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Surface.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 Thermodynamics cimport LatentHeat,ClausiusClapeyron
from NetCDFIO cimport NetCDFIO_Stats
import cython
from thermodynamic_functions import exner, cpm
from libc.math cimport sqrt, log, fabs,atan, exp, fmax
cimport numpy as np
import numpy as np
include "parameters.pxi"
import cython
cdef extern from "advection_interpolation.h":
double interp_2(double phi, double phip1) nogil
cdef extern from "thermodynamic_functions.h":
inline double pd_c(double p0, double qt, double qv) nogil
inline double pv_c(double p0, double qt, double qv) nogil
inline double exner_c(const double p0) nogil
inline double theta_rho_c(double p0, double T,double qt, double qv) nogil
cdef extern from "surface.h":
double compute_ustar(double windspeed, double buoyancy_flux, double z0, double z1) nogil
inline double entropyflux_from_thetaflux_qtflux(double thetaflux, double qtflux, double p0_b, double T_b, double qt_b, double qv_b) nogil
void compute_windspeed(Grid.DimStruct *dims, double* u, double* v, double* speed, double u0, double v0, double gustiness ) nogil
void exchange_coefficients_byun(double Ri, double zb, double z0, double* cm, double* ch, double* lmo) nogil
cdef extern from "entropies.h":
inline double sd_c(double pd, double T) nogil
inline double sv_c(double pv, double T) nogil
cdef class Surface:
def __init__(self,namelist, LatentHeat LH, ParallelMPI.ParallelMPI Par):
casename = namelist['meta']['casename']
if casename == 'SullivanPatton':
self.scheme = SurfaceSullivanPatton()
elif casename == 'Bomex':
self.scheme = SurfaceBomex()
elif casename == 'VARF':
self.scheme = SurfaceVARF(namelist)
elif casename == 'Gabls':
self.scheme = SurfaceGabls(namelist)
elif casename == 'DYCOMS_RF01':
self.scheme = SurfaceDYCOMS_RF01(namelist, LH)
elif casename == 'DYCOMS_RF02':
self.scheme = SurfaceDYCOMS_RF02(namelist, LH)
elif casename == 'Rico':
self.scheme= SurfaceRico()
elif casename == 'VARRico':
self.scheme= SurfaceVARRico(namelist)
else:
self.scheme= SurfaceNone()
return
cpdef initialize(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref, DiagnosticVariables.DiagnosticVariables DV, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
DV.add_variables_2d('obukhov_length', 'm')
DV.add_variables_2d('friction_velocity', 'm/s')
self.scheme.initialize(Gr, Ref, NS, Pa)
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref, PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV, ParallelMPI.ParallelMPI Pa, TimeStepping.TimeStepping TS):
self.scheme.update(Gr, Ref, PV, DV, Pa, TS)
return
cpdef stats_io(self, Grid.Grid Gr, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
self.scheme.stats_io(Gr, NS, Pa)
return
cdef class SurfaceNone:
def __init__(self):
pass
cpdef initialize(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
return
cpdef update(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, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
return
cdef class SurfaceSullivanPatton:
def __init__(self):
self.theta_flux = 0.24 # K m/s
self.z0 = 0.1 #m (Roughness length)
self.gustiness = 0.001 #m/s, minimum surface windspeed for determination of u*
return
cpdef initialize(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
T0 = Ref.p0_half[Gr.dims.gw] * Ref.alpha0_half[Gr.dims.gw]/Rd
self.buoyancy_flux = self.theta_flux * exner(Ref.p0[Gr.dims.gw-1]) * g /T0
self.s_flux = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
self.u_flux = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
self.v_flux = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
NS.add_ts('uw_surface_mean',Gr, Pa)
NS.add_ts('vw_surface_mean',Gr, Pa)
NS.add_ts('s_flux_surface_mean', Gr, Pa)
return
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.cdivision(True)
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref, PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV, ParallelMPI.ParallelMPI Pa, TimeStepping.TimeStepping TS):
# Since this case is completely dry, the computation of entropy flux from sensible heat flux is very simple
if Pa.sub_z_rank != 0:
return
cdef:
Py_ssize_t i
Py_ssize_t j
Py_ssize_t gw = Gr.dims.gw
Py_ssize_t ijk, ij
Py_ssize_t imax = Gr.dims.nlg[0]
Py_ssize_t jmax = Gr.dims.nlg[1]
Py_ssize_t istride = Gr.dims.nlg[1] * Gr.dims.nlg[2]
Py_ssize_t jstride = Gr.dims.nlg[2]
Py_ssize_t istride_2d = Gr.dims.nlg[1]
Py_ssize_t temp_shift = DV.get_varshift(Gr, 'temperature')
