for n in range(nens):
iStorm_ens[n] = iStorm + iBias + np.random.normal(0, 1) * Csprd
jStorm_ens[n] = jStorm + jBias + np.random.normal(0, 1) * Csprd
Rmw_n = Rmw + np.random.normal(0, 1) * Rsprd
Vmax_n = Vmax + np.random.normal(0, 1) * Vsprd
Vout_n = Vout + np.random.normal(0, 1) * 0.0
Xb[n, :] = rv.make_state(ni, nj, nv, iStorm_ens[n], jStorm_ens[n],
Rmw_n, Vmax_n, Vout_n)
###Observations
th = np.random.uniform(0, 360) * np.pi / 180 * np.array([1, 1])
iObs = iStorm + obsR * np.sin(th)
jObs = iStorm + obsR * np.cos(th)
vObs = np.array([0, 1])
nobs = iObs.size # number of observation points
H = rv.obs_operator(iX, jX, nv, iObs, jObs, vObs)
obs = np.dot(H, Xt) + np.random.normal(0.0, obserr, nobs)
##Run filter
Xa = Xb.copy()
krange = np.arange(1, ns + 1)
Xa = DA.filter_update(ni, nj, nv, Xb, iX, jX, H, iObs, jObs, vObs, obs,
obserr, localize_cutoff, krange, filter_kind)
###Diagnose
###domain-averaged (near storm region) state (u,v) error:
for m in range(nens):
u, v = rv.X2uv(ni, nj, Xa[m, :])
square_err = (u - ut)**2 + (v - vt)**2
state_error[realize, m] = np.sqrt(
np.mean(square_err[iStorm - 20:iStorm + 20,
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, ))
if n < nt:
X[:, n + 1] = rv.advance_time(ni, nj, X[:, n], dx,
int(cycle_period / dt), dt, diss)
np.save(outdir + 'truth_state.npy', X)
np.save(outdir + 'truth_ij.npy', loc)
np.save(outdir + 'truth_wind.npy', wind)
np.save(outdir + 'obs.npy', obs)
np.save(outdir + 'obs_i.npy', iObs)
np.save(outdir + 'obs_j.npy', jObs)
np.save(outdir + 'obs_v.npy', vObs)
#!/usr/bin/env python
import numpy as np
import rankine_vortex as rv
import graphics as g
import config as p
import matplotlib.pyplot as plt
import data_assimilation as DA
plt.switch_backend('Agg')
x_in = 17
y_in = 29
iout1 = np.array([x_in])
jout1 = np.array([y_in])
H = rv.obs_operator(p.iX, p.jX, p.nv, iout1, jout1, p.iSite, p.jSite)
obs = np.matmul(H, p.Xt) + np.random.normal(0.0, p.obserr)
# Xa = DA.EnSRF(p.ni, p.nj, p.nv, p.Xb, p.iX, p.jX, H, iout1, jout1, obs, p.obserr, p.localize_cutoff)
Xa = DA.RHF(p.ni, p.nj, p.nv, p.Xb, p.iX, p.jX, H, iout1, jout1, obs, p.obserr,
p.localize_cutoff)
for x_in in range(41):
for y_in in range(41):
iout2 = np.array([x_in])
jout2 = np.array([y_in])
plt.figure(figsize=(3, 3))
###scatter plot
ax = plt.subplot(1, 1, 1)
Hout = rv.obs_operator(p.iX, p.jX, p.nv, iout1, jout1, p.iSite,
import matplotlib.pyplot as plt
plt.switch_backend('Agg')
# for x_in in range(41):
# for y_in in range(41):
for x_in in [17]:
for y_in in [29]:
iout = np.array([x_in])
jout = np.array([y_in])
plt.figure(figsize=(3, 2))
###plot histogram
ax = plt.subplot(1, 1, 1)
Hout = rv.obs_operator(p.iX, p.jX, p.nv, iout, jout, p.iSite, p.jSite)
prior_err = np.dot(Hout, p.Xb.T) - np.dot(Hout, p.Xt)
err_mean = np.mean(prior_err)
err_std = np.std(prior_err)
ii = np.arange(-50, 50, 1)
jj = np.exp(-0.5 * (ii - err_mean)**2 / err_std**2) / np.sqrt(
2 * np.pi) / err_std
jj0 = g.hist_normal(ii, prior_err[0, :])
ax.plot(ii, jj0, 'k', linewidth=2, label='Sample')
ax.plot(ii, jj, 'r:', linewidth=1, label='Gaussian')
# ax.legend(fontsize=12, loc=1)
cmap = [plt.cm.jet(x) for x in np.linspace(0, 1, p.nens_show)]
for n in range(p.nens_show):
ax.scatter(prior_err[0, n], 0, s=40, color=[cmap[n][0:3]])
jStorm_ens[n] = jStorm + jBias + np.random.normal(0, 1) * Csprd
Rmw_n = Rmw + np.random.normal(0, 1) * Rsprd
Vmax_n = Vmax + np.random.normal(0, 1) * Vsprd
Vout_n = Vout + np.random.normal(0, 1) * 0.0
Xb[n, :] = rv.make_state(ni, nj, nv, iStorm_ens[n], jStorm_ens[n], Rmw_n,
Vmax_n, Vout_n)
###Observations
iObs = np.array([63, 63])
jObs = np.array([65, 65])
# iObs = np.array([66.53, 66.53])
# jObs = np.array([66.53, 66.53])
vObs = np.array([0, 1])
nobs = iObs.size # number of observation points
obserr = 4.0 # observation error spread
H = rv.obs_operator(iX, jX, nv, iObs, jObs, vObs)
obs = np.dot(H, Xt) + np.array([2.0, -2.0])
iout = np.array([70])
jout = np.array([55])
vout = np.array([0])
Hout = rv.obs_operator(iX, jX, nv, iout, jout, vout)
##Run filter
Xa = np.zeros((4, nens, ni * nj * nv))
Xa[0, :, :] = Xb
Xa[1, :, :] = DA.filter_update(ni, nj, nv, Xb, iX, jX, H, iObs, jObs, vObs,
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,
