dart tutorial sec’on 12: adap’ve inflaonadap’ve observaon space inflaon in dart try this in...
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DARTTutorialSec'on12:Adap'veInfla'on
Varianceinfla'onforobserva'ons:Anadap'veerrortolerantfilter
1.Forobservedvariable,havees'mateofprior-observedinconsistency.2.Expected(prior_mean–observa'on)=
Assumesthatpriorandobserva'onaresupposedtobeunbiased.Isitmodelerrororrandomchance?
−4 −2 0 2 40
0.2
0.4
0.6
0.8
Prob
abilit
y
Prior PDF
S.D.
Obs. Likelihood
S.D.Expected Separation
Actual 4.714 SDs
σprior2 +σobs
2
DARTTutorialSec'on12:Slide2
Varianceinfla'onforobserva'ons:Anadap'veerrortolerantfilter
1.Forobservedvariable,havees'mateofprior-observedinconsistency.2.Expected(prior_mean–observa'on)=3.Infla'ngincreasesexpectedsepara'on
Increases‘apparent’consistencybetweenpriorandobserva'on.
−4 −2 0 2 40
0.2
0.4
0.6
0.8
Prob
abilit
y
Prior PDF Obs. Likelihood
S.D.Inflated S.D. Expected Separation
Actual 3.698 SDs
σprior2 +σobs
2
DARTTutorialSec'on12:Slide3
Varianceinfla'onforobserva'ons:Anadap'veerrortolerantfilter
DistanceDfrompriormeanytoobsisProby0isobservedgivenλ:
−4 −2 0 2 40
0.2
0.4
0.6
0.8
Prob
abilit
y
Prior PDF Obs. Likelihood
S.D.Inflated S.D. Expected Separation
Actual 3.698 SDs
N 0, λσprior
2 +σobs2( )= N 0,θ( )
p yo |λ( )= 2πθ2( )−1 2 exp −D2 2θ2( )
DARTTutorialSec'on12:Slide4
Varianceinfla'onforobserva'ons:Anadap'veerrortolerantfilter
0 1 2 3 4 5 60
1
2
Obs. Space Inflation Factor: λ
Prior λ PDF
−1 0 1 2 3 40
0.2
0.4
0.6
Observation: y
Obs. LikelihoodPrior PDF
UseBayesiansta's'cstogetes'mateofinfla'onfactorλ.
AssumepriorisGaussian: p λ,tk |Ytk−1( )= N λp ,σλ,p2( )
DARTTutorialSec'on12:Slide5
Varianceinfla'onforobserva'ons:Anadap'veerrortolerantfilter
0 1 2 3 4 5 60
1
2
Obs. Space Inflation Factor: λ
Prior λ PDF
−1 0 1 2 3 40
0.2
0.4
0.6
Observation: y
Obs. LikelihoodPrior PDF
UseBayesiansta's'cstogetes'mateofinfla'onfactorλ.
We’veassumedaGaussianforprior.RecallthatcanbeevaluatedFromnormalPDF.
p λ,tk |Ytk−1( )
p yk |λ( )
p λ,tk |Ytk( )= p yk |λ( )p λ,tk |Ytk−1( ) / normalizationDARTTutorialSec'on12:Slide6
Varianceinfla'onforobserva'ons:Anadap'veerrortolerantfilter
0 1 2 3 4 5 60
1
2
Obs. Space Inflation Factor: λ
Prior λ PDF
−1 0 1 2 3 40
0.2
0.4
0.6
Observation: y
Obs. LikelihoodInflated Prior λ = 0.75
UseBayesiansta's'cstogetes'mateofinfla'onfactorλ.
GetfromnormalPDF.Mul'plybytoget
p λ,tk |Ytk( )= p yk |λ( )p λ,tk |Ytk−1( ) / normalization
p yk |λ= 0.75( )
p λ= 0.75,tk |Ytk−1( )
p λ= 0.75,tk |Ytk( )
DARTTutorialSec'on12:Slide7
Varianceinfla'onforobserva'ons:Anadap'veerrortolerantfilter
0 1 2 3 4 5 60
1
2
Obs. Space Inflation Factor: λ
Prior λ PDF
−1 0 1 2 3 40
0.2
0.4
0.6
Observation: y
Obs. LikelihoodInflated Prior λ = 1.5
UseBayesiansta's'cstogetes'mateofinfla'onfactorλ.