Py_ssize_t s_shift = PV.get_varshift(Gr, 's')
double dzi = 1.0/Gr.dims.dx[2]
double tendency_factor = Ref.alpha0_half[gw]/Ref.alpha0[gw-1]*dzi
#Get the scalar flux (dry entropy only)
with nogil:
for i in xrange(imax):
for j in xrange(jmax):
ijk = i * istride + j * jstride + gw
ij = i * istride_2d + j
self.s_flux[ij] = cpd * self.theta_flux*exner_c(Ref.p0_half[gw])/DV.values[temp_shift+ijk]
PV.tendencies[s_shift + ijk] = PV.tendencies[s_shift + ijk] + self.s_flux[ij] * tendency_factor
cdef:
Py_ssize_t u_shift = PV.get_varshift(Gr, 'u')
Py_ssize_t v_shift = PV.get_varshift(Gr, 'v')
double [:] windspeed = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1],dtype=np.double,order='c')
compute_windspeed(&Gr.dims, &PV.values[u_shift], &PV.values[v_shift], &windspeed[0],Ref.u0, Ref.v0,self.gustiness)
cdef :
Py_ssize_t lmo_shift = DV.get_varshift_2d(Gr, 'obukhov_length')
Py_ssize_t ustar_shift = DV.get_varshift_2d(Gr, 'friction_velocity')
# Get the shear stresses
with nogil:
for i in xrange(1,imax):
for j in xrange(1,jmax):
ij = i * istride_2d + j
DV.values_2d[ustar_shift + ij] = compute_ustar(windspeed[ij],self.buoyancy_flux,self.z0, Gr.dims.dx[2]/2.0)
DV.values_2d[lmo_shift + ij] = -DV.values_2d[ustar_shift + ij]*DV.values_2d[ustar_shift + ij]*DV.values_2d[ustar_shift + ij]/self.buoyancy_flux/vkb
for i in xrange(1,imax-1):
for j in xrange(1,jmax-1):
ijk = i * istride + j * jstride + gw
ij = i * istride_2d + j
self.u_flux[ij] = -interp_2(DV.values_2d[ustar_shift + ij], DV.values_2d[ustar_shift+ij+istride_2d])**2/interp_2(windspeed[ij], windspeed[ij+istride_2d]) * (PV.values[u_shift + ijk] + Ref.u0)
self.v_flux[ij] = -interp_2(DV.values_2d[ustar_shift + ij], DV.values_2d[ustar_shift+ij+1])**2/interp_2(windspeed[ij], windspeed[ij+1]) * (PV.values[v_shift + ijk] + Ref.v0)
PV.tendencies[u_shift + ijk] += self.u_flux[ij] * tendency_factor
PV.tendencies[v_shift + ijk] += self.v_flux[ij] * tendency_factor
return
cpdef stats_io(self, Grid.Grid Gr, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
cdef double tmp
tmp = Pa.HorizontalMeanSurface(Gr, &self.u_flux[0])
NS.write_ts('uw_surface_mean',tmp, Pa)
tmp = Pa.HorizontalMeanSurface(Gr, &self.v_flux[0])
NS.write_ts('vw_surface_mean', tmp, Pa)
tmp = Pa.HorizontalMeanSurface(Gr, &self.s_flux[0])
NS.write_ts('s_flux_surface_mean', tmp, Pa)
return
cdef class SurfaceBomex:
def __init__(self):
self.theta_flux = 8.0e-3 # K m/s
self.qt_flux = 5.2e-5 # m/s
self.ustar_ = 0.28 #m/s
self.theta_surface = 299.1 #K
self.qt_surface = 22.45e-3 # kg/kg
self.buoyancy_flux = g * ((self.theta_flux + (eps_vi-1.0)*(self.theta_surface*self.qt_flux + self.qt_surface *self.theta_flux))
/(self.theta_surface*(1.0 + (eps_vi-1)*self.qt_surface)))
return
@cython.boundscheck(False) #Turn off numpy array index bounds checking
@cython.wraparound(False) #Turn off numpy array wrap around indexing
@cython.cdivision(True)
cpdef initialize(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
self.u_flux = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
self.v_flux = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
self.s_flux = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
NS.add_ts('uw_surface_mean', Gr, Pa)
NS.add_ts('vw_surface_mean', Gr, Pa)
NS.add_ts('s_flux_surface_mean', Gr, Pa)
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref, PrognosticVariables.PrognosticVariables PV,DiagnosticVariables.DiagnosticVariables DV, ParallelMPI.ParallelMPI Pa, TimeStepping.TimeStepping TS):
if Pa.sub_z_rank != 0:
return
cdef :
Py_ssize_t lmo_shift = DV.get_varshift_2d(Gr, 'obukhov_length')
Py_ssize_t ustar_shift = DV.get_varshift_2d(Gr, 'friction_velocity')
Py_ssize_t i
Py_ssize_t j
Py_ssize_t gw = Gr.dims.gw
Py_ssize_t ijk, ij
Py_ssize_t imax = Gr.dims.nlg[0]
Py_ssize_t jmax = Gr.dims.nlg[1]
Py_ssize_t istride = Gr.dims.nlg[1] * Gr.dims.nlg[2]
Py_ssize_t jstride = Gr.dims.nlg[2]
Py_ssize_t istride_2d = Gr.dims.nlg[1]
Py_ssize_t temp_shift = DV.get_varshift(Gr, 'temperature')
Py_ssize_t s_shift = PV.get_varshift(Gr, 's')
Py_ssize_t qt_shift = PV.get_varshift(Gr, 'qt')
Py_ssize_t qv_shift = DV.get_varshift(Gr,'qv')
double dzi = 1.0/Gr.dims.dx[2]
double tendency_factor = Ref.alpha0_half[gw]/Ref.alpha0[gw-1]*dzi
# Get the scalar flux
with nogil:
for i in xrange(imax):
for j in xrange(jmax):
ijk = i * istride + j * jstride + gw
ij = i * istride_2d + j
DV.values_2d[ustar_shift + ij] = self.ustar_
