Tm1.shape = [Nt, nk]
Sm1 = nmp.zeros(Nt * nk)
Sm1.shape = [Nt, nk]
Ztmp[:, :] = ze1t[j1:j2, i1:i2] * ze2t[j1:j2, i1:i2]
for jk in range(nk):
rarea = nmp.sum(Ztmp[:, :] * Xmask[jk, j1:j2, i1:i2])
if rarea > 0.:
Zar2[:, :] = Ztmp[:, :] * Xmask[jk, j1:j2, i1:i2]
for jt in range(Nt):
# Sigma0
Zm1[jt, jk] = nmp.sum(
bphy.sigma0(Xtheta[jt, jk, j1:j2, i1:i2],
Xsali[jt, jk, j1:j2, i1:i2]) *
Zar2[:, :]) / rarea
# Theta
Tm1[jt, jk] = nmp.sum(
Xtheta[jt, jk, j1:j2, i1:i2] * Zar2[:, :]) / rarea
# Salinity
Sm1[jt, jk] = nmp.sum(
Xsali[jt, jk, j1:j2, i1:i2] * Zar2[:, :]) / rarea
else:
Zm1[:, jk] = nmp.nan
Tm1[:, jk] = nmp.nan
Sm1[:, jk] = nmp.nan
# Writing in output file
########################
bt.chck4f(cf_mesh_mask)
id_mm = Dataset(cf_mesh_mask)
Xmask = id_mm.variables['tmask'][0,:,j1:j2,i1:i2]
ze1t = id_mm.variables['e1t'] [0,j1:j2,i1:i2]
ze2t = id_mm.variables['e2t'] [0,j1:j2,i1:i2]
if jb == 0:
zgdept = id_mm.variables['gdept_1d'][0,:]
zgdepw = id_mm.variables['gdepw_1d'][0,:]
nk = len(zgdept)
id_mm.close()
xsurf[:,:] = ze1t[:,:]*ze2t[:,:]
# Surface Sigma0
xsg0 = bph.sigma0(xsst, xsss)
# 3D sigma0
Xsig0 = bph.sigma0(xtpot, xsali)
# Depth of interest (between 300. and 2000.):
#if jb == 0:
# jk1 = bt.find_index_from_value(300., zgdepw)
# jk2 = bt.find_index_from_value(2000., zgdepw)
# nb_zcrit = len(zgdepw[jk1:jk2])
nb_zcrit = nk ; jk1 = 0 ; jk2 = nk-1
rmean_sss0_deep_jfm = nmp.zeros(nb_zcrit)
rmean_sss0_deep_m03 = nmp.zeros(nb_zcrit)
nbp_deeper_zcrit = nmp.zeros(nb_zcrit)
if nb_dim == 4:
xtht = f_nemo_T.variables[cv_T][jt, :, :, :]
xsal = f_nemo_T.variables[cv_S][jt, :, :, :]
if nb_dim == 3:
xtht = f_nemo_T.variables[cv_T][jt, :, :]
xsal = f_nemo_T.variables[cv_S][jt, :, :]
if jt == 0:
if nb_dim == 4:
(nk, nj, ni) = nmp.shape(xtht)
xsg0 = nmp.zeros((nk, nj, ni))
if nb_dim == 3:
(nj, ni) = nmp.shape(xtht)
xsg0 = nmp.zeros((nj, ni))
xsg0 = bp.sigma0(xtht, xsal)
# computing Sigma0 at current time record !
if jt == 0:
f_out = Dataset(cf_out, 'w', format='NETCDF3_CLASSIC')
# Dimensions:
f_out.createDimension('x', ni)
f_out.createDimension('y', nj)
if nb_dim == 4: f_out.createDimension('deptht', nk)
f_out.createDimension('time_counter', None)
id_lon = f_out.createVariable('nav_lon', 'f4', (
'y',
'x',
))
if nj != nj0 or ni != ni0 or nk != nk0:
print 'ERROR: 3D clim and NEMO file do no agree in shape!'
print ' clim => ' + str(ni0) + ', ' + str(nj0) + ', ' + str(
nk0) + ' (' + vdic['F_T_CLIM_3D_12'] + ')'
print ' NEMO => ' + str(ni) + ', ' + str(nj) + ', ' + str(nk)
sys.exit(0)
#############
# M A R C H #
#############
imnth = 2
# march
# Computing sigma0 3D field:
Sigma0 = bphys.sigma0(Tclim[imnth, :, :, :],
Sclim[imnth, :, :, :]) * Xmask[:, :, :]
if ldebug:
Sigma0_nemo = bphys.sigma0(Tnemo[imnth:, :, :],
Snemo[imnth, :, :, :]) * Xmask[:, :, :]
Xmld_obs = nmp.zeros((nj, ni))
mmask = nmp.zeros((nj, ni))
mmask[:, :] = 1.
for jk in range(nk - 1):
zz = vlev[jk]
Sigma0[jk, :, :] = Sigma0[jk, :, :] * mmask[:, :]
Sigma0[jk + 1, :, :] = Sigma0[jk + 1, :, :] * mmask[:, :]
ijloc = nmp.where(Sigma0[jk + 1, :, :] - 0.01 > Sigma0[jk, :, :])
mmask[ijloc] = 0.
