def L96_table():
import dapper.mods.Lorenz96 as model
from dapper.mods.Lorenz96.sakov2008 import HMM as _HMM
model.Force = 8.0 # undo pinheiro2019
HMM = _HMM.copy()
HMM.t.BurnIn = 0
HMM.t.KObs = 10
dpr.set_seed(3000)
# xps
xps = dpr.xpList()
xps += da.Climatology()
xps += da.OptInterp()
xps += da.Var3D(xB=0.02)
xps += da.ExtKF(infl=6)
xps += da.EnKF("PertObs", N=40, infl=1.06)
xps += da.EnKF("Sqrt", N=28, infl=1.02, rot=True)
xps += da.EnKF_N(N=24, rot=True)
xps += da.EnKF_N(N=24, rot=True, xN=2)
xps += da.iEnKS("Sqrt", N=40, infl=1.01, rot=True)
xps += da.LETKF(N=7, rot=True, infl=1.04, loc_rad=4)
xps += da.SL_EAKF(N=7, rot=True, infl=1.07, loc_rad=6)
xps.launch(HMM, store_u=True)
table = xps.tabulate_avrgs(statkeys, decimals=4)
return table.splitlines(True)
def L63_gen():
from dapper.mods.Lorenz63.sakov2012 import HMM as _HMM
HMM = _HMM.copy()
HMM.tseq.BurnIn = 0
HMM.tseq.Ko = 10
# xps
xps = dpr.xpList()
xps += da.Climatology()
xps += da.OptInterp()
xps += da.Persistence()
xps += da.PreProg(lambda k, xx, yy: xx[k])
xps += da.Var3D(xB=0.1)
xps += da.ExtKF(infl=90)
xps += da.EnKF("Sqrt", N=3, infl=1.30)
xps += da.EnKF("Sqrt", N=10, infl=1.02, rot=True)
xps += da.EnKF("PertObs", N=500, infl=0.95, rot=False)
xps += da.EnKF_N(N=10, rot=True)
xps += da.iEnKS("Sqrt", N=10, infl=1.02, rot=True)
xps += da.PartFilt(N=100, reg=2.4, NER=0.3)
xps += da.PartFilt(N=800, reg=0.9, NER=0.2)
xps += da.PartFilt(N=4000, reg=0.7, NER=0.05)
xps += da.PFxN(xN=1000, N=30, Qs=2, NER=0.2)
xps += da.OptPF(N=100, Qs=2, reg=0.7, NER=0.3)
xps += da.EnKS("Serial", N=30, Lag=1)
xps += da.EnRTS("Serial", N=30, DeCorr=0.99)
for xp in xps:
xp.seed = 3000
# Run
xps.launch(HMM, False, store_u=True)
return xps
def L63_gen():
from dapper.mods.Lorenz63.sakov2012 import HMM as _HMM
HMM = _HMM.copy()
HMM.t.BurnIn = 0
HMM.t.KObs = 10
dpr.set_seed(3000)
# xps
xps = dpr.xpList()
xps += da.Climatology()
xps += da.OptInterp()
xps += da.Var3D(xB=0.1)
xps += da.ExtKF(infl=90)
xps += da.EnKF("Sqrt", N=3, infl=1.30)
xps += da.EnKF("Sqrt", N=10, infl=1.02, rot=True)
xps += da.EnKF("PertObs", N=500, infl=0.95, rot=False)
xps += da.EnKF_N(N=10, rot=True)
xps += da.iEnKS("Sqrt", N=10, infl=1.02, rot=True)
xps += da.PartFilt(N=100, reg=2.4, NER=0.3)
xps += da.PartFilt(N=800, reg=0.9, NER=0.2)
xps += da.PartFilt(N=4000, reg=0.7, NER=0.05)
xps += da.PFxN(xN=1000, N=30, Qs=2, NER=0.2)
# Run
xps.launch(HMM, False, store_u=True)
return xps
def L96_gen():
import dapper.mods.Lorenz96 as model
from dapper.mods.Lorenz96.sakov2008 import HMM as _HMM
model.Force = 8.0 # undo pinheiro2019
HMM = _HMM.copy()
HMM.tseq.BurnIn = 0
HMM.tseq.Ko = 10
# xps
xps = dpr.xpList()
xps += da.Climatology()
xps += da.OptInterp()
xps += da.Persistence()
xps += da.PreProg(lambda k, xx, yy: xx[k])
xps += da.Var3D(xB=0.02)
xps += da.ExtKF(infl=6)
xps += da.EnKF("PertObs", N=40, infl=1.06)
xps += da.EnKF("Sqrt", N=28, infl=1.02, rot=True)
xps += da.EnKF_N(N=24, rot=True)
xps += da.EnKF_N(N=24, rot=True, xN=2)
xps += da.iEnKS("Sqrt", N=40, infl=1.01, rot=True)
xps += da.LETKF(N=7, rot=True, infl=1.04, loc_rad=4)
xps += da.SL_EAKF(N=7, rot=True, infl=1.07, loc_rad=6)
for xp in xps:
xp.seed = 3000
xps.launch(HMM, store_u=True)
return xps
def data():
from dapper.mods.LA.small import HMM as _HMM
HMM = _HMM.copy()
HMM.t.BurnIn = 0
HMM.t.KObs = 10
xps = dpr.xpList(unique=True)