DV.values_2d[lmo_shift + ij] = -self.ustar_*self.ustar_*self.ustar_/self.buoyancy_flux/vkb
self.s_flux[ij] = entropyflux_from_thetaflux_qtflux(self.theta_flux, self.qt_flux, Ref.p0_half[gw], DV.values[temp_shift+ijk], PV.values[qt_shift+ijk], DV.values[qv_shift+ijk])
PV.tendencies[s_shift + ijk] += self.s_flux[ij] * tendency_factor
PV.tendencies[qt_shift + ijk] += self.qt_flux * tendency_factor
cdef:
Py_ssize_t u_shift = PV.get_varshift(Gr, 'u')
Py_ssize_t v_shift = PV.get_varshift(Gr, 'v')
double [:] windspeed = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
compute_windspeed(&Gr.dims, &PV.values[u_shift], &PV.values[v_shift], &windspeed[0], Ref.u0, Ref.v0, self.gustiness)
# Get the shear stresses
with nogil:
for i in xrange(1,imax-1):
for j in xrange(1,jmax-1):
ijk = i * istride + j * jstride + gw
ij = i * istride_2d + j
self.u_flux[ij] = -self.ustar_**2/interp_2(windspeed[ij], windspeed[ij+istride_2d]) * (PV.values[u_shift + ijk] + Ref.u0)
self.v_flux[ij] = -self.ustar_**2/interp_2(windspeed[ij], windspeed[ij+1]) * (PV.values[v_shift + ijk] + Ref.v0)
PV.tendencies[u_shift + ijk] += self.u_flux[ij] * tendency_factor
PV.tendencies[v_shift + ijk] += self.v_flux[ij] * tendency_factor
return
cpdef stats_io(self, Grid.Grid Gr, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
cdef double tmp
tmp = Pa.HorizontalMeanSurface(Gr, &self.u_flux[0])
NS.write_ts('uw_surface_mean', tmp, Pa)
tmp = Pa.HorizontalMeanSurface(Gr,&self.v_flux[0])
NS.write_ts('vw_surface_mean', tmp, Pa)
tmp = Pa.HorizontalMeanSurface(Gr,&self.s_flux[0])
NS.write_ts('s_flux_surface_mean', tmp, Pa)
return
cdef class SurfaceVARF:
def __init__(self,namelist):
self.theta_flux = namelist['thermo']['theta_flux'] # K m/s
self.qt_flux = namelist['thermo']['qt_flux'] # m/s
self.ustar_ = 0.28 #m/s
self.theta_surface = namelist['thermo']['theta_surface'] #K
self.qt_surface = namelist['thermo']['qt_surface'] # kg/kg
self.buoyancy_flux = g * ((self.theta_flux + (eps_vi-1.0)*(self.theta_surface*self.qt_flux + self.qt_surface *self.theta_flux))
/(self.theta_surface*(1.0 + (eps_vi-1)*self.qt_surface)))
#pass
return
@cython.boundscheck(False) #Turn off numpy array index bounds checking
@cython.wraparound(False) #Turn off numpy array wrap around indexing
@cython.cdivision(True)
cpdef initialize(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
self.u_flux = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
self.v_flux = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
self.s_flux = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
NS.add_ts('uw_surface_mean', Gr, Pa)
NS.add_ts('vw_surface_mean', Gr, Pa)
NS.add_ts('s_flux_surface_mean', Gr, Pa)
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref, PrognosticVariables.PrognosticVariables PV,DiagnosticVariables.DiagnosticVariables DV, ParallelMPI.ParallelMPI Pa, TimeStepping.TimeStepping TS):
if Pa.sub_z_rank != 0:
return
cdef :
Py_ssize_t lmo_shift = DV.get_varshift_2d(Gr, 'obukhov_length')
Py_ssize_t ustar_shift = DV.get_varshift_2d(Gr, 'friction_velocity')
Py_ssize_t i
Py_ssize_t j
Py_ssize_t gw = Gr.dims.gw
Py_ssize_t ijk, ij
Py_ssize_t imax = Gr.dims.nlg[0]
Py_ssize_t jmax = Gr.dims.nlg[1]
Py_ssize_t istride = Gr.dims.nlg[1] * Gr.dims.nlg[2]
Py_ssize_t jstride = Gr.dims.nlg[2]
Py_ssize_t istride_2d = Gr.dims.nlg[1]
Py_ssize_t temp_shift = DV.get_varshift(Gr, 'temperature')
Py_ssize_t s_shift = PV.get_varshift(Gr, 's')
Py_ssize_t qt_shift = PV.get_varshift(Gr, 'qt')
Py_ssize_t qv_shift = DV.get_varshift(Gr,'qv')
double dzi = 1.0/Gr.dims.dx[2]
double tendency_factor = Ref.alpha0_half[gw]/Ref.alpha0[gw-1]*dzi
# Get the scalar flux
with nogil:
for i in xrange(imax):
for j in xrange(jmax):
ijk = i * istride + j * jstride + gw
ij = i * istride_2d + j
DV.values_2d[ustar_shift + ij] = self.ustar_
DV.values_2d[lmo_shift + ij] = -self.ustar_*self.ustar_*self.ustar_/self.buoyancy_flux/vkb
self.s_flux[ij] = entropyflux_from_thetaflux_qtflux(self.theta_flux, self.qt_flux, Ref.p0_half[gw], DV.values[temp_shift+ijk], PV.values[qt_shift+ijk], DV.values[qv_shift+ijk])
PV.tendencies[s_shift + ijk] += self.s_flux[ij] * tendency_factor
PV.tendencies[qt_shift + ijk] += self.qt_flux * tendency_factor
cdef:
Py_ssize_t u_shift = PV.get_varshift(Gr, 'u')
Py_ssize_t v_shift = PV.get_varshift(Gr, 'v')
double [:] windspeed = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
compute_windspeed(&Gr.dims, &PV.values[u_shift], &PV.values[v_shift], &windspeed[0], Ref.u0, Ref.v0, self.gustiness)
