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ScalarAdvection.pyx
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ScalarAdvection.pyx
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#!python
#cython: boundscheck=False
#cython: wraparound=False
#cython: initializedcheck=False
#cython: cdivision=True
cimport Grid
cimport PrognosticVariables
cimport ParallelMPI
cimport ReferenceState
cimport DiagnosticVariables
cimport TimeStepping
from NetCDFIO cimport NetCDFIO_Stats
from FluxDivergence cimport scalar_flux_divergence
from Thermodynamics cimport LatentHeat
import numpy as np
cimport numpy as np
import cython
cdef extern from "scalar_advection.h":
void compute_advective_fluxes_a(Grid.DimStruct *dims, double *rho0, double *rho0_half, double *velocity,
double *scalar, double* flux, int d, int scheme) nogil
void compute_qt_sedimentation_s_source(Grid.DimStruct *dims, double *p0_half, double* rho0_half, double *flux,
double* qt, double* qv, double* T, double* tendency, double (*lam_fp)(double),
double (*L_fp)(double, double), double dx, Py_ssize_t d)nogil
cdef class ScalarAdvection:
def __init__(self, namelist, LatentHeat LH, ParallelMPI.ParallelMPI Pa):
self.L_fp = LH.L_fp
self.Lambda_fp = LH.Lambda_fp
try:
self.order = namelist['scalar_transport']['order']
except:
Pa.root_print('scalar_transport order not given in namelist')
Pa.root_print('Killing simulation now!')
Pa.kill()
Pa.kill()
try:
self.order_sedimentation = namelist['scalar_transport']['order_sedimentation']
except:
self.order_sedimentation = self.order
return
cpdef initialize(self,Grid.Grid Gr, PrognosticVariables.PrognosticVariables PV, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
self.flux = np.zeros((PV.nv_scalars*Gr.dims.npg*Gr.dims.dims,),dtype=np.double,order='c')
#Initialize output fields
for i in xrange(PV.nv):
if PV.var_type[i] == 1:
NS.add_profile(PV.index_name[i] + '_flux_z',Gr,Pa)
NS.add_profile('s_flux_z_tendency',Gr,Pa)
NS.add_profile('qt_flux_z_tendency',Gr,Pa)
NS.add_profile('s_flux_xy_tendency',Gr,Pa)
NS.add_profile('qt_flux_xy_tendency',Gr,Pa)
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState Rs,PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV, ParallelMPI.ParallelMPI Pa):
cdef:
Py_ssize_t d, i, vel_shift,scalar_shift, scalar_count = 0, flux_shift
Py_ssize_t s_shift = PV.get_varshift(Gr,'s')
Py_ssize_t t_shift = DV.get_varshift(Gr,'temperature')
Py_ssize_t ql_shift, qv_shift, qt_shift
for i in xrange(PV.nv): #Loop over the prognostic variables
if PV.var_type[i] == 1: #Only compute advection if variable i is a scalar
scalar_shift = i * Gr.dims.npg
#No rescaling of fluxes
for d in xrange(Gr.dims.dims): #Loop over the cardinal direction
#The flux has a different shift since it is only for the scalars
flux_shift = scalar_count * (Gr.dims.dims * Gr.dims.npg) + d * Gr.dims.npg
#Make sure that we get the velocity components in the correct order
#Check for a scalar-specific velocity
sc_vel_name = PV.velocity_names_directional[d] + '_' + PV.index_name[i]
if sc_vel_name in DV.name_index:
vel_shift = DV.get_varshift(Gr, sc_vel_name)
if sc_vel_name == 'w_qt':
ql_shift = DV.get_varshift(Gr,'ql')
qt_shift = PV.get_varshift(Gr,'qt')
qv_shift = DV.get_varshift(Gr,'qv')
compute_advective_fluxes_a(&Gr.dims,&Rs.rho0[0],&Rs.rho0_half[0],&DV.values[vel_shift],
&DV.values[ql_shift],&self.flux[flux_shift],d,self.order_sedimentation)
scalar_flux_divergence(&Gr.dims,&Rs.alpha0[0],&Rs.alpha0_half[0],&self.flux[flux_shift],
&PV.tendencies[scalar_shift],Gr.dims.dx[d],d)
compute_qt_sedimentation_s_source(&Gr.dims, &Rs.p0_half[0], &Rs.rho0_half[0], &self.flux[flux_shift],
&PV.values[qt_shift], &DV.values[qv_shift], &DV.values[t_shift],
