filename = '/home/z1s/research/climate/atmos_level.static.nc'
fs = nc.netcdf_file(filename, 'r', mmap=True)
lat = fs.variables['lat'][:]
nlat = np.size(lat)
lon = fs.variables['lon'][:]
nlon = np.size(lon)
land_mask1 = fs.variables['land_mask'][:]
fs.close()
land_mask = land_mask1.copy()
if not land:
land_mask[:, :] = 1
region_mask = -np.ones((nlat, nlon))
region_mask_obs = -np.ones((nlat, nlon))
latt, lont, land_mask_obs = pp.grid_for_map(lat, lon, land_mask)
for regi in range(nregion):
(latlim, lonlim) = grid.get_region_lim(region[regi], lonmax=360)
nlim = np.shape(latlim)[0]
for i in range(nlim):
mask = grid.regionMask(lat, lon, latlim[i], lonlim[i])
region_mask[np.where(mask == 1)] = regi + 1
(latlim, lonlim) = grid.get_region_lim(region[regi], lonmax=180)
nlim = np.shape(latlim)[0]
for i in range(nlim):
mask = grid.regionMask(latt, lont, latlim[i], lonlim[i])
region_mask_obs[np.where(mask == 1)] = regi + 1
region_mask[np.where(land_mask < 0.3)] = -1
region_mask_obs[np.where(land_mask_obs < 0.3)] = -1
MCdiv_rm = MCdiv*area*region_mask
MCdiv_rm = np.sum(np.sum(MCdiv_rm,-1),-1)/np.sum(area*region_mask)
Trdiv_rm = Trdiv*area*region_mask
Trdiv_rm = np.sum(np.sum(Trdiv_rm,-1),-1)/np.sum(area*region_mask)
uvdh_rm = uvdh*area*region_mask
uvdh_rm = np.sum(np.sum(uvdh_rm,-1),-1)/np.sum(area*region_mask)
wdh_rm = wdh*area*region_mask
wdh_rm = np.sum(np.sum(wdh_rm,-1),-1)/np.sum(area*region_mask)
de_rm = de*area*region_mask
de_rm = np.sum(np.sum(de_rm,-1),-1)/np.sum(area*region_mask)
#%%
clev = np.arange(-190,191,20)
m = Basemap(projection='mill',\
fix_aspect=True,\
llcrnrlat=-35,urcrnrlat=5,llcrnrlon=-120,urcrnrlon=-20)
latt,lont,nett = pp.grid_for_map(lat,lon,net)
latt,lont,Totdivt = pp.grid_for_map(lat,lon,Totdiv)
latt,lont,det = pp.grid_for_map(lat,lon,de)
latt,lont,MCdivt = pp.grid_for_map(lat,lon,MCdiv)
latt,lont,Trdivt = pp.grid_for_map(lat,lon,Trdiv)
latt,lont,uvdht = pp.grid_for_map(lat,lon,uvdh)
latt,lont,wdht = pp.grid_for_map(lat,lon,wdh)
latt,lont,whbott = pp.grid_for_map(lat,lon,whbot)
latt,lont,whtopt = pp.grid_for_map(lat,lon,whtop)
lons,lats = np.meshgrid(lont,latt)
x,y = m(lons,lats)
cmap = plt.cm.RdYlBu
#%%
plt.figure()
m.drawcoastlines()
m.drawcountries()
MCdiv_rm = MCdiv * area * region_mask
MCdiv_rm = np.sum(np.sum(MCdiv_rm, -1), -1) / np.sum(area * region_mask)
Trdiv_rm = Trdiv * area * region_mask
Trdiv_rm = np.sum(np.sum(Trdiv_rm, -1), -1) / np.sum(area * region_mask)
uvdh_rm = uvdh * area * region_mask
uvdh_rm = np.sum(np.sum(uvdh_rm, -1), -1) / np.sum(area * region_mask)
wdh_rm = wdh * area * region_mask
