elif nobsall > nobs:
obindx = np.random.choice(np.arange(nobsall),size=nobs,replace=False)
oblats = oblatsall[obindx]; oblons = oblonsall[obindx]
thetaobs = np.ascontiguousarray(thetaobsall[ntime,obindx])
if use_letkf:
covlocal_tmp = np.ascontiguousarray(covlocal1[obindx,:])
else:
covlocal_tmp = np.ascontiguousarray(covlocal[obindx,:])
hcovlocal_tmp = np.ascontiguousarray(hcovlocal[obindx,:][:,obindx])
else:
raise ValueError('nobsall must be >= nobs')
if oberrstdev > 0.: # add observation error
thetaobs += np.random.normal(scale=oberrstdev,size=nobs) # add ob errors
for nanal in range(nanals):
# inverse transform to grid.
uens[nanal],vens[nanal] = sp.getuv(vrtspec[nanal],divspec[nanal])
thetaens[nanal] = sp.spectogrd(thetaspec[nanal])
wens[nanal] =\
models[nanal].dp*sp.spectogrd(divspec[nanal,1,...]-divspec[nanal,0,...])
# forward operator calculation.
theta = thetaens[nanal,::-1,:]
hxens[nanal] = bilintrp(theta,np.degrees(sp.lons),np.degrees(sp.lats[::-1]),oblons,oblats)
hxensmean = hxens.mean(axis=0)
obfits = ((thetaobs-hxensmean)**2).sum(axis=0)/(nobs-1)
obbias = (thetaobs-hxensmean).mean(axis=0)
obsprd = (((hxens-hxensmean)**2).sum(axis=0)/(nanals-1)).mean()
uensmean = uens.mean(axis=0); vensmean = vens.mean(axis=0)
thetensmean = thetaens.mean(axis=0)
wensmean = wens.mean(axis=0)
theterr = (thetatruth[ntime]-thetensmean)**2
theterrb = np.sqrt((theterr*globalmeanwts).sum())
obindx = np.random.choice(np.arange(nobsall), size=nobs, replace=False)
oblats = oblatsall[obindx]
oblons = oblonsall[obindx]
thetaobs = np.ascontiguousarray(thetaobsall[ntime, obindx])
if use_letkf:
covlocal_tmp = np.ascontiguousarray(covlocal1[obindx, :])
else:
covlocal_tmp = np.ascontiguousarray(covlocal[obindx, :])
hcovlocal_tmp = np.ascontiguousarray(hcovlocal[obindx, :][:, obindx])
else:
raise ValueError("nobsall must be >= nobs")
if oberrstdev > 0.0: # add observation error
thetaobs += np.random.normal(scale=oberrstdev, size=nobs) # add ob errors
for nanal in range(nanals):
# inverse transform to grid.
uens[nanal], vens[nanal] = sp.getuv(vrtspec[nanal], divspec[nanal])
thetaens[nanal] = sp.spectogrd(thetaspec[nanal])
wens[nanal] = models[nanal].dp * sp.spectogrd(divspec[nanal, 1, ...] - divspec[nanal, 0, ...])
# forward operator calculation.
theta = thetaens[nanal, ::-1, :]
hxens[nanal] = bilintrp(theta, np.degrees(sp.lons), np.degrees(sp.lats[::-1]), oblons, oblats)
hxensmean = hxens.mean(axis=0)
obfits = ((thetaobs - hxensmean) ** 2).sum(axis=0) / (nobs - 1)
obbias = (thetaobs - hxensmean).mean(axis=0)
obsprd = (((hxens - hxensmean) ** 2).sum(axis=0) / (nanals - 1)).mean()
uensmean = uens.mean(axis=0)
vensmean = vens.mean(axis=0)
thetensmean = thetaens.mean(axis=0)
wensmean = wens.mean(axis=0)
theterr = (thetatruth[ntime] - thetensmean) ** 2
theterrb = np.sqrt((theterr * globalmeanwts).sum())
oblons = oblonsall[obindx]
thetaobs = np.ascontiguousarray(thetaobsall[ntime, obindx])
if use_letkf:
covlocal_tmp = np.ascontiguousarray(covlocal1[obindx, :])
else:
covlocal_tmp = np.ascontiguousarray(covlocal[obindx, :])
hcovlocal_tmp = np.ascontiguousarray(hcovlocal[obindx, :][:,
obindx])
else:
raise ValueError('nobsall must be >= nobs')
if oberrstdev > 0.: # add observation error
thetaobs += rs1.normal(scale=oberrstdev, size=nobs) # add ob errors
for nanal in range(nanals):
