator[0])
NS.write_profile('env_ql_qr', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
return
cpdef get_cloud_heights(self, Grid.Grid Gr, DiagnosticVariables.DiagnosticVariables DV, ParallelMPI.ParallelMPI Pa):
cdef:
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 gw = Gr.dims.gw
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 ql_shift = DV.get_varshift(Gr, 'ql')
double cb, ct
# Compute cloud top and cloud base height
cb = 99999.9
ct = -99999.9
with nogil:
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
if DV.values[ql_shift+ijk] > ql_threshold:
cb = fmin(cb, Gr.z_half[k])
ct = fmax(ct, Gr.z_half[k])
self.cloud_base = Pa.domain_scalar_min(cb)
self.cloud_top = Pa.domain_scalar_max(ct)
return
cdef class PurityTracers:
def __init__(self, namelist):
cdef UpdraftTracers TracersUpdraft
self.TracersUpdraft = UpdraftTracers(namelist)
return
cpdef initialize(self, Grid.Grid Gr, PrognosticVariables.PrognosticVariables PV,
DiagnosticVariables.DiagnosticVariables DV, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
self.TracersUpdraft.initialize(Gr, PV, DV, NS, Pa)
# Here we need to add purity + origin info tracers for each of the updraft diagnostic tracers
# To get this working, we assume only 15 min timescale diagnostic tracers
PV.add_variable('purity_srf', '-', 'purity_srf', 'tracer diagnostics', "sym", "scalar", Pa)
PV.add_variable('time_srf', '-', 'time_srf', 'tracer diagnostics', "sym", "scalar", Pa)
PV.add_variable('qt_srf', '-', 'qt_srf', 'tracer diagnostics', "sym", "scalar", Pa)
PV.add_variable('thetali_srf', '-', 'thetali_srf', 'tracer diagnostics', "sym", "scalar", Pa)
if self.TracersUpdraft.lcl_tracers:
PV.add_variable('purity_lcl', '-', 'purity_lcl', 'tracer diagnostics', "sym", "scalar", Pa)
PV.add_variable('time_lcl', '-', 'time_lcl', 'tracer diagnostics', "sym", "scalar", Pa)
PV.add_variable('qt_lcl', '-', 'qt_lcl', 'tracer diagnostics', "sym", "scalar", Pa)
PV.add_variable('thetali_lcl', '-', 'thetali_lcl', 'tracer diagnostics', "sym", "scalar", Pa)
NS.add_profile('updraft_purity_srf', Gr, Pa)
NS.add_profile('updraft_time_srf', Gr, Pa)
NS.add_profile('updraft_qt_srf', Gr, Pa)
NS.add_profile('updraft_thetali_srf', Gr, Pa)
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref, PrognosticVariables.PrognosticVariables PV,
DiagnosticVariables.DiagnosticVariables DV, TimeStepping.TimeStepping TS, ParallelMPI.ParallelMPI Pa):
self.TracersUpdraft.update(Gr,Ref, PV, DV, TS, Pa)
# First do the surface tracers
cdef:
Py_ssize_t w_shift = PV.get_varshift(Gr,'w')
Py_ssize_t q_shift = PV.get_varshift(Gr,'qt')
Py_ssize_t th_shift #= DV.get_varshift(Gr,'thetali')
Py_ssize_t c_shift = PV.get_varshift(Gr,'c_srf_15')
Py_ssize_t p_shift = PV.get_varshift(Gr,'purity_srf')
Py_ssize_t pt_shift = PV.get_varshift(Gr,'time_srf')
Py_ssize_t pq_shift = PV.get_varshift(Gr,'qt_srf')
Py_ssize_t pth_shift = PV.get_varshift(Gr,'thetali_srf')
