from dapper.tools.localization import nd_Id_localization
# Use small dt to "cope with" ocean sector blow up
# (due to spatially-constant infl)
OneYear = 0.05 * (24 / 6) * 365
t = dpr.Chronology(0.005,
dtObs=0.05,
T=110 * OneYear,
Tplot=Tplot,
BurnIn=10 * OneYear)
land_sites = np.arange(Nx // 2)
ocean_sites = np.arange(Nx // 2, Nx)
jj = land_sites
Obs = dpr.partial_Id_Obs(Nx, jj)
Obs['noise'] = 1
Obs['localizer'] = nd_Id_localization((Nx, ), (1, ), jj)
HMM = dpr.HiddenMarkovModel(Dyn,
Obs,
t,
X0,
LP=LPs(jj),
sectors={
'land': land_sites,
'ocean': ocean_sites
})
####################
# Suggested tuning
from dapper.mods.DoublePendulum import step, x0, LP_setup, dstep_dx
t = dpr.Chronology(0.01, dkObs=100, T=30, BurnIn=10)
Dyn = {
'M': len(x0),
'model': step,
'noise': 0,
'linear': dstep_dx,
}
X0 = dpr.GaussRV(mu=x0, C=0.01**2)
jj = [0, 2]
Obs = dpr.partial_Id_Obs(len(x0), jj)
Obs['noise'] = 0.1**2
HMM = dpr.HiddenMarkovModel(Dyn, Obs, t, X0, LP=LP_setup(jj))
####################
# Suggested tuning
####################
# from dapper.mods.DoublePendulum.settings101 import HMM # Expct rmse.a:
# HMM.t.dkObs = anything
# xps += Climatology() # 5
# xps += OptInterp() # 2.5
# HMM.t.dkObs = 7 # <-- max dkObs for tolerable performance with Var3D
# xps += Var3D(xB=0.1) # 0.81
t = dpr.Chronology(dt=0.005, dtObs=0.05, T=4**3, BurnIn=6)
Dyn = {
'M': LUV.M,
'model': dpr.with_rk4(LUV.dxdt, autonom=True),
'noise': 0,
'linear': LUV.dstep_dx,
}
X0 = dpr.GaussRV(mu=LUV.x0, C=0.01)
R = 1.0
jj = np.arange(nU)
Obs = dpr.partial_Id_Obs(LUV.M, jj)
Obs['noise'] = R
other = {'name': utils.rel2mods(__file__)+'_full'}
HMM_full = dpr.HiddenMarkovModel(Dyn, Obs, t, X0, **other)
################
# Truncated
################
# Just change dt from 005 to 05
t = dpr.Chronology(dt=0.05, dtObs=0.05, T=4**3, BurnIn=6)
Dyn = {
'M': nU,
"""As in Anderson 2010 rank histogram filter"""
import numpy as np
import dapper as dpr
from dapper.mods.Lorenz63 import Tplot, dstep_dx, step, x0
t = dpr.Chronology(0.01, dkObs=12, KObs=1000, Tplot=Tplot, BurnIn=4 * Tplot)
Nx = len(x0)
Dyn = {'M': Nx, 'model': step, 'linear': dstep_dx, 'noise': 0}
X0 = dpr.GaussRV(C=2, mu=x0)
Obs = dpr.partial_Id_Obs(Nx, np.arange(Nx))
Obs['noise'] = 8.0
HMM = dpr.HiddenMarkovModel(Dyn, Obs, t, X0)
####################
# Suggested tuning
####################
# Compare with Anderson's figure 10.
# Benchmarks are fairly reliable (KObs=2000):
# from dapper.mods.Lorenz63.anderson2010rhf import HMM # rmse.a
# xps += SL_EAKF(N=20,infl=1.01,rot=True,loc_rad=np.inf) # 0.87
# xps += EnKF_N (N=20,rot=True) # 0.87
# xps += RHF (N=50,infl=1.10) # 1.28
# xps += RHF (N=50,infl=0.95,rot=True) # 0.94
# xps += RHF (N=20,infl=0.95,rot=True) # 1.07