# yapf: disable
xps += da.EnKF('Sqrt' , N=20)
xps += da.EnKF('PertObs', N=20)
xps += da.EnKF('DEnKF' , N=20)
for Lag in [0, 1, 3]:
xps += da.EnKS('Sqrt' , N=20, Lag=Lag)
xps += da.EnKS('PertObs', N=20, Lag=Lag)
xps += da.EnKS('DEnKF' , N=20, Lag=Lag)
for nIter in [1, 4]:
for MDA in [False, True]:
xps += da.iEnKS('Sqrt' , N=20, Lag=Lag, nIter=nIter, MDA=MDA)
xps += da.iEnKS('PertObs', N=20, Lag=Lag, nIter=nIter, MDA=MDA)
xps += da.iEnKS('Order1' , N=20, Lag=Lag, nIter=nIter, MDA=MDA)
# yapf: enable
for xp in xps:
xp.seed = 3000
xps.launch(HMM, store_u=True, save_as=False)
print(xps.tabulate_avrgs(["rmse.a", "rmse.f", "rmse.u", "rmse.s"]))
return xps
def test_L63():
from dapper.mods.Lorenz63.sakov2012 import HMM as _HMM
xps = dpr.xpList()
xps += da.EnKF("Sqrt", N=10, infl=1.02, rot=True)
xps += da.PartFilt(N=20, reg=2.4, NER=0.3)
xps += da.OptInterp()
# xps += da.iEnKS('Sqrt', N=10, infl=1.02,rot=True)
HMM = _HMM.copy()
HMM.tseq.BurnIn = HMM.tseq.dto
HMM.tseq.Ko = 1
xps.launch(
HMM,
free=False,
statkeys=True,
liveplots=None,
store_u=False,
fail_gently=False,
save_as=False,
)
print(xps.tabulate_avrgs(["rmse.a"]))
# Disabled coz magic doesn't work any more on python 3.7
# spell_out(HMM)
# spell_out(xps[-1])
# spell_out(xps[-1].stats)
# spell_out(xps[-1].avrgs)
assert True # An assertion for pytest to count
return HMM, xps # Return useful stuff
def test_L63():
from dapper.mods.Lorenz63.sakov2012 import HMM as _HMM
xps = dpr.xpList()
xps += da.EnKF('Sqrt', N=10, infl=1.02, rot=True)
xps += da.PartFilt(N=20, reg=2.4, NER=0.3)
xps += da.OptInterp()
# xps += da.iEnKS('Sqrt', N=10, infl=1.02,rot=True)
HMM = _HMM.copy()
HMM.t.BurnIn = HMM.t.dtObs
HMM.t.KObs = 1
xps.launch(HMM,
free=False,
liveplots="all",
store_u=False,
fail_gently=False)
for xp in xps:
xp.stats.replay("all")
xp.stats.replay(t2=1)
xp.stats.replay(t2=0.0)
xp.stats.replay(t2=0.3)
xp.stats.replay(t2=0.8)
xp.stats.replay(t2=0.8, t1=0.2)
xp.stats.replay(t2=np.inf)
xp.stats.replay(t2=np.inf, speed=1)
xp.stats.replay(t2=np.inf, pause_a=0, pause_f=0)
assert True # An assertion for pytest to count
return HMM, xps # Return useful stuff
def test_L96():
xps = dpr.xpList()
from dapper.mods.Lorenz96.sakov2008 import HMM as _HMM
xps += da.EnKF('PertObs', N=40, infl=1.06)
xps += da.EnKF('Serial', N=28, infl=1.02, rot=True)
xps += da.OptInterp()
xps += da.Var3D(xB=0.02)
xps += da.ExtKF(infl=10)
xps += da.LETKF(N=6, rot=True, infl=1.05, loc_rad=4, taper='Step')
# from dapper.mods.Lorenz96.bocquet2015loc import HMM
# xps += da.EnKF_N( N=24, rot=True ,infl=1.01)
# xps += da.PartFilt(N=3000,NER=0.20,reg=1.2)
# xps += da.PFxN( N=1000,xN=100, NER=0.9,Qs=0.6)
HMM = _HMM.copy()
# HMM.t.BurnIn = 10*HMM.t.dtObs
# HMM.t.KObs = 30
HMM.t.BurnIn = HMM.t.dtObs
HMM.t.KObs = 2
xps.launch(HMM,
free=False,
liveplots="all",
store_u=False,
fail_gently=False,
save_as=False)
for xp in xps:
xp.stats.replay("all")
xp.stats.replay(t2=1)
xp.stats.replay(t2=0.0)
xp.stats.replay(t2=0.3)
xp.stats.replay(t2=0.8)
xp.stats.replay(t2=0.8, t1=0.2)
xp.stats.replay(t2=np.inf)
xp.stats.replay(t2=np.inf, speed=1)
xp.stats.replay(t2=np.inf, pause_a=0, pause_f=0)
assert True # An assertion for pytest to count
return HMM, xps # Return useful stuff
def test_L96():
xps = dpr.xpList()
from dapper.mods.Lorenz96.sakov2008 import HMM as _HMM
xps += da.EnKF("PertObs", N=40, infl=1.06)