# inverse transform to grid at obs time (center of IAU window).
uens[nanal], vens[nanal] = sp.getuv(
vrtspec_fcst[nsteps / 2, nanal, ...], divspec_fcst[nsteps / 2,
nanal, ...])
thetaens[nanal] = sp.spectogrd(thetaspec_fcst[nsteps / 2, nanal, ...])
wens[nanal] = models[nanal].dp * sp.spectogrd(divspec_fcst[
nsteps / 2, nanal, 1, :] - divspec_fcst[nsteps / 2, nanal, 0, :])
# forward operator calculation (obs all at center of assim window).
hxens[nanal] = bilintrp(thetaens[nanal, ::-1, :], np.degrees(sp.lons),
np.degrees(sp.lats[::-1]), oblons, oblats)
hxensmean = hxens.mean(axis=0)
obfits = ((thetaobs - hxensmean)**2).sum(axis=0) / (nobs - 1)
obbias = (thetaobs - hxensmean).mean(axis=0)
obsprd = (((hxens - hxensmean)**2).sum(axis=0) / (nanals - 1)).mean()
uensmean = uens.mean(axis=0)
vensmean = vens.mean(axis=0)
thetensmean = thetaens.mean(axis=0)
wensmean = wens.mean(axis=0)
elif nobsall > nobs:
obindx = rs1.choice(np.arange(nobsall),size=nobs,replace=False)
oblats = oblatsall[obindx]; oblons = oblonsall[obindx]
thetaobs = np.ascontiguousarray(thetaobsall[ntime,obindx])
if use_letkf:
covlocal_tmp = np.ascontiguousarray(covlocal1[obindx,:])
else:
covlocal_tmp = np.ascontiguousarray(covlocal[obindx,:])
hcovlocal_tmp = np.ascontiguousarray(hcovlocal[obindx,:][:,obindx])
else:
raise ValueError('nobsall must be >= nobs')
if oberrstdev > 0.: # add observation error
thetaobs += rs1.normal(scale=oberrstdev,size=nobs) # add ob errors
for nanal in range(nanals):
# inverse transform to grid at obs time (center of IAU window).
uens[nanal],vens[nanal] = sp.getuv(vrtspec_fcst[nsteps/2,nanal,...],divspec_fcst[nsteps/2,nanal,...])
thetaens[nanal] = sp.spectogrd(thetaspec_fcst[nsteps/2,nanal,...])
wens[nanal] = models[nanal].dp*sp.spectogrd(divspec_fcst[nsteps/2,nanal,1,:]-divspec_fcst[nsteps/2,nanal,0,:])
# forward operator calculation (obs all at center of assim window).
hxens[nanal] = bilintrp(thetaens[nanal,::-1,:],np.degrees(sp.lons),np.degrees(sp.lats[::-1]),oblons,oblats)
hxensmean = hxens.mean(axis=0)
obfits = ((thetaobs-hxensmean)**2).sum(axis=0)/(nobs-1)
obbias = (thetaobs-hxensmean).mean(axis=0)
obsprd = (((hxens-hxensmean)**2).sum(axis=0)/(nanals-1)).mean()
uensmean = uens.mean(axis=0); vensmean = vens.mean(axis=0)
thetensmean = thetaens.mean(axis=0)
wensmean = wens.mean(axis=0)
theterr = (thetatruth[ntime]-thetensmean)**2
theterr = np.sqrt((theterr*globalmeanwts).sum())
werr = (wtruth[ntime]-wensmean)**2
werr = np.sqrt((werr*globalmeanwts).sum())