Py_ssize_t index_lcl
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, i,j,k
Py_ssize_t gw = Gr.dims.gw, ql_shift
double [:] tracer_normed = np.zeros((Gr.dims.npg),dtype=np.double, order='c')
double [:] mean = Pa.HorizontalMean(Gr, &PV.values[c_shift])
double [:] mean_square = Pa.HorizontalMeanofSquares(Gr, &PV.values[c_shift], &PV.values[c_shift])
double max_x, max_y, max_z, total_max
Py_ssize_t conditions_flag = 1
if 'ql' not in DV.name_index and conditions_flag == 3:
conditions_flag = 2
if conditions_flag == 1:
updraft_indicator_sc(&Gr.dims, &PV.values[c_shift], &tracer_normed[0],
&mean[0], &mean_square[0])
elif conditions_flag == 2:
updraft_indicator_sc_w(&Gr.dims, &PV.values[c_shift], &tracer_normed[0],
&mean[0], &mean_square[0], &PV.values[w_shift])
elif conditions_flag == 3:
ql_shift = DV.get_varshift(Gr, 'ql')
self.TracersUpdraft.get_cloud_heights(Gr,DV, Pa)
updraft_indicator_sc_w_ql(&Gr.dims, &PV.values[c_shift], &tracer_normed[0],&mean[0], &mean_square[0],
&PV.values[w_shift], &DV.values[ql_shift], &Gr.z_half[0],
self.TracersUpdraft.cloud_base, self.TracersUpdraft.cloud_top )
if 'thetali' in DV.name_index:
th_shift = DV.get_varshift(Gr,'thetali')
else:
th_shift = DV.get_varshift(Gr,'theta')
with nogil:
for i in xrange(gw, Gr.dims.nlg[0]-gw):
for j in xrange(gw, Gr.dims.nlg[1]-gw):
ijk = i * istride + j * jstride + Gr.dims.gw
PV.values[p_shift + ijk] = 1.0
PV.values[pt_shift + ijk] = TS.t
PV.values[pq_shift + ijk] = PV.values[q_shift + ijk]
PV.values[pth_shift + ijk] = DV.values[th_shift + ijk]
for k in xrange(gw + 1, Gr.dims.nlg[2]-1):
ijk = i * istride + j * jstride + k
max_x = fmax(tracer_normed[ijk-istride],tracer_normed[ijk+istride])
max_y = fmax(tracer_normed[ijk-jstride],tracer_normed[ijk+jstride])
max_z = fmax(tracer_normed[ijk-1],tracer_normed[ijk+1])
total_max = fmax(fmax(fmax(max_x,max_y),max_z), tracer_normed[ijk])
if total_max < 0.9:
PV.values[p_shift + ijk] = 0.0
PV.values[pt_shift + ijk] = 0.0
PV.values[pq_shift + ijk] = 0.0
PV.values[pth_shift + ijk] = 0.0
else:
PV.values[p_shift + ijk] = fmin(fmax(0.0, PV.values[p_shift + ijk]), 1.0)
PV.values[pt_shift + ijk] = fmin(fmax(0.0,PV.values[pt_shift + ijk] ), TS.t)
if self.TracersUpdraft.lcl_tracers:
index_lcl = self.TracersUpdraft.index_lcl
c_shift = PV.get_varshift(Gr,'c_lcl_15')
p_shift = PV.get_varshift(Gr,'purity_lcl')
pt_shift = PV.get_varshift(Gr,'time_lcl')
pq_shift = PV.get_varshift(Gr,'qt_lcl')
pth_shift = PV.get_varshift(Gr,'thetali_lcl')
mean = Pa.HorizontalMean(Gr, &PV.values[c_shift])
mean_square = Pa.HorizontalMeanofSquares(Gr, &PV.values[c_shift], &PV.values[c_shift])
if conditions_flag == 1:
updraft_indicator_sc(&Gr.dims, &PV.values[c_shift], &tracer_normed[0],
&mean[0], &mean_square[0])
elif conditions_flag == 2:
updraft_indicator_sc_w(&Gr.dims, &PV.values[c_shift], &tracer_normed[0],
