pert = ['LU']
npert = np.size(pert)
ens = ['_A1','_A2','_A3','_A4','_A5']
nens = np.size(ens)
diag = 'var2d'
var = ['t_ref']
#var = ['salt_col','dust_col']
"""
var = ['swdn_sfc','swup_sfc','swdn_toa','swup_toa',\
'swdn_sfc_clr','swup_sfc_clr','swdn_toa_clr','swup_toa_clr',\
'lwdn_sfc','lwdn_sfc_clr','lwup_sfc','lwup_sfc_clr','olr','olr_clr',\
'evap','shflx','netrad_toa','netrad_toa_clr']
"""
nvar = np.size(var)
time = '1870-2015'
for vi in range(nvar):
for i in range(npert):
filename = indir+indir_sub+diag+'.'+time+'.'+pert[i]+ens[0]+'.npz'
npz = np.load(filename)
tmp = npz[var[vi]]
av = np.zeros(tmp.shape+(nens,))
for j in range(nens):
filename = indir+indir_sub+diag+'.'+time+'.'+pert[i]+ens[j]+'.npz'
npz = np.load(filename)
av[...,j] = npz[var[vi]]
av = np.mean(av,-1)
outfile = outdir+outdir_sub+diag+'.'+time+'.'+pert[i]+'_mean'+'.npz'
fio.save(outfile,**{var[vi]:av})
print var[vi]
for i in range(ts_ann.shape[0]):
nan_ind = np.where(np.isnan(ts_ann[i,:,:]))
land_mask[0,nan_ind[0],nan_ind[1]] = 0
ts_ann[np.where(np.isnan(ts_ann))]=0
ts_gm = np.sum(np.nansum(ts_ann*area*land_mask,-1),-1)/np.nansum(area*land_mask)
land_mask.shape = (nlat,nlon)
area.shape = (nlat,nlon)
weight = area/np.sum(area)
"""
if zm:
ts = np.mean(ts,-1)
#%%
if init:
outfile = outdir+'dim.'+obs+'.npz'
fio.save(outfile,lat=lat,lon=lon,land_mask=land_mask)
if init3d:
tmp = nc.netcdf_file(files[-1],'r',mmap=True)
pfull = tmp.variables['pfull'][:].astype(np.float64)
outfile = outdir+'dim.'+obs+'.npz'
fio.save(outfile,pfull=pfull)
ts_flag = ts.copy()
for flagi in range(nflag):
if (flag[flagi] != ''):
ts_flag = pp.month_to_year(ts,yr_ts,flag[flagi])
outdir_sub='ts/'+sub_dict[flag[flagi]]+'/'
outfile = outdir+outdir_sub+diago+'.'+timeo+'.'+obs+'.npz'
fio.save(outfile,**{varo:ts_flag})
var = fs[-1].variables[var_name][500:,:,:,:].astype(np.float64) #t,p,lat,lon
ps = fs[-1].variables['ps'][500:,:,:].astype(np.float64)
fs[-1].close()
var_tr = var-np.mean(var,-1)[...,np.newaxis]
power = np.abs(fft.fft(var_tr,axis=-1))**2
phalf = grid.calcSigmaPres(ps,pk,bk)
dp = (phalf[:,1:,:,:]-phalf[:,:-1,:,:])
dp_m = np.mean(dp,-1)[...,np.newaxis]
power_col = np.sum(power*dp,1)/np.sum(dp,1)
power_m = np.mean(power_col,0)[:,:nlon/2]
k = np.arange(0,nlon/2)
k_m = np.sum(power_m*k,-1)/np.sum(power_m,-1)
L[si,:] = 2*np.pi*RADIUS*np.cos(lat*np.pi/180.)/k_m
if init:
outfile = outdir+'dim.'+perto[si]+'.npz'
fio.save(outfile,lat=lat,lon=lon,phalf=phalf)
if init3d:
pfull = fs[-1].variables['pfull'][:].astype(np.float64)
outfile = outdir+'dim.'+perto[si]+'.npz'
fio.save(outfile,pfull=pfull)
#outfile = outdir+'av/'+diago+'.'+perto[si]+'.npz'
#fio.save(outfile,**{varo_name:tr_zm})
#%%
"""
