dapper.mods.Lorenz96.spantini2019

 1"""Try reproducing Figure 9 of Spantini's coupling/nonlinear-transport paper."""
 2
 3import numpy as np
 4
 5import dapper.mods as modelling
 6from dapper.mods.Lorenz96 import Tplot, dstep_dx, step, x0
 7from dapper.tools.localization import nd_Id_localization
 8
 9tseq = modelling.Chronology(0.02, dto=0.4, Ko=2000, Tplot=Tplot, BurnIn=2 * Tplot)
10Nx = 40
11x0 = x0(Nx)
12Dyn = {
13    "M": Nx,
14    "model": step,
15    "linear": dstep_dx,
16    "noise": 0,
17}
18X0 = modelling.GaussRV(mu=x0, C=0.1)
19jj = np.arange(0, Nx, 2)
20Obs = modelling.partial_Id_Obs(Nx, jj)
21Obs["noise"] = 0.5
22Obs["localizer"] = nd_Id_localization((Nx,), (2,), jj)
23HMM = modelling.HiddenMarkovModel(Dyn, Obs, tseq, X0)
24
25####################
26# Suggested tuning
27####################
28
29#                                                      # rmse.a
30# xps += da.Climatology()                              # 3.6
31# xps += da.OptInterp()                                # 2.5
32# xps += da.Var3D(B="eye", xB=4)                       # 2.3
33# xps += da.EnKF ("Sqrt", N=40 , infl=1.10)            # 2.2
34# xps += da.EnKF ("Sqrt", N=60 , infl=1.10)            # 1.3
35# xps += da.EnKF ("Sqrt", N=100, infl=1.10)            # 0.97
36# xps += da.LETKF(        N=40 , infl=1.10, loc_rad=4) # 1.0
37# xps += da.LETKF(        N=60 , infl=1.02, loc_rad=4) # 0.89
38# xps += da.LETKF(        N=100, infl=1.02, loc_rad=4) # 0.84
39# xps += da.iEnKS("Sqrt", N=40 , infl=1.10, Lag=2)     # 0.52
40# xps += da.iEnKS("Sqrt", N=60 , infl=1.10, Lag=2)     # 0.47
41# xps += da.iEnKS("Sqrt", N=100, xN=1, Lag=2)          # 0.43