def forecast(u, dx, dt, nu, kmax, htype):
for k in range(kmax):
u = step(u, dx, dt, nu)
#if htype["perturbation"] == "etkf" or htype["perturbation"] == "po" \
# or htype["perturbation"] == "letkf" or htype["perturbation"] == "srf" \
# or htype["perturbation"] == "mlefb" or htype["perturbation"] == "gradb":
# u[:, 0] = np.mean(u[:, 1:], axis=1)
return u
def gen_true(x, dt, nu, t0true, t0f, nt, na):
nx = x.size
nmem = len(t0f)
u = np.zeros_like(x)
u[0] = 1
dx = x[1] - x[0]
ut = np.zeros((na, nx))
u0 = np.zeros((nx, nmem))
for k in range(t0true):
u = step(u, dx, dt, nu)
ut[0, :] = u
for i in range(na-1):
for k in range(nt):
u = step(u, dx, dt, nu)
j = (i + 1) * nt + k
if j in t0f:
u0[:, t0f.index(j)] = u
ut[i+1, :] = u
return ut, u0