Tot1 = (MMC+St+Tr)
xsc = 2*np.pi*Rad*np.cos(lat/90*np.pi/2)
xsc.shape = [1,nlat]
clev = np.arange(-7,8,1)*1e15
plt.figure(figsize=[4*6/3,4])
ind = np.where(np.isinf(ratio1))
ratio1[ind] = 0
ratio.shape = (nt,1,nlat,1)
ratio1.shape = (nt,1,nlat,1)
vAdj = vPos*np.sqrt(ratio1)+vNeg*np.sqrt(ratio)
"""
vMSE_ed1 = np.sum(vcomp_ed * MSE_m * dp_ed, 1) / GRAV
vMSEcol_Ed1 = np.mean(np.mean(vMSE_ed1, -1), 0)
vMSE_ed2 = np.sum(vcomp_m * MSE_ed * dp_ed, 1) / GRAV
vMSEcol_Ed2 = np.mean(np.mean(vMSE_ed2, -1), 0)
vMSE_ed3 = np.sum(vcomp_ed * MSE_ed * dp_ed, 1) / GRAV
vMSEcol_Ed3 = np.mean(np.mean(vMSE_ed3, -1), 0)
if init:
outfile = outdir + 'dim.' + perto[si] + '.npz'
fio.save(outfile, lat=lat, lon=lon)
if init3d:
pfull = fs[-1].variables['pfull'][:].astype(np.float64)
outfile = outdir + 'dim.' + perto[si] + '.npz'
fio.save(outfile, pfull=pfull)
outfile = outdir + 'av/' + diago + '.' + perto[si] + '.npz'
fio.save(outfile,\
vMSETot_col_zm=vMSEcol_Tot,vMSEMMC_col_zm=vMSEcol_MMC,vMSEEd_col_zm=vMSEcol_Ed)
#%%
"""
plt.figure()
plt.contourf(lat,pfull,np.mean(np.mean(vcomp_m,-1),0),cmap=plt.cm.RdYlBu_r)
plt.gca().invert_yaxis()
plt.colorbar()
"""
#%%
nlat = np.size(lat)
nlon = np.size(lon)
nlev = np.size(phalf)
#%%
filename = atmdir+'00000.atmos_daily.nc'
fs.append(nc.netcdf_file(filename,'r',mmap=True))
var1 = fs[-1].variables[var1_name][500:,:,:,:].astype(np.float64) #t,p,lat,lon
var2 = fs[-1].variables[var2_name][500:,:,:,:].astype(np.float64) #t,p,lat,lon
var1_tr = var1-np.mean(var1,-1)[...,np.newaxis]
var2_tr = var2-np.mean(var2,-1)[...,np.newaxis]
tr = var1_tr*var2_tr
tr_zm = np.mean(tr,-1)
tr_zm = np.mean(tr_zm,0)
if init:
outfile = outdir+'dim.'+perto[si]+'.npz'
fio.save(outfile,lat=lat,lon=lon,phalf=phalf)
if init3d:
pfull = fs[-1].variables['pfull'][:].astype(np.float64)
outfile = outdir+'dim.'+perto[si]+'.npz'
fio.save(outfile,pfull=pfull)
outfile = outdir+'av/'+diago+'.'+perto[si]+'.npz'
fio.save(outfile,**{varo_name:tr_zm})
#%%
"""
Tot1 = (MMC+St+Tr)
xsc = 2*np.pi*Rad*np.cos(lat/90*np.pi/2)
xsc.shape = [1,nlat]
clev = np.arange(-7,8,1)*1e15
plt.figure(figsize=[4*6/3,4])
lat = fs[-1].variables['lat'][:].astype(np.float64)
lon = fs[-1].variables['lon'][:].astype(np.float64)
phalf = fs[-1].variables['phalf'][:].astype(np.float64)
zsurf = fs[-1].variables['zsurf'][:].astype(np.float64)
fs[-1].close()
nlat = np.size(lat)
nlon = np.size(lon)
nphalf = np.size(phalf)
#%%
filedir = atmdir + 'ts/daily/1yr/'
yr = np.arange(1, 10, 1)
nyr = np.size(yr)
data = np.zeros([nyr, nphalf - 1, nlat])
#tmpZon = np.zeros([nmon,nlev-1,nlat])
#phalfZon = np.zeros([nmon,nlev,nlat])
for yri in range(nyr):
yrC = '000' + str(yr[yri]) + '0101-' + '000' + str(yr[yri]) + '1231.'