&mean[0], &mean_square[0], &PV.values[w_shift])
elif conditions_flag == 3:
ql_shift = DV.get_varshift(Gr, 'ql')
self.TracersUpdraft.get_cloud_heights(Gr,DV, Pa)
updraft_indicator_sc_w_ql(&Gr.dims, &PV.values[c_shift], &tracer_normed[0],&mean[0], &mean_square[0],
&PV.values[w_shift], &DV.values[ql_shift], &Gr.z_half[0],
self.TracersUpdraft.cloud_base, self.TracersUpdraft.cloud_top )
with nogil:
for i in xrange(gw, Gr.dims.nlg[0]-gw):
for j in xrange(gw, Gr.dims.nlg[1]-gw):
for k in xrange(gw,index_lcl+1):
ijk = i * istride + j * jstride + k
PV.values[p_shift + ijk] = 1.0
PV.values[pt_shift + ijk] = TS.t
PV.values[pq_shift + ijk] = PV.values[q_shift + ijk]
PV.values[pth_shift + ijk] = DV.values[th_shift + ijk]
for k in xrange(index_lcl+1, Gr.dims.nlg[2]-gw):
ijk = i * istride + j * jstride + k
max_x = fmax(tracer_normed[ijk-istride],tracer_normed[ijk+istride])
max_y = fmax(tracer_normed[ijk-jstride],tracer_normed[ijk+jstride])
max_z = fmax(tracer_normed[ijk-1],tracer_normed[ijk+1])
total_max = fmax(fmax(fmax(max_x,max_y),max_z), tracer_normed[ijk])
if total_max < 0.9:
PV.values[p_shift + ijk] = 0.0
PV.values[pt_shift + ijk] = 0.0
PV.values[pq_shift + ijk] = 0.0
PV.values[pth_shift + ijk] = 0.0
else:
PV.values[p_shift + ijk] = fmin(fmax(0.0, PV.values[p_shift + ijk]), 1.0)
PV.values[pt_shift + ijk] = fmin(fmax(0.0,PV.values[pt_shift + ijk] ), TS.t)
# Update the boundary points
PV.Update_all_bcs(Gr, Pa )
return
cpdef update_cleanup(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref, PrognosticVariables.PrognosticVariables PV,
DiagnosticVariables.DiagnosticVariables DV,ParallelMPI.ParallelMPI Pa, TimeStepping.TimeStepping TS):
self.TracersUpdraft.update_cleanup(Gr, Ref, PV, DV, Pa, TS)
cdef:
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 p_shift = PV.get_varshift(Gr,'purity_srf')
Py_ssize_t pt_shift = PV.get_varshift(Gr,'time_srf')
Py_ssize_t pq_shift = PV.get_varshift(Gr,'qt_srf')
Py_ssize_t pth_shift = PV.get_varshift(Gr,'thetali_srf')
#Below we assume domain uses only x-y decomposition, for the general case we should use pencils
#but we don't address that complication for now
# Set the value of the surface based tracers
with nogil:
for i in xrange(Gr.dims.nlg[0]):
for j in xrange(Gr.dims.nlg[1]):
ijk = i * istride + j * jstride + Gr.dims.gw
PV.tendencies[p_shift + ijk] = 0.0
PV.tendencies[pt_shift + ijk] = 0.0
PV.tendencies[pq_shift + ijk] = 0.0
PV.tendencies[pth_shift + ijk] = 0.0
for k in xrange( Gr.dims.nlg[2]):
ijk = i * istride + j * jstride + k
PV.values[p_shift + ijk] = fmin(fmax(PV.values[p_shift + ijk],0.0),1.0)
PV.values[pt_shift + ijk] = fmin(fmax(PV.values[pt_shift + ijk],0.0), TS.t)
PV.values[pq_shift + ijk] = fmax(PV.values[pq_shift + ijk],0.0)
PV.values[pth_shift + ijk] = fmax(PV.values[pth_shift + ijk],0.0)
if self.TracersUpdraft.lcl_tracers:
p_shift = PV.get_varshift(Gr,'purity_lcl')
pt_shift = PV.get_varshift(Gr,'time_lcl')