wdh_rm = np.sum(np.sum(wdh_rm, -1), -1) / np.sum(area * region_mask)
de_rm = de * area * region_mask
de_rm = np.sum(np.sum(de_rm, -1), -1) / np.sum(area * region_mask)
#%%
clev = np.arange(-190, 191, 20)
m = Basemap(projection='mill',\
fix_aspect=True,\
llcrnrlat=-35,urcrnrlat=5,llcrnrlon=-120,urcrnrlon=-20)
latt, lont, nett = pp.grid_for_map(lat, lon, net)
latt, lont, Totdivt = pp.grid_for_map(lat, lon, Totdiv)
latt, lont, det = pp.grid_for_map(lat, lon, de)
latt, lont, MCdivt = pp.grid_for_map(lat, lon, MCdiv)
latt, lont, Trdivt = pp.grid_for_map(lat, lon, Trdiv)
latt, lont, uvdht = pp.grid_for_map(lat, lon, uvdh)
latt, lont, wdht = pp.grid_for_map(lat, lon, wdh)
latt, lont, whbott = pp.grid_for_map(lat, lon, whbot)
latt, lont, whtopt = pp.grid_for_map(lat, lon, whtop)
lons, lats = np.meshgrid(lont, latt)
x, y = m(lons, lats)
cmap = plt.cm.RdYlBu
#%%
plt.figure()
m.drawcoastlines()
m.drawcountries()
plt.xticks(lattick)
plt.ylim([-30,5])
plt.tight_layout()
#%%
tmpzon = np.mean(tmp,2)
weight = np.cos(lat*np.pi/180.)
weight1 = weight/np.sum(weight)
weight1.shape = [1,nlat]
tmpglb = np.sum(tmpzon*weight1,1)
"""
#%%
tmp = tmp-tmp[0,:,:,:]
tmp = np.min(tmp,1)
tmp[:,np.where(land_mask>0)[0],np.where(land_mask>0)[1]] = np.nan
latt,lont,tmpt = pp.grid_for_map(lat,lon,tmp)
data = tmpt*sc
clabel = "Temperature difference (K)"
m = Basemap(projection='mill',\
fix_aspect=True,\
llcrnrlat=-90,urcrnrlat=90,llcrnrlon=-180,urcrnrlon=180)
cmap = plt.cm.RdYlBu_r
clev = np.arange(-1,1.1,0.2)
lons,lats = np.meshgrid(lont,latt)
x,y = m(lons,lats)
plt.figure()
m.drawcoastlines()
m.contourf(x,y,data[1,:,:],clev,cmap=cmap,extend='both')
cb = m.colorbar(location='bottom',size='5%',pad='8%')
plt.legend(['c25','c30'],fontsize=fsize,loc='best')
plt.ylabel('swdn_toa (W m$^{-2}$)')
plt.xticks(lattick)
plt.ylim([-30,5])
plt.tight_layout()
#%%
tmpzon = np.mean(tmp,2)
weight = np.cos(lat*np.pi/180.)
weight1 = weight/np.sum(weight)
weight1.shape = [1,nlat]
tmpglb = np.sum(tmpzon*weight1,1)
"""
#%%
latt, lont, tmpt = pp.grid_for_map(lat, lon, tmp)
latt, lont, speedt = pp.grid_for_map(lat, lon, speed)
tmpt = tmpt * sc
clabel = "t_surf (K)"
m = Basemap(projection='mill',\
fix_aspect=True,\
llcrnrlat=20,urcrnrlat=55,llcrnrlon=60,urcrnrlon=140)
cmap = plt.cm.RdYlBu_r
clev = np.arange(-0.1, 0.11, 0.01)
lons, lats = np.meshgrid(lont, latt)
x, y = m(lons, lats)
yy = np.arange(0, len(latt), 1)
xx = np.arange(0, len(lont), 1)
points = np.meshgrid(yy, xx)
plt.xticks(lattick)
plt.ylim([-30,5])
plt.tight_layout()
#%%
tmpzon = np.mean(tmp,2)
weight = np.cos(lat*np.pi/180.)