filename = filedir + diag + '.' + yrC + var + '.nc'
fs.append(nc.netcdf_file(filename, 'r', mmap=True))
#pfull = fs[-1].variables['pfull'][:].astype(np.float64)
tmp = fs[-1].variables[var][:].astype(np.float64) #t,p,lat,lon
#tmp[np.where(tmp<-999)] = np.nan
tmp = np.mean(tmp, 3)
data[yri, :, :] = np.mean(tmp, 0)
fs[-1].close()
#%%
#outfile = outdir+'dim.'+perto[i]+'_sigma.npz'
#fio.save(outfile,lat=lat,lon=lon,phalf=phalf,pfull=pfull)
outfile = outdir + diago + '.' + perto[i] + '_sigma.npz'
fio.save(outfile, **{varo: data})
nlat = np.size(lat)
nlon = np.size(lon)
nphalf = np.size(phalf)
#%%
filedir = atmdir+indir_sub
files = []
for fi in range(nfile):
filename = filedir+diag+'.'+yrstr[fi]+'ann.nc'
files.append(filename)
ts = pp.ts_multi_files(files,var[vi],0)
if zm:
ts = np.nanmean(ts,-1)
#%%
if init and vi==0:
outfile = outdir+'dim.'+simo[i]+'.npz'
fio.save(outfile,lat=lat,lon=lon,phalf=phalf,land_mask=land_mask,year=yr)
if init3d:
tmp = nc.netcdf_file(files[-1],'r',mmap=True)
pfull = tmp.variables['pfull'][:].astype(np.float64)
outfile = outdir+'dim.'+simo[i]+'.npz'
fio.save(outfile,pfull=pfull)
if init3dp:
tmp = nc.netcdf_file(files[-1],'r',mmap=True)
level = tmp.variables['level'][:].astype(np.float64)
outfile = outdir+'dim.'+simo[i]+'.npz'
fio.save(outfile,level=level)
for flagi in range(nflag):
ts_flag = ts
outdir_sub='ts/'+sub_dict[flag[flagi]]+'/'
if (flag[flagi] != ''):
ts_flag = pp.month_to_year(ts,flag[flagi])
time1 = '1870-2015'
time2 = '1870-2014'
for si in range(nsim):
grid_inf = indir+'dim.'+sim[0]+'.npz'
npz = np.load(grid_inf)
lat_in = npz['lat']
lon_in = npz['lon']
filename = indir+indir_sub+diag+'.'+time1+'.'+sim[si]+'.npz'
npz = np.load(filename)
tmp_in = npz[var]
ntime = np.shape(tmp_in)[0]
grid_outf = outdir+'dim.'+sim[0]+'.npz'
npz = np.load(grid_outf)
lat_out = npz['lat']
nlat = np.size(lat_out)
lon_out = npz['lon']
nlon = np.size(lon_out)
(lonm_out,latm_out) = np.meshgrid(lon_out,lat_out)
tmp_out = np.zeros((ntime-1,nlat,nlon))
for ti in range(ntime-1):
(lonm_in,latm_in) = np.meshgrid(lon_in,lat_in)
"""
tmp_rbf = interp.Rbf(latm_in,lonm_in,tmpt[ti,:,:],function='linear',smooth=0)
tmp_out[ti,:,:] = tmp_rbf(latm_out,lonm_out)
"""
tmp_out[ti,:,:] = \
interp.griddata(np.array([latm_in.ravel(),lonm_in.ravel()]).T,\
tmp_in[ti,:,:].ravel(),(latm_out,lonm_out),method='nearest')
outfile = outdir+outdir_sub+diag+'.'+time2+'.'+sim[si]+'.npz'
fio.save(outfile,**{var:tmp_out})
print sim[si]
pfull = fs[-1].variables['pfull'][:].astype(np.float64)
#zsurf = fs[-1].variables['zsurf'][:].astype(np.float64)
fs[-1].close()
#%%
filename = atmdir+'00000.atmos_daily.nc'
fs.append(nc.netcdf_file(filename,'r',mmap=True))
temp = fs[-1].variables['temp'][500:,:,:,:].astype(np.float64) #t,p,lat,lon
q = fs[-1].variables['mix_rat'][500:,:,:,:].astype(np.float64)