pq_shift = PV.get_varshift(Gr,'qt_lcl')
pth_shift = PV.get_varshift(Gr,'thetali_lcl')
with nogil:
for i in xrange( Gr.dims.nlg[0]):
for j in xrange( Gr.dims.nlg[1]):
for k in xrange(self.TracersUpdraft.index_lcl + 1):
ijk = i * istride + j * jstride + k
PV.tendencies[p_shift + ijk] = 0.0
PV.tendencies[pt_shift + ijk] = 0.0
PV.tendencies[pq_shift + ijk] = 0.0
PV.tendencies[pth_shift + ijk] = 0.0
for k in xrange(self.TracersUpdraft.index_lcl + 1, Gr.dims.nlg[2]):
ijk = i * istride + j * jstride + k
PV.values[p_shift + ijk] = fmin(fmax(PV.values[p_shift + ijk],0.0),1.0)
PV.values[pt_shift + ijk] = fmin(fmax(PV.values[pt_shift + ijk],0.0), TS.t)
PV.values[pq_shift + ijk] = fmax(PV.values[pq_shift + ijk],0.0)
PV.values[pth_shift + ijk] = fmax(PV.values[pth_shift + ijk],0.0)
return
cpdef stats_io(self, Grid.Grid Gr, PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV,
TimeStepping.TimeStepping TS, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
self.TracersUpdraft.stats_io(Gr, PV, DV, TS, NS, Pa)
cdef:
double [:] extracted_purity_var = np.zeros((Gr.dims.npg),dtype=np.double, order='c')
Py_ssize_t p_shift = PV.get_varshift(Gr,'purity_srf')
Py_ssize_t pt_shift = PV.get_varshift(Gr,'time_srf')
Py_ssize_t pq_shift = PV.get_varshift(Gr,'qt_srf')
Py_ssize_t pth_shift = PV.get_varshift(Gr,'thetali_srf')
tmp = Pa.HorizontalMeanConditional(Gr, &PV.values[p_shift], &self.TracersUpdraft.updraft_indicator[0])
NS.write_profile('updraft_purity_srf', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
purity_extract_time(&Gr.dims, &PV.values[p_shift], &PV.values[pt_shift], &extracted_purity_var[0], TS.t)
tmp = Pa.HorizontalMeanConditional(Gr, &extracted_purity_var[0], &self.TracersUpdraft.updraft_indicator[0])
NS.write_profile('updraft_time_srf', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
purity_extract_value(&Gr.dims, &PV.values[p_shift], &PV.values[pq_shift], &extracted_purity_var[0])
tmp = Pa.HorizontalMeanConditional(Gr, &extracted_purity_var[0], &self.TracersUpdraft.updraft_indicator[0])
NS.write_profile('updraft_qt_srf', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
purity_extract_value(&Gr.dims, &PV.values[p_shift], &PV.values[pth_shift], &extracted_purity_var[0])
tmp = Pa.HorizontalMeanConditional(Gr, &extracted_purity_var[0], &self.TracersUpdraft.updraft_indicator[0])
NS.write_profile('updraft_thetali_srf', tmp[Gr.dims.gw:-Gr.dims.gw], Pa)
return
cdef updraft_indicator_sc(Grid.DimStruct *dims, double *tracer_raw, double *tracer_normed,
double *mean, double *meansquare_sigma):
cdef:
Py_ssize_t imax = dims.nlg[0]
Py_ssize_t jmax = dims.nlg[1]
Py_ssize_t kmax = dims.nlg[2]
Py_ssize_t istride = dims.nlg[1] * dims.nlg[2]
Py_ssize_t jstride = dims.nlg[2]
Py_ssize_t ishift, jshift, ijk, i,j,k
double sigma_min
double sigma_sum = 0.0
with nogil:
for k in xrange(kmax):