GetfromnormalPDF.Mul'plybytoget
p λ,tk |Ytk( )= p yk |λ( )p λ,tk |Ytk−1( ) / normalization
p yk |λ=1.50( )
p λ=1.50,tk |Ytk−1( )
p λ=1.50,tk |Ytk( )
DARTTutorialSec'on12:Slide8
Varianceinfla'onforobserva'ons:Anadap'veerrortolerantfilter
0 1 2 3 4 5 60
1
2
Obs. Space Inflation Factor: λ
Prior λ PDF
−1 0 1 2 3 40
0.2
0.4
0.6
Observation: y
Obs. LikelihoodInflated Prior λ = 2.25
UseBayesiansta's'cstogetes'mateofinfla'onfactorλ.
GetfromnormalPDF.Mul'plybytoget
p λ,tk |Ytk( )= p yk |λ( )p λ,tk |Ytk−1( ) / normalization
p yk |λ= 2.25( )
p λ= 2.25,tk |Ytk−1( )
p λ= 2.25,tk |Ytk( )
DARTTutorialSec'on12:Slide9
Varianceinfla'onforobserva'ons:Anadap'veerrortolerantfilter
0 1 2 3 4 5 60
1
2
Obs. Space Inflation Factor: λ
Prior λ PDF
Likelihood y observed given λPosterior
−1 0 1 2 3 40
0.2
0.4
0.6
Observation: y
Obs. Likelihood
UseBayesiansta's'cstogetes'mateofinfla'onfactorλ.
Repeatforarangeofvaluesofλ.Nowmustgetposteriorinsameformasprior(Gaussian).
p λ,tk |Ytk( )= p yk |λ( )p λ,tk |Ytk−1( ) / normalizationDARTTutorialSec'on12:Slide10
Varianceinfla'onforobserva'ons:Anadap'veerrortolerantfilter
1 20
1
2
Obs. Space Inflation Factor: λ
Prior λ PDF
Posterior
−1 0 1 2 3 40
0.2
0.4
0.6
Observation: y
Obs. Likelihood
UseBayesiansta's'cstogetes'mateofinfla'onfactorλ.
Verylicleinforma'onaboutλinasingleobserva'on.Posteriorandpriorareverysimilar.Normalizedposteriorindis'nguishablefromprior.
p λ,tk |Ytk( )= p yk |λ( )p λ,tk |Ytk−1( ) / normalizationDARTTutorialSec'on12:Slide11
Varianceinfla'onforobserva'ons:Anadap'veerrortolerantfilter
1 2
−0.01
0
0.01
Obs. Space Inflation Factor: λ
λ: Posterior − Prior
Max density shifted to right
−1 0 1 2 3 40
0.2
0.4
0.6
Observation: y
Obs. Likelihood
UseBayesiansta's'cstogetes'mateofinfla'onfactorλ.
Verylicleinforma'onaboutλinasingleobserva'on.Posteriorandpriorareverysimilar.Differenceshowsslightshiftolargervaluesofλ.
p λ,tk |Ytk( )= p yk |λ( )p λ,tk |Ytk−1( ) / normalizationDARTTutorialSec'on12:Slide12
Varianceinfla'onforobserva'ons:Anadap'veerrortolerantfilter
1 20
1
2
Obs. Space Inflation Factor: λ
Prior λ PDFFind Max by search
Max is new λ mean
−1 0 1 2 3 40
0.2
0.4
0.6
Observation: y
Obs. Likelihood
UseBayesiansta's'cstogetes'mateofinfla'onfactorλ.
Oneop'onistouseGaussianpriorforλ.Selectmax(mode)ofposteriorasmeanofupdatedGaussian.Doafitforupdatedstandarddevia'on.
p λ,tk |Ytk( )= p yk |λ( )p λ,tk |Ytk−1( ) / normalizationDARTTutorialSec'on12:Slide13
Varianceinfla'onforobserva'ons:Anadap'veerrortolerantfilter
A. Compu'ngupdatedinfla'onmean,.
Modeofcanbefoundanaly'cally!Solvingleadsto6thorderpolyinθ.
Thiscanbereducedtoacubicequa'onandsolvedtogivemode.Newissettothemode.Thisisrela'velycheapcomparedtocompu'ngregressions.
λu
p yk |λ( )p λ,tk |Ytk−1( )
∂ p yk |λ( )p λ,tk |Ytk−1( )⎡⎣⎢
⎤⎦⎥ /∂y= 0
λu
DARTTutorialSec'on12:Slide14
Varianceinfla'onforobserva'ons:Anadap'veerrortolerantfilter
B.Compu'ngupdatedinfla'onvariance,.