pfull.shape = (1,20,1,1)
theta = temp*(p0/pfull)**(Rair/Cp)
thetae = theta*np.exp(sc[i]*Le*q/(Cp*temp))
temp_zm = np.mean(np.mean(temp,3),0)
theta_zm = np.mean(np.mean(theta,3),0)
thetae_zm = np.mean(np.mean(thetae,3),0)
pfull.shape = (20)
tmp = fs[-1].variables[var][500:,:,:,:].astype(np.float64)
tmp_zm = np.mean(np.mean(tmp,3),0)
if init:
outfile = outdir+'dim.'+perto[i]+'.npz'
fio.save(outfile,lat=lat,lon=lon)
if init3d:
pfull = fs[-1].variables['pfull'][:].astype(np.float64)
outfile = outdir+'dim.'+perto[i]+'.npz'
fio.save(outfile,pfull=pfull,phalf=phalf)
outfile = outdir+'av/'+diago+'.'+perto[i]+'.npz'
fio.save(outfile,**{varo:tmp_zm})
fio.save(outfile,temp_zm=temp_zm,theta_zm=theta_zm,thetae_zm=thetae_zm)
nlev = np.size(phalf)
#%%
filename = atmdir + '00000.atmos_daily.nc'
fs.append(nc.netcdf_file(filename, 'r', mmap=True))
var1 = fs[-1].variables[var1_name][500:, :, :, :].astype(
np.float64) #t,p,lat,lon
var2 = fs[-1].variables[var2_name][500:, :, :, :].astype(
np.float64) #t,p,lat,lon
var1_tr = var1 - np.mean(var1, -1)[..., np.newaxis]
var2_tr = var2 - np.mean(var2, -1)[..., np.newaxis]
tr = var1_tr * var2_tr
tr_zm = np.mean(tr, -1)
tr_zm = np.mean(tr_zm, 0)
if init:
outfile = outdir + 'dim.' + perto[si] + '.npz'
fio.save(outfile, lat=lat, lon=lon, phalf=phalf)
if init3d:
pfull = fs[-1].variables['pfull'][:].astype(np.float64)
outfile = outdir + 'dim.' + perto[si] + '.npz'
fio.save(outfile, pfull=pfull)
outfile = outdir + 'av/' + diago + '.' + perto[si] + '.npz'
fio.save(outfile, **{varo_name: tr_zm})
#%%
"""
Tot1 = (MMC+St+Tr)
xsc = 2*np.pi*Rad*np.cos(lat/90*np.pi/2)
xsc.shape = [1,nlat]
clev = np.arange(-7,8,1)*1e15
plt.figure(figsize=[4*6/3,4])
bk = fs[-1].variables['bk'][:].astype(np.float64)
pk = fs[-1].variables['pk'][:].astype(np.float64)
lat = fs[-1].variables['lat'][:].astype(np.float64)
lon = fs[-1].variables['lon'][:].astype(np.float64)
phalf = fs[-1].variables['phalf'][:].astype(np.float64)
zsurf = fs[-1].variables['zsurf'][:].astype(np.float64)
fs[-1].close()
nlat = np.size(lat)
nlon = np.size(lon)
nphalf = np.size(phalf)
#%%
filedir = atmdir+indir_sub
files = []
for fi in range(nfile):
filename = filedir+diag+'.'+yrstr[fi]+'.nc'
files.append(filename)
av = pp.av_multi_files(files,var,0)
av = np.mean(av,-1)
#%%
if init:
outfile = outdir+'dim.'+perto[i]+'.npz'
fio.save(outfile,lat=lat,lon=lon,phalf=phalf)
if init3d:
tmp = nc.netcdf_file(files[-1],'r',mmap=True)
pfull = tmp.variables['pfull'][:].astype(np.float64)
outfile = outdir+'dim.'+perto[i]+'.npz'
fio.save(outfile,pfull=pfull)
outfile = outdir+outdir_sub+diago+'.'+perto[i]+'.npz'
fio.save(outfile,**{varo:av})
time1 = '1870-2015'
time2 = '1870-2014'
for si in range(nsim):
grid_inf = indir + 'dim.' + sim[0] + '.npz'
npz = np.load(grid_inf)
lat_in = npz['lat']
lon_in = npz['lon']
