Rmw = 5 # radius of maximum wind
Vmax = 50 # maximum wind speed
Vout = 0 # wind speed outside of vortex
iStorm = 63 # location of vortex in i, j
jStorm = 63
wind_highlight = (-15, 15)
##truth
Xt = np.load(outdir + 'truth_state.npy')[:, t]
# plt.switch_backend('Agg')
plt.figure(figsize=(10, 10))
cmap = [plt.cm.jet(x) for x in np.linspace(0, 1, nens)]
ax = plt.subplot(221)
u, v = rv.X2uv(ni, nj, Xt)
zeta = rv.uv2zeta(u, v, dx)
ii, jj = np.mgrid[0:ni, 0:nj]
# ax.contourf(ii, jj, u, np.arange(-70, 71, 5), cmap='bwr')
ax.contourf(ii, jj, zeta, np.arange(-3, 3, 0.1) * 1e-3, cmap='bwr')
ax.contour(ii, jj, u, wind_highlight, colors='k', linewidths=3)
ax.set_title('Truth', fontsize=20)
ax.tick_params(labelsize=15)
for k in range(len(filter_kind)):
Xa = np.load(outdir + filter_kind[k] + '_ens.npy')[:, :, 1, t]
ax = plt.subplot(2, 2, k + 2)
for m in range(nens):
u, v = rv.X2uv(ni, nj, Xa[:, m])
ax.contour(ii,
jj,
jBias = 0
Rsprd = 0
Vsprd = 0
filter_kind = sys.argv[1] #'EnSRF'
ns = int(sys.argv[2])
nens = int(sys.argv[3]) # ensemble size
Csprd = int(sys.argv[4])
obsR = int(sys.argv[5])
obserr = int(sys.argv[6]) # observation error spread
localize_cutoff = 0
##truth
iX, jX = rv.make_coords(ni, nj)
Xt = rv.make_state(ni, nj, nv, iStorm, jStorm, Rmw, Vmax, Vout)
ut, vt = rv.X2uv(ni, nj, Xt)
##DA trials
casename = '{}_s{}_N{}_C{}_R{}_err{}'.format(filter_kind, ns, nens, Csprd,
obsR, obserr)
if os.path.exists(outdir + casename + '.npy'):
state_error = np.load(outdir + casename + '.npy')
else:
state_error = np.zeros((nrealize, nens + 1))
state_error[:, :] = np.nan
for realize in range(r0, r0 + 100):
np.random.seed(realize) # fix random number seed, make results predictable
##Prior ensemble
casename = ('NoDA_s1', 'obs.intv.1/EnSRF_s1', 'obs.intv.1/EnSRF_s4'
) #sys.argv[1] #'EnSRF_s3'
col = ([.3, .6, .3], [.8, .3, .1], [.2, .7, .9])
for c in range(3):
X = np.load(outdir + 'truth_state.npy')
loc = np.load(outdir + 'truth_ij.npy')
wind = np.load(outdir + 'truth_wind.npy')
Xens = np.load(outdir + casename[c] + '_ens.npy')
loc_ens = np.load(outdir + casename[c] + '_ij.npy')
wind_ens = np.load(outdir + casename[c] + '_wind.npy')
nX, nens, nc, nt = Xens.shape
tt = np.arange(0, nt * 2, 2) ##time in h
cmap = [plt.cm.jet(x) for x in np.linspace(0, 1, nens)]
ii, jj = np.mgrid[0:ni, 0:nj]
u, v = rv.X2uv(ni, nj, X[:, -1])
zeta = rv.uv2zeta(u, v, dx)
##track errors
ax = plt.subplot(231)
# c = ax.contourf(ii, jj, zeta, np.arange(-3, 3, 0.1)*1e-3, cmap='bwr')
# c = ax.contourf(ii, jj, u, np.arange(-70, 70, 5), cmap='bwr')
for m in range(nens):
ax.plot(loc_ens[0, m, 1, 0:nt],
loc_ens[1, m, 1, 0:nt],
color=col[c],
marker=None) ##member
# ax.plot(loc[0, nens, 0:nt], loc_ens[1, nens, 0:nt], 'g', linewidth=3) ##ens mean
ax.plot(loc[0, 0:nt], loc[1, 0:nt], 'k', linewidth=3) ##true
ax.set_xlim(20, 80)
ax.set_ylim(40, 100)
X = np.zeros((ni * nj * nv, nt + 1))
loc = np.zeros((2, nt + 1))
wind = np.zeros((nt + 1, ))
obs = np.zeros((nt + 1, nobs * nv))
iObs = np.zeros((nt + 1, nobs * nv))
jObs = np.zeros((nt + 1, nobs * nv))
vObs = np.zeros((nt + 1, nobs * nv))
##initial state
np.random.seed(0)
X_bkg = rv.make_background_flow(ni, nj, nv, dx, ampl=1e-4)
X[:, 0] = X_bkg + rv.make_state(ni, nj, nv, iStorm, jStorm, Rmw, Vmax, Vout)
for n in range(nt + 1):
print(n)