1.Evaluatenumeratoratmeanandsecondpoint,e.g.
2. Findsogoesthroughand
3. Computeaswhere
σλ,u2
λu λu +σλ,p
σλ,u2
N λu ,σλ,u2( )
p λu( ) p λu +σλ,p( )
σλ,u2 =−σλ,p
2 / 2 ln r r= p λu +σλ,p( ) / p λu( )
DARTTutorialSec'on12:Slide15
Observa'onSpaceComputa'onswithAdap'veErrorCorrec'on
−4 −2 0 2 40
0.2
0.4
0.6
Prob
abilit
y
Prior PDFObs. Likelihood
1. Computeupdatedinfla'ondistribu'on,.
p λ,tk |Ytk( )
DARTTutorialSec'on12:Slide16
Observa'onSpaceComputa'onswithAdap'veErrorCorrec'on
−4 −2 0 2 40
0.2
0.4
0.6
Prob
abilit
y Obs. Likelihood
1. Computeupdatedinfla'ondistribu'on,.2. Inflateensembleusingmeanofupdatedλdistribu'on.
DARTTutorialSec'on12:Slide17
p λ,tk |Ytk( )
Observa'onSpaceComputa'onswithAdap'veErrorCorrec'on
−4 −2 0 2 40
0.2
0.4
0.6
Prob
abilit
y Obs. Likelihood
1. Computeupdatedinfla'ondistribu'on,.2. Inflateensembleusingmeanofupdatedλdistribu'on.3. Computeposteriorforyusinginflatedprior.
DARTTutorialSec'on12:Slide18
p λ,tk |Ytk( )
Observa'onSpaceComputa'onswithAdap'veErrorCorrec'on
−4 −2 0 2 40
0.2
0.4
0.6
Prob
abilit
y Obs. Likelihood
1. Computeupdatedinfla'ondistribu'on,.2. Inflateensembleusingmeanofupdatedλdistribu'on.3. Computeposteriorforyusinginflatedprior.4. ComputeincrementsfromORIGINALpriorensemble.
DARTTutorialSec'on12:Slide19
p λ,tk |Ytk( )
Adap'veObserva'onSpaceInfla'oninDART
Observa'onspaceadap'veinfla'onisnotsupportedinDARTManhacanrelease
DARTTutorialSec'on12:Slide20
Adap'veObserva'onSpaceInfla'oninDART
TrythisinLorenz96(verifyotheraspectsofinput.nml).Use40memberensemble.(setens_size=40infilter_nml).Setredvaluesasaboveforadap'veobserva'onspaceinfla'on.
inf_flavor =1, 0,inf_ini'al_from_restart =.false., .false.,inf_sd_ini'al_from_restart =.false., .false.,inf_determinis'c =.true., .true.,inf_diag_file_name ='prior_inflate_diag', 'post_inflate_diag',inf_ini'al =1.00, 1.0,inf_sd_ini'al =0.2, 0.0,inf_damping =1.0, 1.0,inf_lower_bound =1.0, 1.0,inf_upper_bound =1000000.0, 1000000.0,inf_sd_lower_bound =0.0, 0.0,
BeforeAssimila'on
AferAssimila'on
Flavor: 0=>NONE 1=>notsupported 3=>physicalspace
Ini'alinfla'onvalueIni'alstandarddevia'on
Lowerboundons.d.
Observa'onspaceadap'veinfla'onisnotsupportedinDARTManhacanrelease
DARTTutorialSec'on12:Slide21
priorinfla'on
posteriorinfla'on
Adap'veObserva'onSpaceInfla'oninDART
Runthefilter.Examineperformancewithplot_total_errinMatlab.Timeseriesofinfla'onandstandarddevia'onareinprior_inflate_diag.Infla'onadjustswith'me.Standarddevia'onisnon-increasing.
DARTTutorialSec'on12:Slide22
AlgorithmicVariants
1.Increaseprioryvariancebyaddingrandomgaussiannoise.Asopposedto‘determinis'c’linearinfla'ng.Setinf_determinis9cinfirstcolumnto.false.Changeitbackto.true.afercheckingthisout.
2.Justhaveafixedvalueforobs.spaceλ.
Cheap,handlesblowupofstatevarsunconstrainedbyobs.Wealreadytriedthisinsec'on9.