# Get the shear stresses
with nogil:
for i in xrange(1,imax-1):
for j in xrange(1,jmax-1):
ijk = i * istride + j * jstride + gw
ij = i * istride_2d + j
self.u_flux[ij] = -self.ustar_**2/interp_2(windspeed[ij], windspeed[ij+istride_2d]) * (PV.values[u_shift + ijk] + Ref.u0)
self.v_flux[ij] = -self.ustar_**2/interp_2(windspeed[ij], windspeed[ij+1]) * (PV.values[v_shift + ijk] + Ref.v0)
PV.tendencies[u_shift + ijk] += self.u_flux[ij] * tendency_factor
PV.tendencies[v_shift + ijk] += self.v_flux[ij] * tendency_factor
return
cpdef stats_io(self, Grid.Grid Gr, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
cdef double tmp
tmp = Pa.HorizontalMeanSurface(Gr, &self.u_flux[0])
NS.write_ts('uw_surface_mean', tmp, Pa)
tmp = Pa.HorizontalMeanSurface(Gr,&self.v_flux[0])
NS.write_ts('vw_surface_mean', tmp, Pa)
tmp = Pa.HorizontalMeanSurface(Gr,&self.s_flux[0])
NS.write_ts('s_flux_surface_mean', tmp, Pa)
return
cdef class SurfaceGabls:
def __init__(self, namelist):
self.gustiness = 0.001
self.z0 = 0.1
# Rate of change of surface temperature, in K/hour
# GABLS1 IC (Beare et al) value is 0.25 (given as default)
try:
self.cooling_rate = namelist['surface']['cooling_rate']
except:
self.cooling_rate = 0.25
return
cpdef initialize(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
self.u_flux = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1],dtype=np.double,order='c')
self.v_flux = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1],dtype=np.double,order='c')
self.b_flux = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1],dtype=np.double,order='c')
self.s_flux = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1],dtype=np.double,order='c')
NS.add_ts('uw_surface_mean', Gr, Pa)
NS.add_ts('vw_surface_mean', Gr, Pa)
NS.add_ts('s_flux_surface_mean', Gr, Pa)
NS.add_ts('b_flux_surface_mean', Gr, Pa)
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref, PrognosticVariables.PrognosticVariables PV,DiagnosticVariables.DiagnosticVariables DV, ParallelMPI.ParallelMPI Pa, TimeStepping.TimeStepping TS):
if Pa.sub_z_rank != 0:
return
cdef:
Py_ssize_t u_shift = PV.get_varshift(Gr, 'u')
Py_ssize_t v_shift = PV.get_varshift(Gr, 'v')
Py_ssize_t s_shift = PV.get_varshift(Gr, 's')
Py_ssize_t t_shift = DV.get_varshift(Gr, 'temperature')
Py_ssize_t th_shift = DV.get_varshift(Gr, 'theta')
double [:] windspeed = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
compute_windspeed(&Gr.dims, &PV.values[u_shift], &PV.values[v_shift], &windspeed[0], Ref.u0, Ref.v0, self.gustiness)
cdef:
Py_ssize_t i,j, ijk, ij
Py_ssize_t gw = Gr.dims.gw
Py_ssize_t imax = Gr.dims.nlg[0]
Py_ssize_t jmax = Gr.dims.nlg[1]
Py_ssize_t istride = Gr.dims.nlg[1] * Gr.dims.nlg[2]
Py_ssize_t jstride = Gr.dims.nlg[2]
Py_ssize_t istride_2d = Gr.dims.nlg[1]
double theta_rho_b, Nb2, Ri
double zb = Gr.dims.dx[2] * 0.5
double [:] cm= np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
double ch=0.0
double sst = 265.0 - self.cooling_rate * TS.t/3600.0 # sst = theta_surface also
double theta_rho_g = theta_rho_c(Ref.Pg, sst, 0.0, 0.0)
double s_star = sd_c(Ref.Pg,sst)
double tendency_factor = Ref.alpha0_half[gw]/Ref.alpha0[gw-1]/Gr.dims.dx[2]
cdef :
Py_ssize_t lmo_shift = DV.get_varshift_2d(Gr, 'obukhov_length')
Py_ssize_t ustar_shift = DV.get_varshift_2d(Gr, 'friction_velocity')
with nogil:
for i in xrange(gw-1, imax-gw+1):
for j in xrange(gw-1,jmax-gw+1):
ijk = i * istride + j * jstride + gw
ij = i * istride_2d + j
theta_rho_b = DV.values[th_shift + ijk]
Nb2 = g/theta_rho_g*(theta_rho_b-theta_rho_g)/zb
Ri = Nb2 * zb* zb/(windspeed[ij] * windspeed[ij])
exchange_coefficients_byun(Ri,zb,self.z0, &cm[ij], &ch, &DV.values_2d[lmo_shift + ij])
self.s_flux[ij] = -ch * windspeed[ij] * (PV.values[s_shift+ijk] - s_star)
self.b_flux[ij] = -ch * windspeed[ij] * (DV.values[th_shift+ijk] - sst)*9.81/263.5
DV.values_2d[ustar_shift + ij] = sqrt(cm[ij]) * windspeed[ij]
for i in xrange(gw, imax-gw):
for j in xrange(gw, jmax-gw):
ijk = i * istride + j * jstride + gw
ij = i * istride_2d + j
self.u_flux[ij] = -interp_2(cm[ij], cm[ij+istride_2d])*interp_2(windspeed[ij], windspeed[ij+istride_2d]) * (PV.values[u_shift + ijk] + Ref.u0)
self.v_flux[ij] = -interp_2(cm[ij], cm[ij+1])*interp_2(windspeed[ij], windspeed[ij+1]) * (PV.values[v_shift + ijk] + Ref.v0)
PV.tendencies[u_shift + ijk] += self.u_flux[ij] * tendency_factor
PV.tendencies[v_shift + ijk] += self.v_flux[ij] * tendency_factor