u, v = rv.X2uv(ni, nj, X[:, n])
zeta = rv.uv2zeta(u, v, dx)
loc[0, n], loc[1, n] = rv.get_center_ij(u, v, dx)
wind[n] = rv.get_max_wind(u, v)
for p in range(nobs):
for v in range(nv):
iObs[n, p * nv +
v] = np.random.uniform(-obs_range, obs_range) + loc[0, n]
jObs[n, p * nv +
v] = np.random.uniform(-obs_range, obs_range) + loc[1, n]
vObs[n, p * nv + v] = v
H = rv.obs_operator(iX, jX, nv, iObs[n, :], jObs[n, :], vObs[n, :])
obs[n, :] = np.dot(H, X[:, n]) + np.random.normal(0.0, obserr,
(nobs * nv, ))
nj = 128
nv = 2 # number of variables, (u, v)
dx = 9000
nt = 12
obs_int = 1
cmap = [plt.cm.jet(x) for x in np.linspace(0, 1, nt)]
X = np.load(outdir + 'truth_state.npy')
obs = np.load(outdir + 'obs.npy')
iObs = np.load(outdir + 'obs_i.npy')
jObs = np.load(outdir + 'obs_j.npy')
nt1, nobs1 = obs.shape
nobs = int(nobs1 / 2)
ii, jj = np.mgrid[0:ni, 0:nj]
u, v = rv.X2uv(ni, nj, X[:, -1])
zeta = rv.uv2zeta(u, v, dx)
plt.figure(figsize=(10, 8))
ax = plt.subplot(111)
for n in range(0, nt, obs_int):
ax.scatter(iObs[n, ::2], jObs[n, ::2], s=50, color=[cmap[n][0:3]])
#for p in range(nobs):
ax.set_xlim(0, ni)
ax.set_ylim(0, nj)
# plt.savefig('1.pdf')
plt.show()
obs, obserr, 0, np.arange(1, 2), 'EnSRF')
Xa[2, :, :] = DA.filter_update(ni, nj, nv, Xb, iX, jX, H, iObs, jObs, vObs,
obs, obserr, 0, np.arange(1, 2), 'EnSRF')
Xa[3, :, :] = DA.filter_update(ni, nj, nv, Xb, iX, jX, H, iObs, jObs, vObs,
obs, obserr, 0, np.arange(1, 2), 'PF')
##plot
plt.switch_backend('Agg')
plt.figure(figsize=(7, 7))
ii, jj = np.mgrid[0:ni, 0:nj]
cmap = [plt.cm.jet(m) for m in np.linspace(0.2, 0.8, nens)]
for i in range(4):
ax = plt.subplot(2, 2, i + 1)
for m in range(nens):
u, v = rv.X2uv(ni, nj, Xa[i, m, :])
ax.contour(ii, jj, u, (-15, 15), colors=[cmap[m][0:3]], linewidths=2)
# u, v = rv.X2uv(ni, nj, np.mean(Xa[i, :, :], axis=0))
# ax.contour(ii, jj, u, (-15, 15), colors='r', linewidths=3)
ut, vt = rv.X2uv(ni, nj, Xt)
ax.contour(ii, jj, ut, (-15, 15), colors='k', linewidths=3)
ax.plot(iObs, jObs, 'k+', markersize=10, markeredgewidth=2)
ax.plot(iout, jout, 'kx', markersize=10, markeredgewidth=2)
ax.set_aspect('equal', 'box')
ax.set_xlim(43, 83)
ax.set_ylim(43, 83)
ax.set_xticks(np.arange(43, 84, 10))
ax.set_xticklabels(np.arange(-20, 21, 10))
ax.set_yticks(np.arange(43, 84, 10))
ax.set_yticklabels(np.arange(-20, 21, 10))
ax.tick_params(labelsize=12)
### Rankine Vortex definition, truth
Rmw = 5 # radius of maximum wind
Vmax = 50 # maximum wind speed
Vout = 0 # wind speed outside of vortex
iStorm = 63 # location of vortex in i, j
jStorm = 63
##truth
Xt = np.load(outdir + 'truth_state.npy')[:, t]
plt.switch_backend('Agg')
plt.figure(figsize=(10, 10))
cmap = [plt.cm.jet(x) for x in np.linspace(0, 1, nens)]
ax = plt.subplot(221)
u, v = rv.X2uv(ni, nj, Xt)
ii, jj = np.mgrid[0:ni, 0:nj]
ax.contourf(ii, jj, u, np.arange(-50, 51, 5), cmap='bwr')
g.plot_contour(ax, ni, nj, Xt, 'black', 3)
g.set_axis(ax, ni, nj)
ax.set_title('Truth', fontsize=20)
ax.tick_params(labelsize=15)
for k in range(len(filter_kind)):
Xa = np.load(outdir + filter_kind[k] + '_ens.npy')[:, :, t]
ax = plt.subplot(2, 2, k + 2)
for n in range(nens):
g.plot_contour(ax, ni, nj, Xa[:, n], [cmap[n][0:3]], 1)
g.plot_contour(ax, ni, nj, Xt, 'black', 3)
g.set_axis(ax, ni, nj)
ax.set_title(filter_kind[k], fontsize=20)
