# net atm radiative and turbulent flux
net = swdn_toa - swup_toa - olr + swup_sfc + lwup_sfc - swdn_sfc - lwdn_sfc + shflx + LE * evap
net_atm[yri * 12 + moni, :, :] = np.mean(net, 0)
nt = vcomp.shape[0]
# calculate pressure
phalf = grid.calcSigmaPres(ps, pk, bk)
pfull = 0.5 * (phalf[:, 1:, :, :] + phalf[:, :-1, :, :])
dp = (phalf[:, 1:, :, :] - phalf[:, :-1, :, :])[:, ptop:, :, :]
#%%
# calculate MSE (subtract mean)
MSE = CP_AIR * temp + GRAV * hght + LE * sphum - LF * ice_wat
ene = CV_AIR * temp + GRAV * hght + LE * sphum - LF * ice_wat
MSEm = np.sum(MSE * dp, 1) / np.sum(dp, 1)
MSEm = np.mean(MSEm, 0)
area = grid.calcGridArea(lat, lon)
MSEm = np.sum(MSEm * area) / np.sum(area)
# MSEm = np.mean(MSE[:,:,:,:])
MSE = MSE - MSEm
# calculate dp
dpMon = np.mean(dp, 0)
dpMC[yri * 12 + moni, :, :, :] = dpMon
pfullMC[yri * 12 + moni, :, :, :] = np.mean(pfull, 0)
MSECol = np.sum(MSE * dpMon, 1) / GRAV
dMSE[yri * 12 +
moni, :, :] = (MSECol[-1, ...] -
MSECol[0, ...]) / (np.size(mind) * 86400)
eneCol = np.sum(MSE * dpMon, 1) / GRAV
dene[yri * 12 +
moni, :, :] = (eneCol[-1, ...] -
eneCol[0, ...]) / (np.size(mind) * 86400)
filename = outdir+'dim.'+pert[0]+'.npz'
npz = np.load(filename)
lat = npz['lat']
nlat = np.size(lat)
ntime = 100
tmp = np.zeros([npert,ntime,nlat])
t_ref = np.zeros([npert,ntime,nlat])
precip = np.zeros([npert,ntime,nlat])
netrad_toa = np.zeros([npert,ntime,nlat])
ind = range(npert)
for i in ind:
filename = outdir+outdir_sub+diag+'.'+time+'.'+pert[i]+'.npz'
npz = np.load(filename)
t_ref[i,:,:] = npz[var][:,:]
area = grid.calcGridArea(lat,1)
area.shape = [1,1,nlat]
t_ref_gm = np.sum(t_ref*area,2)/np.sum(area)
precip_gm = np.sum(precip*area,2)
netrad_toa_gm = np.sum(netrad_toa*area,2)/np.sum(area)
#tmp_gm = np.sum(tmp*weight1,2)
sc = 1
z = np.zeros([npert,2])
plt.figure()
for i in ind:
x = t_ref_gm[i,:]-t_ref_gm[0,:]
plt.plot(x)
print np.mean(x[80:])
lon = fs[-1].variables['lon'][:].astype(np.float64)
nlat = np.size(lat)
nlon = np.size(lon)
#phalf = fs[-1].variables['phalf'][:].astype(np.float64)
#zsurf = fs[-1].variables['zsurf'][:].astype(np.float64)
if ('land_mask' in fs[-1].variables):
land_mask = fs[-1].variables['land_mask'][:].astype(np.float64)
fs[-1].close()
#%%
filedir = atmdir+indir_sub
files = []
for fi in range(nfile):
filename = filedir+diag+'.'+yrstr[fi]+'.nc'
files.append(filename)
ts = pp.ts_multi_files(files,var,0)
ts = np.array(ts)
#%%
if (flag != ''):
ts = pp.month_to_year(ts,yr_ts,flag)
if zm:
ts_zon = np.mean(ts,-1)
if gm:
area = grid.calcGridArea(lat,lon)
area.shape = [1,nlat,nlon]
ts_glb = np.sum(ts*area,-1)
ts_glb = np.sum(ts_glb,-1)
ts_glb = ts_glb/np.sum(area)
print ts_glb
plt.plot(ts_zon[0,:]-ts_zon[0,0])
plt.legend(pert,fontsize=10)
npz = np.load(filename)
lat = npz['lat']
nlat = np.size(lat)
ntime = 100
tmp = np.zeros([npert,ntime,nlat])
t_ref = np.zeros([npert,ntime,nlat])
precip = np.zeros([npert,ntime,nlat])
netrad_toa = np.zeros([npert,ntime,nlat])
ind = range(npert)
for i in ind:
filename = outdir+outdir_sub+diag+'.'+time+'.'+pert[i]+'.npz'
npz = np.load(filename)
t_ref[i,:,:] = npz[var1]
netrad_toa[i,:,:] = npz[var2]
area = grid.calcGridArea(lat,1)
area.shape = [1,1,nlat]
t_ref_gm = np.sum(t_ref*area,2)/np.sum(area)
precip_gm = np.sum(precip*area,2)/np.sum(area)
netrad_toa_gm = np.sum(netrad_toa*area,2)/np.sum(area)
#tmp_gm = np.sum(tmp*weight1,2)
sc = 1
z = np.zeros([npert,2])
plt.figure()
for i in range(1,npert):
x = (t_ref_gm[i,:]-t_ref_gm[0,:])[:20]
y = (netrad_toa_gm[i,:]-netrad_toa_gm[0,:])[:20]
plt.scatter(x,y)