DARTTutorialSec'on12:Slide23
AlgorithmicVariants
3.Fixvalueofλstandarddevia'on,.Reducescost,computa'onofcansome'mesbetricky.Avoidsgepngsmall(errormodelfilterdivergence,Yikes!).Havetohavesomeintui'onaboutthevaluefor.Thisappearstobemostviableop'onforlargemodels.Valuesof=0.05to0.10workforverybroadrangeofproblems.Thisisasamplingerrorclosureproblem(akintoturbulence).
Tofix:
Setinflate_sd_ini9altofixedvalue,forinstance0.10,Setinflate_sd_lower_boundtosamevalue. (s.d.can’tgetanysmaller).
Trythisinlorenz_96.Lookathowtheinfla'onvaries.
σλ σλ
σλ σλ
σλ
σλ
DARTTutorialSec'on12:Slide24
Poten'alproblemswithobserva'onspaceadap'veinfla'on
1.Veryheuris'c.
2.Errormodelfilterdivergence(precyhardtothinkabout).3.Equilibra'onproblems,oscilla'onsinλwith'me.4.Notclearthatsingledistribu'onforallobserva'onsisright.5.Amplifyingunwantedmodelresonances(gravitywaves)
DARTTutorialSec'on12:Slide25
Poten'alproblemswithobserva'onspaceadap'veinfla'on
1.Veryheuris'c.
2.Errormodelfilterdivergence(precyhardtothinkabout).3.Equilibra'onproblems,oscilla'onsinλwith'me.4.Notclearthatsingledistribu'onforallobserva'onsisright.5.Amplifyingunwantedmodelresonances(gravitywaves)
Tryturningthisonin9varmodel.Fixed0.05forinf_sd_ini9al,sd_lower_bound.
DARTTutorialSec'on12:Slide26
Adap'veStateSpaceInfla'onAlgorithm
Supposewewantaglobalstatespaceinfla'on,λs,instead.Makesameleastsquaresassump'onthatisusedinensemblefilter.Infla'onofλsforstatevariablesinflatesobs.priorsbysameamount.Getsamelikelihoodasbefore:Computeupdateddistribu'onforλsexactlyasforobserva'onspace.
p yo |λ( )= 2πθ2( )−1 2 exp −D2 2θ2( )
θ= λsσprior2 +σobs
2
DARTTutorialSec'on12:Slide27
Implementa'onofAdap'veStateSpaceInfla'onAlgorithm
1.Applyinfla'ontostatevariableswithmeanofλsdistribu'on.2.Dofollowingforobserva'onsatgiven'mesequen'ally:
a.Computeforwardoperatortogetpriorensemble.b.Computeupdatedes'mateforλsmeanandvariance.c.Computeincrementsforpriorensemble.d.Regressincrementsontostatevariables.
DARTTutorialSec'on12:Slide28
inf_flavor =3, 0,inf_ini'al_from_restart =.false., .false.,inf_sd_ini'al_from_restart =.false., .false.,inf_determinis'c =.true., .true.,inf_ini'al =1.00, 1.0,inf_sd_ini'al =0.2, 0.0,inf_damping =1.0, 1.0,inf_lower_bound =1.0, 1.0,inf_upper_bound =1000000.0, 1000000.0,inf_sd_lower_bound =0.0, 0.0,
BeforeAssimila'on
AferAssimila'on
Flavor: 0=>NONE 2=>varyingstatespace 3=>constantstatespace
Ini'alinfla'onvalueIni'alstandarddevia'on
TrythisinLorenz96(verifyotheraspectsofinput.nml).Use40memberensemble.(setens_size=40in&filter_nml).Setredvaluesasaboveforadap'vespa'ally-constantstatespaceinfla'on.
Lowerboundons.d.
Experimen'ngwithspa'ally-constantstatespaceinfla'on
DARTTutorialSec'on12:Slide29
priorinfla'on
posteriorinfla'on
Experimen'ngwithspa'ally-constantstatespaceinfla'on
Runthefilter:Examineperformancewithplot_total_errinMatlab.
Timeseriesofinfla'onmeanandstandarddevia'onareinpreassim.ncfile:• Thiscanbeviewedwithncview(moreonthislater).• Infla'onadjustswith'me.• Infla'onstandarddevia'onisnon-increasingwith'me.• Thisfilealsohas'meseriesoftheensemble.Finalvaluesofinfla'onforrestartareinfilter_output.ncfile.
DARTTutorialSec'on12:Slide30
Adap'veInfla'onAlgorithmicVariants
1.IncreasepriorstatevariancebyaddingrandomGaussiannoise.Asopposedto‘determinis'c’linearinfla'ng.Setinf_determinis9cinfirstcolumnto.false.Changeitbackto.true. afercheckingthisout.