filename = indir + indir_sub + diag + '.' + time1 + '.' + sim[si] + '.npz'
npz = np.load(filename)
tmp_in = npz[var]
ntime = np.shape(tmp_in)[0]
grid_outf = outdir + 'dim.' + sim[0] + '.npz'
npz = np.load(grid_outf)
lat_out = npz['lat']
nlat = np.size(lat_out)
lon_out = npz['lon']
nlon = np.size(lon_out)
(lonm_out, latm_out) = np.meshgrid(lon_out, lat_out)
tmp_out = np.zeros((ntime - 1, nlat, nlon))
for ti in range(ntime - 1):
(lonm_in, latm_in) = np.meshgrid(lon_in, lat_in)
"""
tmp_rbf = interp.Rbf(latm_in,lonm_in,tmpt[ti,:,:],function='linear',smooth=0)
tmp_out[ti,:,:] = tmp_rbf(latm_out,lonm_out)
"""
tmp_out[ti,:,:] = \
interp.griddata(np.array([latm_in.ravel(),lonm_in.ravel()]).T,\
tmp_in[ti,:,:].ravel(),(latm_out,lonm_out),method='nearest')
outfile = outdir + outdir_sub + diag + '.' + time2 + '.' + sim[si] + '.npz'
fio.save(outfile, **{var: tmp_out})
print sim[si]
nens = np.size(ens)
diag = 'var2d'
var = ['precip', 'low_cld_amt']
#var = ['salt_col','dust_col']
"""
var = ['swdn_sfc','swup_sfc','swdn_toa','swup_toa',\
'swdn_sfc_clr','swup_sfc_clr','swdn_toa_clr','swup_toa_clr',\
'lwdn_sfc','lwdn_sfc_clr','lwup_sfc','lwup_sfc_clr','olr','olr_clr',\
'evap','shflx','netrad_toa','netrad_toa_clr']
"""
nvar = np.size(var)
time = '1870-2015'
for vi in range(nvar):
for i in range(npert):
filename = indir + indir_sub + diag + '.' + time + '.' + pert[i] + ens[
0] + '.npz'
npz = np.load(filename)
tmp = npz[var[vi]]
av = np.zeros(tmp.shape + (nens, ))
for j in range(nens):
filename = indir + indir_sub + diag + '.' + time + '.' + pert[
i] + ens[j] + '.npz'
npz = np.load(filename)
av[..., j] = npz[var[vi]]
av = np.mean(av, -1)
outfile = outdir + outdir_sub + diag + '.' + time + '.' + pert[
i] + '_mean' + '.npz'
fio.save(outfile, **{var[vi]: av})
print var[vi]
nphalf = np.size(phalf)
#%%
filedir = atmdir + indir_sub
files = []
for fi in range(nfile):
filename = filedir + diag + '.' + yrstr[fi] + 'ann.nc'
files.append(filename)
ts = pp.ts_multi_files(files, var[vi], 0)
if zm:
ts = np.nanmean(ts, -1)
#%%
if init and vi == 0:
outfile = outdir + 'dim.' + simo[i] + '.npz'
fio.save(outfile,
lat=lat,
lon=lon,
phalf=phalf,
land_mask=land_mask,
year=yr)
if init3d:
tmp = nc.netcdf_file(files[-1], 'r', mmap=True)
pfull = tmp.variables['pfull'][:].astype(np.float64)
outfile = outdir + 'dim.' + simo[i] + '.npz'
fio.save(outfile, pfull=pfull)
if init3dp:
tmp = nc.netcdf_file(files[-1], 'r', mmap=True)
level = tmp.variables['level'][:].astype(np.float64)
outfile = outdir + 'dim.' + simo[i] + '.npz'
fio.save(outfile, level=level)
for flagi in range(nflag):
ts_flag = ts
outdir_sub = 'ts/' + sub_dict[flag[flagi]] + '/'
ens = ['A1','A2','A3','A4','A5']
npert = np.size(pert)
nens = np.size(ens)
sim = []
for i in range(npert):
for j in range(nens):
sim.append(pert[i]+ens[j])
nsim = np.size(sim)
#npert = 4
diag = 'rad2d.'