PV.tendencies[s_shift + ijk] += self.s_flux[ij] * tendency_factor
return
cpdef stats_io(self, Grid.Grid Gr, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
cdef double tmp
tmp = Pa.HorizontalMeanSurface(Gr, &self.u_flux[0])
NS.write_ts('uw_surface_mean', tmp, Pa)
tmp = Pa.HorizontalMeanSurface(Gr, &self.v_flux[0])
NS.write_ts('vw_surface_mean', tmp, Pa)
tmp = Pa.HorizontalMeanSurface(Gr, &self.s_flux[0])
NS.write_ts('s_flux_surface_mean', tmp, Pa)
tmp = Pa.HorizontalMeanSurface(Gr,&self.b_flux[0])
NS.write_ts('b_flux_surface_mean', tmp, Pa)
return
cdef class SurfaceDYCOMS_RF01:
def __init__(self,namelist, LatentHeat LH):
self.ft = 15.0
self.fq = 115.0
self.gustiness = 0.0
self.cm = 0.0011
self.L_fp = LH.L_fp
self.Lambda_fp = LH.Lambda_fp
sst = 292.5 # K
psurface = 1017.8e2 # Pa
theta_surface = sst/exner(psurface)
qt_surface = 13.84e-3 # qs(sst) using Teten's formula
density_surface = 1.22 #kg/m^3
theta_flux = self.ft/(density_surface*cpm(qt_surface)*exner(psurface))
qt_flux_ = self.fq/self.L_fp(sst,self.Lambda_fp(sst))
self.buoyancy_flux = g * ((theta_flux + (eps_vi-1.0)*(theta_surface*qt_flux_ + qt_surface * theta_flux))
/(theta_surface*(1.0 + (eps_vi-1)*qt_surface)))
cpdef initialize(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
self.windspeed = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
self.u_flux = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
self.v_flux = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
self.qt_flux = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
self.s_flux = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
NS.add_ts('uw_surface_mean', Gr, Pa)
NS.add_ts('vw_surface_mean', Gr, Pa)
NS.add_ts('s_flux_surface_mean', Gr, Pa)
NS.add_ts('qt_flux_surface_mean', Gr, Pa)
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref, PrognosticVariables.PrognosticVariables PV,DiagnosticVariables.DiagnosticVariables DV, ParallelMPI.ParallelMPI Pa, TimeStepping.TimeStepping TS):
if Pa.sub_z_rank != 0:
return
cdef:
Py_ssize_t u_shift = PV.get_varshift(Gr, 'u')
Py_ssize_t v_shift = PV.get_varshift(Gr, 'v')
Py_ssize_t s_shift = PV.get_varshift(Gr, 's')
Py_ssize_t qt_shift = PV.get_varshift(Gr, 'qt')
Py_ssize_t t_shift = DV.get_varshift(Gr, 'temperature')
Py_ssize_t ql_shift = DV.get_varshift(Gr, 'ql')
compute_windspeed(&Gr.dims, &PV.values[u_shift], &PV.values[v_shift], &self.windspeed[0],Ref.u0, Ref.v0,self.gustiness)
cdef:
Py_ssize_t i,j, ijk, ij
Py_ssize_t gw = Gr.dims.gw
Py_ssize_t imax = Gr.dims.nlg[0]
Py_ssize_t jmax = Gr.dims.nlg[1]
Py_ssize_t istride = Gr.dims.nlg[1] * Gr.dims.nlg[2]
Py_ssize_t jstride = Gr.dims.nlg[2]
Py_ssize_t istride_2d = Gr.dims.nlg[1]
double tendency_factor = Ref.alpha0_half[gw]/Ref.alpha0[gw-1]/Gr.dims.dx[2]
double lam
double lv
double pv
double pd
double sv
double sd
double [:] windspeed = self.windspeed
Py_ssize_t lmo_shift = DV.get_varshift_2d(Gr, 'obukhov_length')
Py_ssize_t ustar_shift = DV.get_varshift_2d(Gr, 'friction_velocity')
with nogil:
for i in xrange(gw-1, imax-gw+1):
for j in xrange(gw-1, jmax-gw+1):
ijk = i * istride + j * jstride + gw
ij = i * istride_2d + j
DV.values_2d[ustar_shift + ij] = sqrt(self.cm) * self.windspeed[ij]
DV.values_2d[lmo_shift + ij] = -DV.values_2d[ustar_shift + ij]**3.0/self.buoyancy_flux/vkb
lam = self.Lambda_fp(DV.values[t_shift+ijk])
lv = self.L_fp(DV.values[t_shift+ijk],lam)
pv = pv_c(Ref.p0_half[gw], PV.values[ijk + qt_shift], PV.values[ijk + qt_shift] - DV.values[ijk + ql_shift])
pd = pd_c(Ref.p0_half[gw], PV.values[ijk + qt_shift], PV.values[ijk + qt_shift] - DV.values[ijk + ql_shift])
sv = sv_c(pv,DV.values[t_shift+ijk])
sd = sd_c(pd,DV.values[t_shift+ijk])
self.qt_flux[ij] = self.fq / lv / 1.22
self.s_flux[ij] = Ref.alpha0_half[gw] * (self.ft/DV.values[t_shift+ijk] + self.fq*(sv - sd)/lv)
for i in xrange(gw, imax-gw):
for j in xrange(gw, jmax-gw):
ijk = i * istride + j * jstride + gw
ij = i * istride_2d + j
self.u_flux[ij] = -self.cm * interp_2(windspeed[ij], windspeed[ij+istride_2d]) * (PV.values[u_shift + ijk] + Ref.u0)
self.v_flux[ij] = -self.cm * interp_2(windspeed[ij], windspeed[ij+1]) * (PV.values[v_shift + ijk] + Ref.v0)
PV.tendencies[u_shift + ijk] += self.u_flux[ij] * tendency_factor
PV.tendencies[v_shift + ijk] += self.v_flux[ij] * tendency_factor
PV.tendencies[s_shift + ijk] += self.s_flux[ij] * tendency_factor
PV.tendencies[qt_shift + ijk] += self.qt_flux[ij] * tendency_factor