2.Justhaveafixedvalueforstatespaceλ.
Constantinspaceand'me.Cheap,handlesblowupofstatevarsunconstrainedbyobs.Wealreadytriedthisinsec'on9.Setinflate_sd_ini9alandinf_sd_lower_boundto0.Setinf_ini9altodesiredinfla'onvalue.
DARTTutorialSec'on12:Slide31
Adap'veInfla'onAlgorithmicVariants
3.Fixvalueofλstandarddevia'on,.Reducescost,computa'onofcansome'mesbetricky.Avoidsgepngsmall(errormodelfilterdivergence,Yikes!).Havetohavesomeintui'onaboutthevaluefor.Thisappearstobemostviableop'onforlargemodels.Valuesof=0.10to0.60workforverybroadrangeofproblems.Thisisasamplingerrorclosureproblem(akintoturbulence).
Tofix:
Setinflate_sd_ini9altofixedvalue,forinstance0.20,Setinflate_sd_lower_boundtosamevalue. (s.d.can’tgetanysmaller).
TrythisinLorenz96.Lookathowtheinfla'onvaries.
σλ σλ
σλ σλ
σλ
σλ
DARTTutorialSec'on12:Slide32
Adap'veInfla'onAlgorithmicVariants
4.Infla'ondampingInfla'onmeandampedtowards1.0everyassimila'on'me.Setbynamelistentryinf_damping.inf_damping=0.9:90%oftheinfla'ondifferencefrom1.0isretained.
Canbeusefulinmodelswithheterogeneousobserva'onsin'me.Forinstance,awell-observedhurricanecrossesamodeldomain.Adap'veinfla'onincreasesalonghurricanetrace.Aferhurricane,fewerobserva'ons,nolongerneedsomuchinfla'on.Forlargeearthsystemmodels,followingvaluesmaywork:
inf_sd_ini9al =0.6,inf_damping =0.9,inf_sd_lower_bound =0.6.
DARTTutorialSec'on12:Slide33
Simula'ngModelErrorin40-VariableLorenz96Model
Infla'oncandealwithallsortsoferrors,includingmodelerror.CansimulatemodelerrorinLorenz96bychangingforcing.Synthe'cobserva'onsarefrommodelwithforcing=8.0.Useforcinginmodel_nmltointroducemodelerror.
Tryforcingvaluesof7,6,5,3withandwithoutadap'veinfla'on.TheF=3modelisperiodic,looksveryliclelikeF=8.
DARTTutorialSec'on12:Slide34
Simula'ngModelErrorin40-VariableLorenz96Model
40statevariables:X1,X2,...,XN.dXi/dt=(Xi+1-Xi-2)Xi-1-Xi+F;i=1,...,40withcyclicindices.UseF=8.0,4th-orderRunge-Kucawithdt=0.05.
TimeseriesofstatevariablefromfreeLorenz96integra'on
Anderson: Ensemble Tutorial 26 12/4/07
Simulating Model Error in 40-Variable Lorenz-96 Model
40 state variables: X1, X2,..., XNdXi / dt = (Xi+1 - Xi-2)Xi-1 - Xi + F;i = 1,..., 40 with cyclic indicesUse F = 8.0, 4th-order Runge-Kutta with dt=0.05
0 50 100 150 200 250 300 350 400−8
−6
−4
−2
0
2
4
6
8
10
12
TIME
X(1)
Time series ofstate variablefrom free L96integration
DARTTutorialSec'on12:Slide35
Experimentaldesign:Lorenz96ModelErrorSimula'on
Truthandobserva'onscomesfromlongrunwithF=8
200randomlylocated(fixedin'me)‘observingloca'ons’
Independent1.0observa'onerrorvariance
Observa'onseveryhour
σλis0.05,meanofλadjustsbutvarianceisfixed
4groupsof20memberseach(80ensemblememberstotal)
Resultsfrom10daysafer40dayspin-up
Varyassimila'ngmodelforcing:F=8,6,3,0
Simulatesincreasingmodelerror
DARTTutorialSec'on12:Slide36
Assimila'ngF=8TruthwithF=8Ensemble
Anderson: Ensemble Tutorial 28 12/4/07
Assimilating F=8 Truth with F=8 EnsembleModel time series Mean value of λ
Assimilation Results
0 5 10−5
0
5
10
model time (pseudo−days)
F=8 0 5 101
1.005
1.01
model time (pseudo−days)
F=8
0 5 10−10
0
10
True State Ensemble Mean Ensemble Members
Anderson: Ensemble Tutorial 28 12/4/07
Assimilating F=8 Truth with F=8 EnsembleModel time series Mean value of λ
Assimilation Results