xps += da.EnKF("Serial", N=28, infl=1.02, rot=True)
xps += da.OptInterp()
xps += da.Var3D(xB=0.02)
xps += da.ExtKF(infl=10)
xps += da.LETKF(N=6, rot=True, infl=1.05, loc_rad=4, taper="Step")
# from dapper.mods.Lorenz96.bocquet2015loc import HMM
# xps += da.EnKF_N( N=24, rot=True ,infl=1.01)
# xps += da.PartFilt(N=3000,NER=0.20,reg=1.2)
# xps += da.PFxN( N=1000,xN=100, NER=0.9,Qs=0.6)
HMM = _HMM.copy()
# HMM.tseq.BurnIn = 10*HMM.tseq.dto
# HMM.tseq.Ko = 30
HMM.tseq.BurnIn = HMM.tseq.dto
HMM.tseq.Ko = 2
xps.launch(
HMM,
free=False,
statkeys=True,
liveplots=None,
store_u=False,
fail_gently=False,
save_as=False,
)
print(xps.tabulate_avrgs(["rmse.a"]))
# spell_out(HMM)
# spell_out(xps[-1])
# spell_out(xps[-1].stats)
# spell_out(xps[-1].avrgs)
assert True # An assertion for pytest to count
return HMM, xps # Return useful stuff
# #### Load experiment setup: the hidden Markov model (HMM)
from dapper.mods.Lorenz63.sakov2012 import HMM # isort:skip
# #### Generate the same random numbers each time this script is run
seed = dpr.set_seed(3000)
# #### Simulate synthetic truth (xx) and noisy obs (yy)
HMM.t.T = 30 # shorten experiment
xx, yy = HMM.simulate()
# #### Specify a DA method configuration ("xp" for "experiment")
xp = da.EnKF('Sqrt', N=10, infl=1.02, rot=True)
# xp = da.Var3D()
# xp = da.PartFilt(N=100, reg=2.4, NER=0.3)
# #### Assimilate yy, knowing the HMM; xx is used to assess the performance
xp.assimilate(HMM, xx, yy, liveplots=not nb)
# #### Average the time series of various statistics
xp.stats.average_in_time()
# #### Print some averages
print(xp.avrgs.tabulate(['rmse.a', 'rmv.a']))
core.diffusion = xp.Diffus1
xx, yy = HMMs("Tay2", xp.resoltn, R=xp.ObsErr2).simulate()
if xp.resoltn == "Low":
xx = xx[::2]
hmm = HMMs(xp.stepper, xp.resoltn, R=xp.ObsErr2)
return hmm, xx, yy
# #### DA method and experiment listing
xps = dpr.xpList()
for resolution in ["Low", "High"]:
for step_kind in ["RK4", "EM"]:
for diffusion_stddev in [0.1, 0.25, 0.5, 0.75, 1.0]:
for obs_noise_variance in [0.1, 0.25, 0.5, 0.75, 1.0]:
xp = da.EnKF('PertObs', N=100)
xp.resoltn = resolution
xp.stepper = step_kind
xp.Diffus1 = diffusion_stddev
xp.ObsErr2 = obs_noise_variance
xps.append(xp)
# #### Run experiments
save_as = xps.launch(HMMs(), __file__, setup=setup)
# #### Load data
# This block is redundant if running in the same script
# as generates the data and not using multiprocessing.
# +
import dapper as dpr
import dapper.da_methods as da
seed = dpr.set_seed(3000)
# #### DA method configurations
xps = dpr.xpList()
from dapper.mods.Lorenz63.sakov2012 import HMM # Expected rmse.a:
xps += da.Climatology() # 7.6
xps += da.OptInterp() # 1.25
xps += da.Var3D(xB=0.1) # 1.03
xps += da.ExtKF(infl=90) # 0.87
xps += da.EnKF('Sqrt', N=3, infl=1.30) # 0.82
xps += da.EnKF('Sqrt', N=10, infl=1.02, rot=True) # 0.63
xps += da.EnKF('PertObs', N=500, infl=0.95, rot=False) # 0.56
xps += da.EnKF_N(N=10, rot=True) # 0.54
xps += da.iEnKS('Sqrt', N=10, infl=1.02, rot=True) # 0.31
xps += da.PartFilt(N=100, reg=2.4, NER=0.3) # 0.38
xps += da.PartFilt(N=800, reg=0.9, NER=0.2) # 0.28
# xps += da.PartFilt( N=4000, reg=0.7 , NER=0.05) # 0.27
# xps += da.PFxN(xN=1000, N=30 , Qs=2 , NER=0.2) # 0.56
# #### With Lorenz-96 instead
# +
# from dapper.mods.Lorenz96.sakov2008 import HMM # Expected rmse.a:
# xps += da.Climatology() # 3.6
# xps += da.OptInterp() # 0.95