meansquare_sigma[k] = meansquare_sigma[k] - mean[k] * mean[k]
meansquare_sigma[k] = sqrt(fmax(meansquare_sigma[k],0.0))
sigma_sum += meansquare_sigma[k]
sigma_min = sigma_sum/(k+1.0) * 0.05
if meansquare_sigma[k] < sigma_min:
for i in xrange(imax):
ishift = i*istride
for j in xrange(jmax):
jshift = j*jstride
ijk = ishift + jshift + k
tracer_normed[ijk] = 0.0
else:
for i in xrange(imax):
ishift = i*istride
for j in xrange(jmax):
jshift = j*jstride
ijk = ishift + jshift + k
tracer_normed[ijk] = (tracer_raw[ijk] - mean[k])/ meansquare_sigma[k]
return
cdef updraft_indicator_sc_w(Grid.DimStruct *dims, double *tracer_raw, double *tracer_normed, double *mean, double *meansquare_sigma, double *w):
cdef:
Py_ssize_t imax = dims.nlg[0]
Py_ssize_t jmax = dims.nlg[1]
Py_ssize_t kmax = dims.nlg[2]
Py_ssize_t istride = dims.nlg[1] * dims.nlg[2]
Py_ssize_t jstride = dims.nlg[2]
Py_ssize_t ishift, jshift, ijk, i,j,k
double sigma_min
double sigma_sum = 0.0
double w_half
with nogil:
for k in xrange(1,kmax):
meansquare_sigma[k] = meansquare_sigma[k] - mean[k] * mean[k]
meansquare_sigma[k] = sqrt(fmax(meansquare_sigma[k],0.0))
sigma_sum += meansquare_sigma[k]
sigma_min = sigma_sum/(k+1.0) * 0.05
if meansquare_sigma[k] < sigma_min:
for i in xrange(imax):
ishift = i*istride
for j in xrange(jmax):
jshift = j*jstride
ijk = ishift + jshift + k
tracer_normed[ijk] = 0.0
else:
for i in xrange(imax):
ishift = i*istride
for j in xrange(jmax):
jshift = j*jstride
ijk = ishift + jshift + k
w_half = 0.5*(w[ijk-1] + w[ijk])
tracer_normed[ijk] = copysign( (tracer_raw[ijk] - mean[k])/ meansquare_sigma[k] , w_half)
return
cdef updraft_indicator_sc_w_ql(Grid.DimStruct *dims, double *tracer_raw, double *tracer_normed, double *mean,
double *meansquare_sigma, double *w, double *ql, double *z_half, double z_cb, double z_ct):
cdef:
Py_ssize_t istride = dims.nlg[1] * dims.nlg[2]
Py_ssize_t jstride = dims.nlg[2]
Py_ssize_t ishift, jshift, ijk, i,j,k
double sigma_min
double sigma_sum = 0.0
double z_ql = z_cb + 0.25 * (z_ct - z_cb)
double w_half
with nogil:
for k in xrange(dims.nlg[2]):
meansquare_sigma[k] = meansquare_sigma[k] - mean[k] * mean[k]
meansquare_sigma[k] = sqrt(fmax(meansquare_sigma[k],0.0))
sigma_sum += meansquare_sigma[k]
sigma_min = sigma_sum/(k+1.0) * 0.05
if meansquare_sigma[k] < sigma_min:
for i in xrange(dims.nlg[0]):
ishift = i*istride
for j in xrange(dims.nlg[1]):
jshift = j*jstride
ijk = ishift + jshift + k
tracer_normed[ijk] = 0.0
else:
for i in xrange(dims.nlg[0]):
ishift = i*istride
for j in xrange(dims.nlg[1]):
jshift = j*jstride
ijk = ishift + jshift + k
w_half = 0.5*(w[ijk-1] + w[ijk])
tracer_normed[ijk] = copysign( (tracer_raw[ijk] - mean[k])/ meansquare_sigma[k], w_half)
if z_ct > z_cb:
with nogil:
for k in xrange(dims.nlg[2]):
if z_half[k] >= z_ql and z_half[k] <= z_ct:
for i in xrange(dims.nlg[0]):
for j in xrange(dims.nlg[1]):
ijk = i * istride + j * jstride + k