0 5 10−5
0
5
10
model time (pseudo−days)
F=8 0 5 101
1.005
1.01
model time (pseudo−days)
F=8
0 5 10−10
0
10
True State Ensemble Mean Ensemble Members
Anderson: Ensemble Tutorial 28 12/4/07
Assimilating F=8 Truth with F=8 EnsembleModel time series Mean value of λ
Assimilation Results
0 5 10−5
0
5
10
model time (pseudo−days)
F=8 0 5 101
1.005
1.01
model time (pseudo−days)
F=8
0 5 10−10
0
10
True State Ensemble Mean Ensemble Members
Assimila'onResults
model'me(pseudo−days)
model'me(pseudo−days)
Model'meseries Meanvalueofλ
DARTTutorialSec'on12:Slide37
Anderson: Ensemble Tutorial 29 12/4/07
Assimilating F=8 Truth with F=6 EnsembleModel time series Mean value of λ
Assimilation Results
0 5 10−5
0
5
10
model time (pseudo−days)
F=8 F=6 0 5 101
1.5
model time (pseudo−days)
F=6 F=8
0 5 10−10
0
10
True State Ensemble Mean Ensemble Members
Anderson: Ensemble Tutorial 29 12/4/07
Assimilating F=8 Truth with F=6 EnsembleModel time series Mean value of λ
Assimilation Results
0 5 10−5
0
5
10
model time (pseudo−days)
F=8 F=6 0 5 101
1.5
model time (pseudo−days)
F=6 F=8
0 5 10−10
0
10
True State Ensemble Mean Ensemble Members
Anderson: Ensemble Tutorial 29 12/4/07
Assimilating F=8 Truth with F=6 EnsembleModel time series Mean value of λ
Assimilation Results
0 5 10−5
0
5
10
model time (pseudo−days)
F=8 F=6 0 5 101
1.5
model time (pseudo−days)
F=6 F=8
0 5 10−10
0
10
True State Ensemble Mean Ensemble Members
Assimila'ngF=8TruthwithF=6Ensemble
Assimila'onResults
model'me(pseudo−days)
model'me(pseudo−days)
Model'meseries Meanvalueofλ
DARTTutorialSec'on12:Slide38
Anderson: Ensemble Tutorial 30 12/4/07
Assimilating F=8 Truth with F=3 EnsembleModel time series Mean value of λ
Assimilation Results
0 5 101
1.5
model time (pseudo−days)
F=3 F=6
F=8
0 5 10−5
0
5
10
model time (pseudo−days)
F=8 F=6 F=3
0 5 10−10
0
10
True State Ensemble Mean Ensemble Members
Anderson: Ensemble Tutorial 30 12/4/07
Assimilating F=8 Truth with F=3 EnsembleModel time series Mean value of λ
Assimilation Results
0 5 101
1.5
model time (pseudo−days)
F=3 F=6
F=8
0 5 10−5
0
5
10
model time (pseudo−days)
F=8 F=6 F=3
0 5 10−10
0
10
True State Ensemble Mean Ensemble Members
Anderson: Ensemble Tutorial 30 12/4/07
Assimilating F=8 Truth with F=3 EnsembleModel time series Mean value of λ
Assimilation Results
0 5 101
1.5
model time (pseudo−days)
F=3 F=6
F=8
0 5 10−5
0
5
10
model time (pseudo−days)
F=8 F=6 F=3
0 5 10−10
0
10
True State Ensemble Mean Ensemble Members
Assimila'ngF=8TruthwithF=3Ensemble
Assimila'onResults
model'me(pseudo−days)
model'me(pseudo−days)
Model'meseries Meanvalueofλ
DARTTutorialSec'on12:Slide39
Anderson: Ensemble Tutorial 31 12/4/07
Assimilating F=8 Truth with F=0 EnsembleModel time series Mean value of λ
Assimilation Results
Prior RMS Error, Spread, and λ Grow as Model Error Grows
0 5 10−5
0
5
10
model time (pseudo−days)
F=8 F=6 F=3 F=0 0 5 101
1.5
model time (pseudo−days)
F=0 F=3
F=6 F=8
0 5 10−10
0
10
True State Ensemble Mean Ensemble Members
Anderson: Ensemble Tutorial 31 12/4/07
Assimilating F=8 Truth with F=0 EnsembleModel time series Mean value of λ
Assimilation Results
Prior RMS Error, Spread, and λ Grow as Model Error Grows
0 5 10−5
0
5
10
model time (pseudo−days)
F=8 F=6 F=3 F=0 0 5 101