time = '1870-2015.'
#tmp = np.zeros([12,24,90])
var = ['swup_toa','swdn_toa','olr']
#varo = ['z500','pv500']
nvar = np.size(var)
for si in range(nsim):
filename = basedir+diag+time+sim[si]+'.npz'
npz = np.load(filename)
netrad_toa= npz['swdn_toa']-npz['swup_toa']-npz['olr']
fio.save(filename,netrad_toa=netrad_toa)
for vi in range(nvar):
filename = basedir+diag+time+sim[si]+'.npz'
npz = np.load(filename)
#tmp = npz[var[vi]]
#fio.save(outfile,**{var[vi]:tmp})
if var[vi] in npz.keys():
fio.delete(filename,var[vi])
#fio.delete(filename,'pv500')
print sim[si]
lon = fs[-1].variables['lon'][:].astype(np.float64)
phalf = fs[-1].variables['phalf'][:].astype(np.float64)
zsurf = fs[-1].variables['zsurf'][:].astype(np.float64)
fs[-1].close()
nlat = np.size(lat)
nlon = np.size(lon)
nphalf = np.size(phalf)
#%%
filedir = atmdir+'ts/daily/1yr/'
yr = np.arange(1,10,1)
nyr = np.size(yr)
data = np.zeros([nyr,nphalf-1,nlat])
#tmpZon = np.zeros([nmon,nlev-1,nlat])
#phalfZon = np.zeros([nmon,nlev,nlat])
for yri in range(nyr):
yrC = '000'+str(yr[yri])+'0101-'+'000'+str(yr[yri])+'1231.'
filename = filedir+diag+'.'+yrC+var+'.nc'
fs.append(nc.netcdf_file(filename,'r',mmap=True))
#pfull = fs[-1].variables['pfull'][:].astype(np.float64)
tmp = fs[-1].variables[var][:].astype(np.float64) #t,p,lat,lon
#tmp[np.where(tmp<-999)] = np.nan
tmp = np.mean(tmp,3)
data[yri,:,:] = np.mean(tmp,0)
fs[-1].close()
#%%
#outfile = outdir+'dim.'+perto[i]+'_sigma.npz'
#fio.save(outfile,lat=lat,lon=lon,phalf=phalf,pfull=pfull)
outfile = outdir+diago+'.'+perto[i]+'_sigma.npz'
fio.save(outfile,**{varo:data})
land_mask = np.array(land_mask)
fs[-1].close()
#%%
files = []
nfile = 1
for fi in range(nfile):
files.append(filename)
ts = pp.ts_multi_files(files,var,0)
#%%
ts = ts[1200:-5,:,:]
#ts = ts[:-6,:,:]
#ts[np.where(abs(ts)>999)] = np.nan
if zm:
ts = np.mean(ts,-1)
#%%
if init:
outfile = outdir+'dim.'+obs+'.npz'
fio.save(outfile,lat=lat,lon=lon,land_mask=land_mask)
if init3d:
tmp = nc.netcdf_file(files[-1],'r',mmap=True)
pfull = tmp.variables['pfull'][:].astype(np.float64)
outfile = outdir+'dim.'+obs+'.npz'
fio.save(outfile,pfull=pfull)
for flagi in range(nflag):
if (flag[flagi] != ''):
ts_flag = pp.month_to_year(ts,yr_ts,flag[flagi])
outdir_sub='ts/'+sub_dict[flag[flagi]]+'/'
outfile = outdir+outdir_sub+diago+'.'+timeo+'.'+obs+'.npz'
fio.save(outfile,**{varo:ts_flag})
np.float64) #t,p,lat,lon
ps = fs[-1].variables['ps'][500:, :, :].astype(np.float64)
fs[-1].close()
var_tr = var - np.mean(var, -1)[..., np.newaxis]
power = np.abs(fft.fft(var_tr, axis=-1))**2
phalf = grid.calcSigmaPres(ps, pk, bk)
