observations.append(ob30)
observations.append(ob31)
observations.append(ob32)
observations.append(ob33)
observations.append(ob34)
observations.append(ob35)
observations.append(ob36)
observations.append(ob37)
observations.append(ob38)
observations.append(ob39)
observations.append(ob40)
observations.append(ob41)
observations.append(ob42)
# Put the state class object and observation objects into EnSRF object
assimilator = EnSRF(statecls, observations, loc='GC')
print(assimilator)
# Update the prior with EFA- post_state is an EnsembleState object
post_state, post_obs = assimilator.update()
state = post_state
outfile = '/home/disk/hot/stangen/Documents/GEFS/posterior/' + \
'2017090600/2017090600_21mem_10days_42MSLP_Z500_WC_efa.nc'
tunit = 'seconds since 1970-01-01'
# Write ensemble forecast to netcdf
with Dataset(outfile, 'w') as dset:
dset.createDimension('time', None)
dset.createDimension('lat', state.ny())
dset.createDimension('lon', state.nx())
dset.createDimension('ens', state.nmems())
olons = np.array([o.lon for o in observations])
assert olat in olats
assert olon in olons
lons = state['lon'][0, :]
lats = state['lat'][:, 0]
o_ind = np.where(
(olats == olat) * (olons == olon))[0][0]
observations = [observations[o_ind]]
print(' Only assimilating the ob at {}N {}E'.format(
olat, olon))
#==== ASSIMILATE THE OBS ======================================================
print('\n(3) Assimilating the observations...')
assimilator = EnSRF(state,
observations,
nproc=1,
verbose=True,
loc=localization,
inflation=inflations)
start = time.time()
if plot_covs:
assert one_ob
covs, kal, mask = assimilator.update(return_covs=True,
cov_file=cov_file)
plot_covariances(lons,
lats,
covs,
kal,
mask,
varlist,
olat,