1.5
model time (pseudo−days)
F=0 F=3
F=6 F=8
0 5 10−10
0
10
True State Ensemble Mean Ensemble Members
Anderson: Ensemble Tutorial 31 12/4/07
Assimilating F=8 Truth with F=0 EnsembleModel time series Mean value of λ
Assimilation Results
Prior RMS Error, Spread, and λ Grow as Model Error Grows
0 5 10−5
0
5
10
model time (pseudo−days)
F=8 F=6 F=3 F=0 0 5 101
1.5
model time (pseudo−days)
F=0 F=3
F=6 F=8
0 5 10−10
0
10
True State Ensemble Mean Ensemble Members
Assimila'ngF=8TruthwithF=0Ensemble
Assimila'onResults
model'me(pseudo−days)
model'me(pseudo−days)
Model'meseries Meanvalueofλ
DARTTutorialSec'on12:Slide40
Anderson: Ensemble Tutorial 31 12/4/07
Assimilating F=8 Truth with F=0 EnsembleModel time series Mean value of λ
Assimilation Results
Prior RMS Error, Spread, and λ Grow as Model Error Grows
0 5 10−5
0
5
10
model time (pseudo−days)
F=8 F=6 F=3 F=0 0 5 101
1.5
model time (pseudo−days)
F=0 F=3
F=6 F=8
0 5 10−10
0
10
True State Ensemble Mean Ensemble Members
Anderson: Ensemble Tutorial 31 12/4/07
Assimilating F=8 Truth with F=0 EnsembleModel time series Mean value of λ
Assimilation Results
Prior RMS Error, Spread, and λ Grow as Model Error Grows
0 5 10−5
0
5
10
model time (pseudo−days)
F=8 F=6 F=3 F=0 0 5 101
1.5
model time (pseudo−days)
F=0 F=3
F=6 F=8
0 5 10−10
0
10
True State Ensemble Mean Ensemble Members
Anderson: Ensemble Tutorial 31 12/4/07
Assimilating F=8 Truth with F=0 EnsembleModel time series Mean value of λ
Assimilation Results
Prior RMS Error, Spread, and λ Grow as Model Error Grows
0 5 10−5
0
5
10
model time (pseudo−days)
F=8 F=6 F=3 F=0 0 5 101
1.5
model time (pseudo−days)
F=0 F=3
F=6 F=8
0 5 10−10
0
10
True State Ensemble Mean Ensemble Members
Assimila'ngF=8TruthwithF=0Ensemble
Assimila'onResults
model'me(pseudo−days)
model'me(pseudo−days)
Model'meseries Meanvalueofλ
PriorRMSError,Spread,andλGrowasModelErrorGrows
DARTTutorialSec'on12:Slide41
Anderson: Ensemble Tutorial 32 12/4/07
Base case: 200 randomly located observations per time
Assimilating Model Forcing, F Assim. Forcing, F(Error saturation is approximately 30.0)
Prior RMS Error, Spread, and λ Grow as Model Error Grows
0 101
1.2
1.4
1.6
1.8
0 5 10 150
2
4
6RMS ErrorSpread
Basecase:200randomlylocatedobserva'onsper'me
Assimila'ngModelForcing,F Assim.Forcing,F
(Errorsatura'onisapproximately30.0)PriorRMSError,Spread,andλGrowasModelErrorGrows
Infla'on
DARTTutorialSec'on12:Slide42
Anderson: Ensemble Tutorial 33 12/4/07
Less well observed case, 40 randomly located observations per time
Assimilating Model Forcing, F Assim. Forcing, F0 5 10 151
1.5
2
2.5
3
3.540 Obs.200 Obs.
0 5 10 150
5
10
15
20 RMS ErrorSpreadRMS 40 ObsSpread 40 Obs
Lesswellobservedcase,40randomlylocatedobsper'me
Assimila'ngModelForcing,F Assim.Forcing,F
DARTTutorialSec'on12:Slide43
Spa'allyvaryingadap'veinfla'onalgorithm
Haveadistribu'onforλforeachstatevariable,λs,i.Usepriorcorrela'onfromensembletodetermineimpactofλs,ionpriorvarianceforgivenobserva'on.Ifγiscorrela'onbetweenstatevariableiandobserva'onthenEqua'onforfindingmodeofposteriorisnowfull12thorder:
Analy'csolu'onappearsunlikely.CandoTaylorexpansionofθaroundλs,i.Retaininglineartermisnormallyquiteaccurate.Thereisananaly'csolu'ontofindmodeofproductinthiscase!