dp = (phalf[:, 1:, :, :] - phalf[:, :-1, :, :])
dp_m = np.mean(dp, -1)[..., np.newaxis]
power_col = np.sum(power * dp, 1) / np.sum(dp, 1)
power_m = np.mean(power_col, 0)[:, :nlon / 2]
k = np.arange(0, nlon / 2)
k_m = np.sum(power_m * k, -1) / np.sum(power_m, -1)
L[si, :] = 2 * np.pi * RADIUS * np.cos(lat * np.pi / 180.) / k_m
if init:
outfile = outdir + 'dim.' + perto[si] + '.npz'
fio.save(outfile, lat=lat, lon=lon, phalf=phalf)
if init3d:
pfull = fs[-1].variables['pfull'][:].astype(np.float64)
outfile = outdir + 'dim.' + perto[si] + '.npz'
fio.save(outfile, pfull=pfull)
#outfile = outdir+'av/'+diago+'.'+perto[si]+'.npz'
#fio.save(outfile,**{varo_name:tr_zm})
#%%
color = ['k', 'b', 'r', 'b', 'r']
ls = ['-', '-', '-', '--', '--']
plt.figure()
for si in range(npert):
plt.plot(lat, L[si, :] / 1.e6, color=color[si], ls=ls[si])
plt.xlim(-90, 90)
plt.xticks(np.arange(-60, 61, 30))
fs.append(nc.netcdf_file(stafile,'r',mmap=True))
"""
bk = fs[-1].variables['bk'][:].astype(np.float64)
pk = fs[-1].variables['pk'][:].astype(np.float64)
lat = fs[-1].variables['lat'][:].astype(np.float64)
lon = fs[-1].variables['lon'][:].astype(np.float64)
phalf = fs[-1].variables['phalf'][:].astype(np.float64)
zsurf = fs[-1].variables['zsurf'][:].astype(np.float64)
fs[-1].close()
nlat = np.size(lat)
nlon = np.size(lon)
nphalf = np.size(phalf)
"""
#%%
filedir = atmdir+subdir
filename = filedir+diag+'.'+yrC+'.'+var+'.nc'
fs.append(nc.netcdf_file(filename,'r',mmap=True))
#pfull = fs[-1].variables['pfull'][:].astype(np.float64)
tmp = fs[-1].variables[var][:].astype(np.float64) #t,p,lat,lon
#tmp[np.where(tmp<-999)] = np.nan
tmp = np.mean(tmp,2)
tmp = np.mean(tmp,0)
fs[-1].close()
#%%
#outfile = outdir+'dim.'+perto[i]+'_sigma.npz'
#fio.save(outfile,lat=lat,lon=lon,phalf=phalf,pfull=pfull)
outfile = outdir+diago+'.'+perto[i]+'_sigma.npz'
fio.save(outfile,**{varo:tmp})
var = 't_ref'
diag = 'var2d'
time = '1880-2015'
grid_inf = indir + 'dim.' + obs_in + '.npz'
npz = np.load(grid_inf)
lat_in = npz['lat']
lon_in = npz['lon']
land_mask_in = npz['land_mask']
(latt, lont, land_maskt) = (lat_in, lon_in, land_mask_in)
(lonm_in, latm_in) = np.meshgrid(lont, latt)
land_mask_out = interp.griddata(np.array([latm_in.ravel(),lonm_in.ravel()]).T,\
land_maskt.ravel(),(latm_out,lonm_out),method='linear')
outfile = outdir + 'dim.' + obs_out + '.npz'
fio.save(outfile, lat=lat_out, lon=lon_out, land_mask=land_mask_out)
filename = indir + indir_sub + diag + '.' + time + '.' + obs_in + '.npz'
npz = np.load(filename)
tmp_in = npz[var]
latt, lont, tmpt = lat_in, lon_in, tmp_in
ntime = np.shape(tmpt)[0]
tmp_out = np.zeros((ntime, nlat, nlon))