cpdef stats_io(self, Grid.Grid Gr, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
cdef double tmp
tmp = Pa.HorizontalMeanSurface(Gr, &self.u_flux[0])
NS.write_ts('uw_surface_mean',tmp, Pa)
tmp = Pa.HorizontalMeanSurface(Gr,&self.v_flux[0])
NS.write_ts('vw_surface_mean', tmp, Pa)
tmp = Pa.HorizontalMeanSurface(Gr,&self.s_flux[0])
NS.write_ts('s_flux_surface_mean', tmp, Pa)
tmp = Pa.HorizontalMeanSurface(Gr,&self.qt_flux[0])
NS.write_ts('qt_flux_surface_mean', tmp, Pa)
return
cdef class SurfaceDYCOMS_RF02:
def __init__(self,namelist, LatentHeat LH):
self.ft = 16.0
self.fq = 93.0
self.gustiness = 0.0
self.ustar = 0.25
self.L_fp = LH.L_fp
self.Lambda_fp = LH.Lambda_fp
sst = 292.5 # K
psurface = 1017.8e2 # Pa
theta_surface = sst/exner(psurface)
qt_surface = 13.84e-3 # qs(sst) using Teten's formula
density_surface = 1.22 #kg/m^3
theta_flux = self.ft/(density_surface*cpm(qt_surface)*exner(psurface))
qt_flux_ = self.fq/self.L_fp(sst,self.Lambda_fp(sst))
self.buoyancy_flux = g * ((theta_flux + (eps_vi-1.0)*(theta_surface*qt_flux_ + qt_surface * theta_flux))
/(theta_surface*(1.0 + (eps_vi-1)*qt_surface)))
cpdef initialize(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
self.windspeed = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
self.u_flux = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
self.v_flux = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
self.qt_flux = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
self.s_flux = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
NS.add_ts('uw_surface_mean', Gr, Pa)
NS.add_ts('vw_surface_mean', Gr, Pa)
NS.add_ts('s_flux_surface_mean', Gr, Pa)
NS.add_ts('qt_flux_surface_mean', Gr, Pa)
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref, PrognosticVariables.PrognosticVariables PV,DiagnosticVariables.DiagnosticVariables DV, ParallelMPI.ParallelMPI Pa, TimeStepping.TimeStepping TS):
if Pa.sub_z_rank != 0:
return
cdef:
Py_ssize_t u_shift = PV.get_varshift(Gr, 'u')
Py_ssize_t v_shift = PV.get_varshift(Gr, 'v')
Py_ssize_t s_shift = PV.get_varshift(Gr, 's')
Py_ssize_t qt_shift = PV.get_varshift(Gr, 'qt')
Py_ssize_t t_shift = DV.get_varshift(Gr, 'temperature')
Py_ssize_t ql_shift = DV.get_varshift(Gr, 'ql')
compute_windspeed(&Gr.dims, &PV.values[u_shift], &PV.values[v_shift], &self.windspeed[0],Ref.u0, Ref.v0,self.gustiness)
cdef:
Py_ssize_t i,j, ijk, ij
Py_ssize_t gw = Gr.dims.gw
Py_ssize_t imax = Gr.dims.nlg[0]
Py_ssize_t jmax = Gr.dims.nlg[1]
Py_ssize_t istride = Gr.dims.nlg[1] * Gr.dims.nlg[2]
Py_ssize_t jstride = Gr.dims.nlg[2]
Py_ssize_t istride_2d = Gr.dims.nlg[1]
double tendency_factor = Ref.alpha0_half[gw]/Ref.alpha0[gw-1]/Gr.dims.dx[2]
double lam
double lv
double pv
double pd
double sv
double sd
double [:] windspeed = self.windspeed
Py_ssize_t lmo_shift = DV.get_varshift_2d(Gr, 'obukhov_length')
Py_ssize_t ustar_shift = DV.get_varshift_2d(Gr, 'friction_velocity')
with nogil:
for i in xrange(gw-1, imax-gw+1):
for j in xrange(gw-1, jmax-gw+1):
ijk = i * istride + j * jstride + gw
ij = i * istride_2d + j
DV.values_2d[ustar_shift + ij] = self.ustar
DV.values_2d[lmo_shift + ij] = -self.ustar**3.0/ self.buoyancy_flux /vkb
lam = self.Lambda_fp(DV.values[t_shift+ijk])
lv = self.L_fp(DV.values[t_shift+ijk],lam)
pv = pv_c(Ref.p0_half[gw], PV.values[ijk + qt_shift], PV.values[ijk + qt_shift] - DV.values[ijk + ql_shift])
pd = pd_c(Ref.p0_half[gw], PV.values[ijk + qt_shift], PV.values[ijk + qt_shift] - DV.values[ijk + ql_shift])
sv = sv_c(pv,DV.values[t_shift+ijk])
sd = sd_c(pd,DV.values[t_shift+ijk])
self.qt_flux[ij] = self.fq / lv / 1.21
self.s_flux[ij] = Ref.alpha0_half[gw] * (self.ft/DV.values[t_shift+ijk] + self.fq*(sv - sd)/lv)
for i in xrange(gw, imax-gw):
for j in xrange(gw, jmax-gw):
ijk = i * istride + j * jstride + gw
ij = i * istride_2d + j
self.u_flux[ij] = -self.ustar*self.ustar / interp_2(windspeed[ij], windspeed[ij+istride_2d]) * (PV.values[u_shift + ijk] + Ref.u0)
self.v_flux[ij] = -self.ustar*self.ustar / interp_2(windspeed[ij], windspeed[ij+1]) * (PV.values[v_shift + ijk] + Ref.v0)
PV.tendencies[u_shift + ijk] += self.u_flux[ij] * tendency_factor
PV.tendencies[v_shift + ijk] += self.v_flux[ij] * tendency_factor
PV.tendencies[s_shift + ijk] += self.s_flux[ij] * tendency_factor
PV.tendencies[qt_shift + ijk] += self.qt_flux[ij] * tendency_factor
return
cpdef stats_io(self, Grid.Grid Gr, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
cdef double tmp