&PV.tendencies[s_shift], self.Lambda_fp,self.L_fp, Gr.dims.dx[d],d)
else:
# print(sc_vel_name, ' detected as sedimentation velocity')
#First get the tendency associated with the sedimentation velocity
compute_advective_fluxes_a(&Gr.dims,&Rs.rho0[0],&Rs.rho0_half[0],&DV.values[vel_shift],
&PV.values[scalar_shift],&self.flux[flux_shift],d,self.order_sedimentation)
scalar_flux_divergence(&Gr.dims,&Rs.alpha0[0],&Rs.alpha0_half[0],&self.flux[flux_shift],
&PV.tendencies[scalar_shift],Gr.dims.dx[d],d)
# now the advective flux for all scalars
vel_shift = PV.velocity_directions[d]*Gr.dims.npg
compute_advective_fluxes_a(&Gr.dims,&Rs.rho0[0],&Rs.rho0_half[0],&PV.values[vel_shift],
&PV.values[scalar_shift],&self.flux[flux_shift],d,self.order)
scalar_flux_divergence(&Gr.dims,&Rs.alpha0[0],&Rs.alpha0_half[0],&self.flux[flux_shift],
&PV.tendencies[scalar_shift],Gr.dims.dx[d],d)
scalar_count += 1
return
cpdef stats_io(self, Grid.Grid Gr, ReferenceState.ReferenceState RS, PrognosticVariables.PrognosticVariables PV, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
cdef:
Py_ssize_t scalar_count = 0, i, d = 2, flux_shift, k
double[:] tmp
double [:] tmp_interp = np.zeros(Gr.dims.nlg[2],dtype=np.double,order='c')
double [:] tmp_tendency = np.zeros(Gr.dims.npg,dtype=np.double,order='c')
double [:] mean_tendency = np.zeros(Gr.dims.nlg[2],dtype=np.double,order='c')
for i in xrange(PV.nv):
if PV.var_type[i] == 1:
flux_shift = scalar_count * (Gr.dims.dims * Gr.dims.npg) + d * Gr.dims.npg
tmp = Pa.HorizontalMean(Gr, &self.flux[flux_shift])
for k in xrange(Gr.dims.gw,Gr.dims.nlg[2]-Gr.dims.gw):
tmp_interp[k] = 0.5*(tmp[k-1]+tmp[k])
NS.write_profile(PV.index_name[i] + '_flux_z', tmp_interp[Gr.dims.gw:-Gr.dims.gw], Pa)
if PV.index_name[i] == 's':
tmp_tendency[:] = 0.0
scalar_flux_divergence(&Gr.dims, &RS.alpha0[0], &RS.alpha0_half[0], &self.flux[flux_shift],
&tmp_tendency[0], Gr.dims.dx[d], d)
mean_tendency = Pa.HorizontalMean(Gr, &tmp_tendency[0])
NS.write_profile(PV.index_name[i] + '_flux_z_tendency', mean_tendency[Gr.dims.gw:-Gr.dims.gw], Pa)
#Now get x direction tendency
tmp_tendency[:] = 0.0
flux_shift = scalar_count * (Gr.dims.dims * Gr.dims.npg) + 0 * Gr.dims.npg
scalar_flux_divergence(&Gr.dims, &RS.alpha0[0], &RS.alpha0_half[0], &self.flux[flux_shift],
&tmp_tendency[0], Gr.dims.dx[0], 0)
#Next y direction
flux_shift = scalar_count * (Gr.dims.dims * Gr.dims.npg) + 1 * Gr.dims.npg
scalar_flux_divergence(&Gr.dims, &RS.alpha0[0], &RS.alpha0_half[0], &self.flux[flux_shift],
&tmp_tendency[0], Gr.dims.dx[1], 1)
mean_tendency = Pa.HorizontalMean(Gr, &tmp_tendency[0])
NS.write_profile(PV.index_name[i] + '_flux_xy_tendency', mean_tendency[Gr.dims.gw:-Gr.dims.gw], Pa)
elif PV.index_name[i] == 'qt':
tmp_tendency[:] = 0.0
scalar_flux_divergence(&Gr.dims, &RS.alpha0[0], &RS.alpha0_half[0], &self.flux[flux_shift],
&tmp_tendency[0], Gr.dims.dx[d], d)
mean_tendency = Pa.HorizontalMean(Gr, &tmp_tendency[0])
NS.write_profile(PV.index_name[i] + '_flux_z_tendency', mean_tendency[Gr.dims.gw:-Gr.dims.gw], Pa)
#Now get x direction tendency
tmp_tendency[:] = 0.0
flux_shift = scalar_count * (Gr.dims.dims * Gr.dims.npg) + 0 * Gr.dims.npg
scalar_flux_divergence(&Gr.dims, &RS.alpha0[0], &RS.alpha0_half[0], &self.flux[flux_shift],
&tmp_tendency[0], Gr.dims.dx[0], 0)
#Next y direction
flux_shift = scalar_count * (Gr.dims.dims * Gr.dims.npg) + 1 * Gr.dims.npg
scalar_flux_divergence(&Gr.dims, &RS.alpha0[0], &RS.alpha0_half[0], &self.flux[flux_shift],
&tmp_tendency[0], Gr.dims.dx[1], 1)
mean_tendency = Pa.HorizontalMean(Gr, &tmp_tendency[0])
NS.write_profile(PV.index_name[i] + '_flux_xy_tendency', mean_tendency[Gr.dims.gw:-Gr.dims.gw], Pa)
scalar_count += 1
return