θ= 1+γ λs,i −1( )⎡
⎣⎢⎤⎦⎥2σprior2 +σobs
2
DARTTutorialSec'on12:Slide44
inf_flavor =2, 0,inf_ini'al_from_restart =.false., .false.,inf_sd_ini'al_from_restart =.false., .false.,inf_determinis'c =.true., .true.,inf_ini'al =1.00, 1.0,inf_sd_ini'al =0.2, 0.0,inf_damping =1.0, 1.0,inf_lower_bound =1.0, 1.0,inf_upper_bound =1000000.0, 1000000.0,inf_sd_lower_bound =0.0, 0.0,
BeforeAssimila'on
AferAssimila'on
Flavor: 2=>varyingstatespace 3=>constantstatespace 0=>NONE
Ini'alinfla'onvalueIni'alstandarddevia'on
TrythisinLorenz96(verifyotheraspectsofinput.nml).Use40memberensemble.(setens_size=40infilter_nml).Setredvaluesasaboveforadap'vespa'ally-varyingstatespaceinfla'on.
Lowerboundons.d.
Experimen'ngwithspa'ally-varyingstatespaceinfla'on
DARTTutorialSec'on12:Slide45
models/lorenz_96/work/
Experimen'ngwithspa'ally-varyingstatespaceinfla'on
Cantrythiswiththeotheralgorithmicvariants.Spa'ally-varyingadap'veinfla'onisthemostcommonchoiceinDARTforlargeearthsystemmodels.
DARTTutorialSec'on12:Slide46
PosteriorInfla'on
Sofar,we’vealwaysusedthefirstcolumnoftheinfla'onnamelist.Infla'onisperformedafermodeladvancesbutbeforeassimila'on.Canalsodoposteriorinfla'onusingsecondcolumn.Thisdoesinfla'onaferassimila'onbutbeforemodeladvance.Helpstoincreasevarianceinforecasts.Canalsodobothpriorandposteriorinfla'on(usebothcolumns).Diagnos'csareinsamefileswith‘post’insteadof‘prior’.
DARTTutorialSec'on12:Slide47
Combinedmodelandobserva'onalerrorvarianceadap'vealgorithm
Isthisreallypossible.Yes,incertainsitua'ons...Isthereenoughinforma'onavailable?Spa'ally-varyinfla'onforstate.Infla'onfactorfordifferentsetsofobserva'ons(allradiosondeT’s).Differentλ’sseedifferentobserva'ons.TestsinL96withmodelerrorANDincorrectobs.errorvariancecancorrectforboth.
θ= 1+γ λs,i −1( )⎡
⎣⎢⎤⎦⎥2σprior2 +λoσobs
2
DARTTutorialSec'on12:Slide48
1. FilteringForaOneVariableSystem2. TheDARTDirectoryTree3. DARTRunGmeControlandDocumentaGon4. HowshouldobservaGonsofastatevariableimpactanunobservedstatevariable?
MulGvariateassimilaGon.5. ComprehensiveFilteringTheory:Non-IdenGtyObservaGonsandtheJointPhaseSpace6. OtherUpdatesforAnObservedVariable7. SomeAddiGonalLow-OrderModels8. DealingwithSamplingError9. MoreonDealingwithError;InflaGon10. RegressionandNonlinearEffects11. CreaGngDARTExecutables12. AdapGveInflaGon13. HierarchicalGroupFiltersandLocalizaGon14. QualityControl15. DARTExperiments:ControlandDesign16. DiagnosGcOutput17. CreaGngObservaGonSequences18. LostinPhaseSpace:TheChallengeofNotKnowingtheTruth19. DART-CompliantModelsandMakingModelsCompliant20. ModelParameterEsGmaGon21. ObservaGonTypesandObservingSystemDesign22. ParallelAlgorithmImplementaGon23. Loca'onmoduledesign(notavailable)24. Fixedlagsmoother(notavailable)25. Asimple1DadvecGonmodel:TracerDataAssimilaGon
DARTTutorialIndextoSec'ons
DARTTutorialSec'on12:Slide49