bt.chck4f(cf_mesh_mask)
id_mm = Dataset(cf_mesh_mask)
Xmask = id_mm.variables['tmask'][0, :, j1:j2, i1:i2]
ze1t = id_mm.variables['e1t'][0, j1:j2, i1:i2]
ze2t = id_mm.variables['e2t'][0, j1:j2, i1:i2]
if jb == 0:
zgdept = id_mm.variables['gdept_1d'][0, :]
zgdepw = id_mm.variables['gdepw_1d'][0, :]
nk = len(zgdept)
id_mm.close()
xsurf[:, :] = ze1t[:, :] * ze2t[:, :]
# Surface Sigma0
xsg0 = bph.sigma0(xsst, xsss)
# 3D sigma0
Xsig0 = bph.sigma0(xtpot, xsali)
# Depth of interest (between 300. and 2000.):
#if jb == 0:
# jk1 = bt.find_index_from_value(300., zgdepw)
# jk2 = bt.find_index_from_value(2000., zgdepw)
# nb_zcrit = len(zgdepw[jk1:jk2])
nb_zcrit = nk
jk1 = 0
jk2 = nk - 1
rmean_sss0_deep_jfm = nmp.zeros(nb_zcrit)
# Want PmE (positive when ocean gains FW), in NEMO files its the oposite EmP...
if 'wfo' in list_variables[:]:
Zpme = -id_in_T.variables['wfo'] [:,:,:] ; # wfo same as below = EmP, > 0 when ocean losing water
lpme = True
if 'sowaflup' in list_variables[:]:
Zpme = -id_in_T.variables['sowaflup'] [:,:,:] ; # Net Downward Heat Flux
# # sowaflup = EmP (>0 if more evaporation than P)
lpme = True
print '(has ',Xtemp.shape[0],' time snapshots)\n'
id_in_T.close()
[ Nt, nk, nj, ni ] = Xtemp.shape ; print 'Nt, nk, nj, ni =', Nt, nk, nj, ni
Zss0 = sigma0( Zsst, Zsss )
print len(ve3t[:])
if len(ve3t[:]) != nk: print 'Problem with nk!!!'; sys.exit(0)
# NEMO output, Grid U and V
# ~~~~~~~~~~~~~~~~~~~~~~~~~
if luv:
id_in_U = Dataset(cf_in_U)
list_variables = id_in_U.variables.keys()
if NN_TAUX in list_variables[:]:
print ' clim => '+str(ni0)+', '+str(nj0)+', '+str(nk0)+' ('+F_T_CLIM_3D_12+')'
print ' NEMO => '+str(ni)+', '+str(nj)+', '+str(nk)
sys.exit(0)
#############
# M A R C H #
#############
imnth = 2 ; # march
# Computing sigma0 3D field:
Sigma0 = bphys.sigma0(Tclim[imnth,:,:,:], Sclim[imnth,:,:,:])*Xmask[:,:,:]
if ldebug: Sigma0_nemo = bphys.sigma0(Tnemo[imnth:,:,:], Snemo[imnth,:,:,:])*Xmask[:,:,:]
Xmld_obs = nmp.zeros(nj*ni) ; Xmld_obs.shape = [nj,ni]
mmask = nmp.zeros(nj*ni) ; mmask.shape = [nj,ni]
mmask[:,:] = 1.
for jk in range(nk-1):
zz = vlev[jk]
Sigma0[jk,:,:] = Sigma0[jk,:,:]*mmask[:,:]
Sigma0[jk+1,:,:] = Sigma0[jk+1,:,:]*mmask[:,:]
Zpme = -id_in_T.variables['wfo'][:, :, :]
# wfo same as below = EmP, > 0 when ocean losing water
lpme = True
if 'sowaflup' in list_variables[:]:
Zpme = -id_in_T.variables['sowaflup'][:, :, :]
# Net Downward Heat Flux
# # sowaflup = EmP (>0 if more evaporation than P)
lpme = True
print '(has ', Xtemp.shape[0], ' time snapshots)\n'
id_in_T.close()
[Nt, nk, nj, ni] = Xtemp.shape
print 'Nt, nk, nj, ni =', Nt, nk, nj, ni
Zss0 = sigma0(Zsst, Zsss)
print len(ve3t[:])
if len(ve3t[:]) != nk:
print 'Problem with nk!!!'
sys.exit(0)
# NEMO output, Grid U and V
# ~~~~~~~~~~~~~~~~~~~~~~~~~
if luv:
id_in_U = Dataset(cf_in_U)
list_variables = id_in_U.variables.keys()
if vdic_uv['NN_TAUX'] in list_variables[:]:
Ztaux = id_in_U.variables[vdic_uv['NN_TAUX']][:, :, :]
# Net Downward Heat Flux
ltau = True
Tm1.shape = [Nt, nk]
Sm1 = nmp.zeros(Nt * nk)
Sm1.shape = [Nt, nk]
Ztmp[:, :] = ze1t[j1:j2, i1:i2] * ze2t[j1:j2, i1:i2]
for jk in range(nk):
rarea = nmp.sum(Ztmp[:, :] * Xmask[jk, j1:j2, i1:i2])
if rarea > 0.0:
Zar2[:, :] = Ztmp[:, :] * Xmask[jk, j1:j2, i1:i2]
for jt in range(Nt):
# Sigma0
Zm1[jt, jk] = (
nmp.sum(bphy.sigma0(Xtheta[jt, jk, j1:j2, i1:i2], Xsali[jt, jk, j1:j2, i1:i2]) * Zar2[:, :]) / rarea
)
# Theta
Tm1[jt, jk] = nmp.sum(Xtheta[jt, jk, j1:j2, i1:i2] * Zar2[:, :]) / rarea
# Salinity
Sm1[jt, jk] = nmp.sum(Xsali[jt, jk, j1:j2, i1:i2] * Zar2[:, :]) / rarea
else:
Zm1[:, jk] = nmp.nan
Tm1[:, jk] = nmp.nan
Sm1[:, jk] = nmp.nan
# Writing in output file
########################
cf_out = DIAG_D + "/TS_z_box_" + cbox + "_" + CONFRUN + ".nc"