weight1 = weight/np.sum(weight)
weight1.shape = [1,nlat]
tmpglb = np.sum(tmpzon*weight1,1)
"""
#%%
tmp = tmp - tmp[0, :, :, :]
tmp = np.min(tmp, 1)
tmp[:, np.where(land_mask > 0)[0], np.where(land_mask > 0)[1]] = np.nan
latt, lont, tmpt = pp.grid_for_map(lat, lon, tmp)
data = tmpt * sc
clabel = "Temperature difference (K)"
m = Basemap(projection='mill',\
fix_aspect=True,\
llcrnrlat=-90,urcrnrlat=90,llcrnrlon=-180,urcrnrlon=180)
cmap = plt.cm.RdYlBu_r
clev = np.arange(-1, 1.1, 0.2)
lons, lats = np.meshgrid(lont, latt)
x, y = m(lons, lats)
plt.figure()
m.drawcoastlines()
m.contourf(x, y, data[1, :, :], clev, cmap=cmap, extend='both')
cb = m.colorbar(location='bottom', size='5%', pad='8%')
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)
var = 't_ref'
diag = 'var2d'
time = '1850-2015'
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']
land_mask_in = npz['land_mask']
(latt,lont,land_maskt) = pp.grid_for_map(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.'+sim[si]+'.npz'
fio.save(outfile,lat=lat_out,lon=lon_out,land_mask=land_mask_out)
filename = indir+indir_sub+diag+'.'+time+'.'+sim[si]+'.npz'
npz = np.load(filename)
tmp_in = npz[var]
latt,lont,tmpt = pp.grid_for_map(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)
sim = []
for i in range(npert):
for j in range(nens):
sim.append(pert[i]+ens[j])
nsim = np.size(sim)
var = 't_ref'
diag = 'var2d'
time = '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+'.'+time+'.'+sim[si]+'.npz'
npz = np.load(filename)
tmp_in = npz[var]
latt,lont,tmpt = pp.grid_for_map(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')
outfile = outdir+outdir_sub+diag+'.'+time+'.'+sim[si]+'.npz'
fio.save(outfile,**{var:tmp_out})
print sim[si]
plt.legend(['c25','c30'],fontsize=fsize,loc='best')
plt.ylabel('swdn_toa (W m$^{-2}$)')
plt.xticks(lattick)
plt.ylim([-30,5])
plt.tight_layout()
#%%
tmpzon = np.mean(tmp,2)
weight = np.cos(lat*np.pi/180.)
weight1 = weight/np.sum(weight)
weight1.shape = [1,nlat]
tmpglb = np.sum(tmpzon*weight1,1)
"""
#%%
latt,lont,tmpt = pp.grid_for_map(lat,lon,tmp)
latt,lont,speedt = pp.grid_for_map(lat,lon,speed)
tmpt = tmpt*sc
clabel = "t_surf (K)"
m = Basemap(projection='mill',\
fix_aspect=True,\
llcrnrlat=20,urcrnrlat=55,llcrnrlon=60,urcrnrlon=140)
cmap = plt.cm.RdYlBu_r
clev = np.arange(-0.1,0.11,0.01)
lons,lats = np.meshgrid(lont,latt)
x,y = m(lons,lats)
yy = np.arange(0,len(latt),1)
xx = np.arange(0,len(lont),1)
points = np.meshgrid(yy,xx)
filename = '/home/z1s/research/climate/atmos_level.static.nc'
fs = nc.netcdf_file(filename,'r',mmap=True)
lat = fs.variables['lat'][:]
nlat = np.size(lat)
lon = fs.variables['lon'][:]
nlon = np.size(lon)
land_mask1 = fs.variables['land_mask'][:]
fs.close()
land_mask = land_mask1.copy()
if not land:
land_mask[:,:] = 1
region_mask = -np.ones((nlat,nlon))
region_mask_obs = -np.ones((nlat,nlon))
latt,lont,land_mask_obs = pp.grid_for_map(lat,lon,land_mask)
for regi in range(nregion):
(latlim,lonlim) = grid.get_region_lim(region[regi],lonmax=360)
nlim = np.shape(latlim)[0]
for i in range(nlim):
mask = grid.regionMask(lat,lon,latlim[i],lonlim[i])
region_mask[np.where(mask==1)] = regi+1
(latlim,lonlim) = grid.get_region_lim(region[regi],lonmax=180)
nlim = np.shape(latlim)[0]
for i in range(nlim):
mask = grid.regionMask(latt,lont,latlim[i],lonlim[i])
region_mask_obs[np.where(mask==1)] = regi+1
region_mask[np.where(land_mask<0.3)] = -1
region_mask_obs[np.where(land_mask_obs<0.3)] = -1