for ti in range(ntime):
(lonm_in, latm_in) = np.meshgrid(lont, latt)
"""
tmp_rbf = interp.Rbf(latm_in,lonm_in,tmpt[ti,:,:],function='linear',smooth=0)
tmp_out[ti,:,:] = tmp_rbf(latm_out,lonm_out)
"""
tmp_out[ti,:,:] = \
interp.griddata(np.array([latm_in.ravel(),lonm_in.ravel()]).T,\
tmpt[ti,:,:].ravel(),(latm_out,lonm_out),method='linear')
for i in ind:
atmdir = basedir + exper + pert[i] + plat + 'pp/' + diag + '/'
stafile = atmdir + diag + '.static.nc'
fs = []
fs.append(nc.netcdf_file(stafile, 'r', mmap=True))
bk = fs[-1].variables['bk'][:].astype(np.float64)
pk = fs[-1].variables['pk'][:].astype(np.float64)
lat = fs[-1].variables['lat'][:].astype(np.float64)
lon = fs[-1].variables['lon'][:].astype(np.float64)
phalf = fs[-1].variables['phalf'][:].astype(np.float64)
zsurf = fs[-1].variables['zsurf'][:].astype(np.float64)
fs[-1].close()
nlat = np.size(lat)
nlon = np.size(lon)
nphalf = np.size(phalf)
#%%
filedir = atmdir + subdir
filename = filedir + diag + '.' + yrC + '.' + var + '.nc'
fs.append(nc.netcdf_file(filename, 'r', mmap=True))
#pfull = fs[-1].variables['pfull'][:].astype(np.float64)
tmp = fs[-1].variables[var][:].astype(np.float64) #t,p,lat,lon
#tmp[np.where(tmp<-999)] = np.nan
tmp = np.mean(tmp, 2)
#tmp = np.mean(tmp,0)
fs[-1].close()
#%%
#outfile = outdir+'dim.'+perto[i]+'_sigma.npz'
#fio.save(outfile,lat=lat,lon=lon,phalf=phalf,pfull=pfull)
outfile = outdir + diago + '.' + perto[i] + '_sigma.npz'
fio.save(outfile, **{varo: tmp})
pfull = fs[-1].variables['pfull'][:].astype(np.float64)
#zsurf = fs[-1].variables['zsurf'][:].astype(np.float64)
fs[-1].close()
#%%
filename = atmdir + '00000.atmos_daily.nc'
fs.append(nc.netcdf_file(filename, 'r', mmap=True))
temp = fs[-1].variables['temp'][500:, :, :, :].astype(
np.float64) #t,p,lat,lon
q = fs[-1].variables['mix_rat'][500:, :, :, :].astype(np.float64)
pfull.shape = (1, 20, 1, 1)
theta = temp * (p0 / pfull)**(Rair / Cp)
thetae = theta * np.exp(sc[i] * Le * q / (Cp * temp))
temp_zm = np.mean(np.mean(temp, 3), 0)
theta_zm = np.mean(np.mean(theta, 3), 0)
thetae_zm = np.mean(np.mean(thetae, 3), 0)
pfull.shape = (20)
tmp = fs[-1].variables[var][500:, :, :, :].astype(np.float64)
tmp_zm = np.mean(np.mean(tmp, 3), 0)
if init:
outfile = outdir + 'dim.' + perto[i] + '.npz'
fio.save(outfile, lat=lat, lon=lon)
if init3d:
pfull = fs[-1].variables['pfull'][:].astype(np.float64)
outfile = outdir + 'dim.' + perto[i] + '.npz'
fio.save(outfile, pfull=pfull, phalf=phalf)
outfile = outdir + 'av/' + diago + '.' + perto[i] + '.npz'
fio.save(outfile, **{varo: tmp_zm})
fio.save(outfile, temp_zm=temp_zm, theta_zm=theta_zm, thetae_zm=thetae_zm)