tmp = Pa.HorizontalMeanSurface(Gr, &self.u_flux[0])
NS.write_ts('uw_surface_mean',tmp, Pa)
tmp = Pa.HorizontalMeanSurface(Gr,&self.v_flux[0])
NS.write_ts('vw_surface_mean', tmp, Pa)
tmp = Pa.HorizontalMeanSurface(Gr,&self.s_flux[0])
NS.write_ts('s_flux_surface_mean', tmp, Pa)
tmp = Pa.HorizontalMeanSurface(Gr,&self.qt_flux[0])
NS.write_ts('qt_flux_surface_mean', tmp, Pa)
return
cdef class SurfaceVARRico:
def __init__(self,namelist):
self.cm = namelist['thermo']['cm']
self.ch = namelist['thermo']['ch']
self.cq = namelist['thermo']['cq']
self.z0 = 0.00015
self.gustiness = 0.0
return
cpdef initialize(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
self.u_flux = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
self.v_flux = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
self.qt_flux = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
self.s_flux = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
self.cm = self.cm*(log(20.0/self.z0)/log(Gr.zl_half[Gr.dims.gw]/self.z0))**2
self.ch = self.ch*(log(20.0/self.z0)/log(Gr.zl_half[Gr.dims.gw]/self.z0))**2
self.cq = self.cq*(log(20.0/self.z0)/log(Gr.zl_half[Gr.dims.gw]/self.z0))**2
print(self.cm, self.ch, self.cq)
cdef double pv_star = pv_c(Ref.Pg, Ref.qtg, Ref.qtg)
cdef double pd_star = Ref.Pg - pv_star
self.s_star = (1.0-Ref.qtg) * sd_c(pd_star, Ref.Tg) + Ref.qtg * sv_c(pv_star,Ref.Tg)
NS.add_ts('uw_surface_mean', Gr, Pa)
NS.add_ts('vw_surface_mean', Gr, Pa)
NS.add_ts('s_flux_surface_mean', Gr, Pa)
NS.add_ts('qt_flux_surface_mean', Gr, Pa)
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref, PrognosticVariables.PrognosticVariables PV,
DiagnosticVariables.DiagnosticVariables DV, ParallelMPI.ParallelMPI Pa, TimeStepping.TimeStepping TS):
if Pa.sub_z_rank != 0:
return
cdef:
Py_ssize_t i,j, ijk, ij
Py_ssize_t gw = Gr.dims.gw
Py_ssize_t imax = Gr.dims.nlg[0]
Py_ssize_t jmax = Gr.dims.nlg[1]
Py_ssize_t istride = Gr.dims.nlg[1] * Gr.dims.nlg[2]
Py_ssize_t jstride = Gr.dims.nlg[2]
Py_ssize_t istride_2d = Gr.dims.nlg[1]
Py_ssize_t u_shift = PV.get_varshift(Gr, 'u')
Py_ssize_t v_shift = PV.get_varshift(Gr, 'v')
Py_ssize_t s_shift = PV.get_varshift(Gr, 's')
Py_ssize_t qt_shift = PV.get_varshift(Gr, 'qt')
Py_ssize_t t_shift = DV.get_varshift(Gr, 'temperature')
Py_ssize_t lmo_shift = DV.get_varshift_2d(Gr, 'obukhov_length')
Py_ssize_t ustar_shift = DV.get_varshift_2d(Gr, 'friction_velocity')
double [:] windspeed = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
double ustar_
double buoyancy_flux, theta_flux
double theta_surface = Ref.Tg * exner_c(Ref.Pg)
double tendency_factor = Ref.alpha0_half[gw]/Ref.alpha0[gw-1]/Gr.dims.dx[2]
double cm_sqrt = sqrt(self.cm)
compute_windspeed(&Gr.dims, &PV.values[u_shift], &PV.values[v_shift], &windspeed[0],Ref.u0, Ref.v0,self.gustiness)
with nogil:
for i in xrange(gw, imax-gw):
for j in xrange(gw,jmax-gw):
ijk = i * istride + j * jstride + gw
ij = i * istride_2d + j
theta_flux = -self.ch * windspeed[ij] * (DV.values[t_shift + ijk]*exner_c(Ref.p0_half[gw]) - theta_surface)
self.s_flux[ij] = -self.ch * windspeed[ij] * (PV.values[s_shift + ijk] - self.s_star)
self.qt_flux[ij] = -self.cq * windspeed[ij] * (PV.values[qt_shift + ijk] - Ref.qtg)
buoyancy_flux = g * ((theta_flux + (eps_vi-1.0)*(theta_surface*self.qt_flux[ij] + Ref.qtg * theta_flux))/(theta_surface*(1.0 + (eps_vi-1)*Ref.qtg)))
self.u_flux[ij] = -self.cm * interp_2(windspeed[ij], windspeed[ij + istride_2d]) * (PV.values[u_shift + ijk] + Ref.u0)
self.v_flux[ij] = -self.cm * interp_2(windspeed[ij], windspeed[ij + 1])* (PV.values[v_shift + ijk] + Ref.v0)
ustar_ = cm_sqrt * windspeed[ij]
DV.values_2d[ustar_shift + ij] = ustar_
DV.values_2d[lmo_shift + ij] = -ustar_ * ustar_ * ustar_/buoyancy_flux/vkb
PV.tendencies[u_shift + ijk] += self.u_flux[ij] * tendency_factor
PV.tendencies[v_shift + ijk] += self.v_flux[ij] * tendency_factor
PV.tendencies[s_shift + ijk] += self.s_flux[ij] * tendency_factor
PV.tendencies[qt_shift + ijk] += self.qt_flux[ij] * tendency_factor
return
cpdef stats_io(self, Grid.Grid Gr, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
cdef double tmp
tmp = Pa.HorizontalMeanSurface(Gr, &self.u_flux[0])
NS.write_ts('uw_surface_mean',tmp, Pa)
tmp = Pa.HorizontalMeanSurface(Gr,&self.v_flux[0])
NS.write_ts('vw_surface_mean', tmp, Pa)
tmp = Pa.HorizontalMeanSurface(Gr,&self.s_flux[0])
NS.write_ts('s_flux_surface_mean', tmp, Pa)
tmp = Pa.HorizontalMeanSurface(Gr,&self.qt_flux[0])
NS.write_ts('qt_flux_surface_mean', tmp, Pa)
return
cdef class SurfaceRico:
def __init__(self):
self.cm =0.001229
self.ch = 0.001094
self.cq = 0.001133
self.z0 = 0.00015
self.gustiness = 0.0
return
cpdef initialize(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
self.u_flux = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
self.v_flux = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
self.qt_flux = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
self.s_flux = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
self.cm = self.cm*(log(20.0/self.z0)/log(Gr.zl_half[Gr.dims.gw]/self.z0))**2
self.ch = self.ch*(log(20.0/self.z0)/log(Gr.zl_half[Gr.dims.gw]/self.z0))**2
self.cq = self.cq*(log(20.0/self.z0)/log(Gr.zl_half[Gr.dims.gw]/self.z0))**2
print(self.cm, self.ch, self.cq)
cdef double pv_star = pv_c(Ref.Pg, Ref.qtg, Ref.qtg)
cdef double pd_star = Ref.Pg - pv_star
self.s_star = (1.0-Ref.qtg) * sd_c(pd_star, Ref.Tg) + Ref.qtg * sv_c(pv_star,Ref.Tg)
NS.add_ts('uw_surface_mean', Gr, Pa)
NS.add_ts('vw_surface_mean', Gr, Pa)
NS.add_ts('s_flux_surface_mean', Gr, Pa)
NS.add_ts('qt_flux_surface_mean', Gr, Pa)
return
cpdef update(self, Grid.Grid Gr, ReferenceState.ReferenceState Ref, PrognosticVariables.PrognosticVariables PV,
DiagnosticVariables.DiagnosticVariables DV, ParallelMPI.ParallelMPI Pa, TimeStepping.TimeStepping TS):
if Pa.sub_z_rank != 0:
return
cdef:
Py_ssize_t i,j, ijk, ij
Py_ssize_t gw = Gr.dims.gw
Py_ssize_t imax = Gr.dims.nlg[0]
Py_ssize_t jmax = Gr.dims.nlg[1]
Py_ssize_t istride = Gr.dims.nlg[1] * Gr.dims.nlg[2]
Py_ssize_t jstride = Gr.dims.nlg[2]
Py_ssize_t istride_2d = Gr.dims.nlg[1]
Py_ssize_t u_shift = PV.get_varshift(Gr, 'u')
Py_ssize_t v_shift = PV.get_varshift(Gr, 'v')
Py_ssize_t s_shift = PV.get_varshift(Gr, 's')
Py_ssize_t qt_shift = PV.get_varshift(Gr, 'qt')
Py_ssize_t t_shift = DV.get_varshift(Gr, 'temperature')
Py_ssize_t lmo_shift = DV.get_varshift_2d(Gr, 'obukhov_length')
Py_ssize_t ustar_shift = DV.get_varshift_2d(Gr, 'friction_velocity')
double [:] windspeed = np.zeros(Gr.dims.nlg[0]*Gr.dims.nlg[1], dtype=np.double, order='c')
double ustar_
double buoyancy_flux, theta_flux
double theta_surface = Ref.Tg * exner_c(Ref.Pg)
double tendency_factor = Ref.alpha0_half[gw]/Ref.alpha0[gw-1]/Gr.dims.dx[2]
double cm_sqrt = sqrt(self.cm)
compute_windspeed(&Gr.dims, &PV.values[u_shift], &PV.values[v_shift], &windspeed[0],Ref.u0, Ref.v0,self.gustiness)
with nogil:
for i in xrange(gw, imax-gw):
for j in xrange(gw,jmax-gw):
ijk = i * istride + j * jstride + gw
ij = i * istride_2d + j
theta_flux = -self.ch * windspeed[ij] * (DV.values[t_shift + ijk]*exner_c(Ref.p0_half[gw]) - theta_surface)
self.s_flux[ij] = -self.ch * windspeed[ij] * (PV.values[s_shift + ijk] - self.s_star)
self.qt_flux[ij] = -self.cq * windspeed[ij] * (PV.values[qt_shift + ijk] - Ref.qtg)
buoyancy_flux = g * ((theta_flux + (eps_vi-1.0)*(theta_surface*self.qt_flux[ij] + Ref.qtg * theta_flux))/(theta_surface*(1.0 + (eps_vi-1)*Ref.qtg)))
self.u_flux[ij] = -self.cm * interp_2(windspeed[ij], windspeed[ij + istride_2d]) * (PV.values[u_shift + ijk] + Ref.u0)
self.v_flux[ij] = -self.cm * interp_2(windspeed[ij], windspeed[ij + 1])* (PV.values[v_shift + ijk] + Ref.v0)
ustar_ = cm_sqrt * windspeed[ij]
DV.values_2d[ustar_shift + ij] = ustar_
DV.values_2d[lmo_shift + ij] = -ustar_ * ustar_ * ustar_/buoyancy_flux/vkb
PV.tendencies[u_shift + ijk] += self.u_flux[ij] * tendency_factor
PV.tendencies[v_shift + ijk] += self.v_flux[ij] * tendency_factor
PV.tendencies[s_shift + ijk] += self.s_flux[ij] * tendency_factor
PV.tendencies[qt_shift + ijk] += self.qt_flux[ij] * tendency_factor
return
cpdef stats_io(self, Grid.Grid Gr, NetCDFIO_Stats NS, ParallelMPI.ParallelMPI Pa):
cdef double tmp
tmp = Pa.HorizontalMeanSurface(Gr, &self.u_flux[0])
NS.write_ts('uw_surface_mean',tmp, Pa)
tmp = Pa.HorizontalMeanSurface(Gr,&self.v_flux[0])
NS.write_ts('vw_surface_mean', tmp, Pa)
tmp = Pa.HorizontalMeanSurface(Gr,&self.s_flux[0])
NS.write_ts('s_flux_surface_mean', tmp, Pa)
tmp = Pa.HorizontalMeanSurface(Gr,&self.qt_flux[0])
NS.write_ts('qt_flux_surface_mean', tmp, Pa)
return
# Anderson, R. J., 1993: A Study of Wind Stress and Heat Flux over the Open
# Ocean by the Inertial-Dissipation Method. J. Phys. Oceanogr., 23, 2153--“2161.
# See also: ARPS documentation
cdef inline double compute_z0(double z1, double windspeed) nogil:
cdef double z0 =z1*exp(-kappa/sqrt((0.4 + 0.079*windspeed)*1e-3))
return z0