DART Tutorial Sec’on 1: Filtering For a One Variable System · DART Tutorial Sec’on 1: Slide 2...
Transcript of DART Tutorial Sec’on 1: Filtering For a One Variable System · DART Tutorial Sec’on 1: Slide 2...
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TheNa'onalCenterforAtmosphericResearchissponsoredbytheNa'onalScienceFounda'on.Anyopinions,findingsandconclusionsorrecommenda'onsexpressedinthispublica'onarethoseoftheauthor(s)anddonotnecessarilyreflecttheviewsoftheNa'onalScienceFounda'on.
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DARTTutorialSec'on1:FilteringForaOneVariableSystem
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DARTTutorialSec'on1:Slide2
Introduc'on
Thisseriesoftutorialpresenta'onsisdesignedtointroducebothbasicEnsembleKalmanfiltertheoryandtheDataAssimila'onResearchTestbedCommunityFacilityforEnsembleDataAssimila'on.ThereissignificantoverlapwiththeDART_LABtutorialthatisalsopartoftheDARTsubversioncheckout.IfyouhavealreadystudiedDART_LAB,feelfreetoskipthroughtheredundanttheoryslides.However,doingtheexercisesinallsec'onsofthistutorialisrecommendedinordertolearnthebestwaystousetheDARTsystem.
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−6 −4 −2 0 2 4 60
0.05
0.1
0.15
0.2 Prior PDF
Prob
abilit
y
A :PriorEs'matebasedonallpreviousinforma'on,C.B :Anaddi'onalobserva'on.p(A|BC) :Posterior(updatedes'mate)basedonCandB.
p A | BC( ) = p(B | AC)p(A |C)p(B |C)
= p(B | AC)p(A |C)p(B | x)p(x |C)dx∫
Bayes’Rule
DARTTutorialSec'on1:Slide3DARTTutorialSec'on1:Slide3
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A :PriorEs'matebasedonallpreviousinforma'on,C.B :Anaddi'onalobserva'on.p(A|BC) :Posterior(updatedes'mate)basedonCandB.
−6 −4 −2 0 2 4 60
0.05
0.1
0.15
0.2 Prior PDF
Prob
abilit
y Obs. Likelihood
p A | BC( ) = p(B | AC)p(A |C)p(B |C)
= p(B | AC)p(A |C)p(B | x)p(x |C)dx∫
Bayes’Rule
DARTTutorialSec'on1:Slide4
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A :PriorEs'matebasedonallpreviousinforma'on,C.B :Anaddi'onalobserva'on.p(A|BC) :Posterior(updatedes'mate)basedonCandB.
−6 −4 −2 0 2 4 60
0.05
0.1
0.15
0.2 Prior PDF
Prob
abilit
y Obs. Likelihood
Product (Numerator)
p A | BC( ) = p(B | AC)p(A |C)p(B |C)
= p(B | AC)p(A |C)p(B | x)p(x |C)dx∫
Bayes’Rule
DARTTutorialSec'on1:Slide5
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A :PriorEs'matebasedonallpreviousinforma'on,C.B :Anaddi'onalobserva'on.p(A|BC) :Posterior(updatedes'mate)basedonCandB.
−6 −4 −2 0 2 4 60
0.05
0.1
0.15
0.2 Prior PDF
Prob
abilit
y Obs. Likelihood
Normalization (Denom.)
p A | BC( ) = p(B | AC)p(A |C)p(B |C)
= p(B | AC)p(A |C)p(B | x)p(x |C)dx∫
Bayes’Rule
DARTTutorialSec'on1:Slide6
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−6 −4 −2 0 2 4 60
0.05
0.1
0.15
0.2 Prior PDF
Prob
abilit
y Obs. Likelihood
Normalization (Denom.)
Posterior
p A | BC( ) = p(B | AC)p(A |C)p(B |C)
= p(B | AC)p(A |C)p(B | x)p(x |C)dx∫
A :PriorEs'matebasedonallpreviousinforma'on,C.B :Anaddi'onalobserva'on.p(A|BC) :Posterior(updatedes'mate)basedonCandB.
Bayes’Rule
DARTTutorialSec'on1:Slide7
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Green==Prior
Red==Observa'on
Blue==Posterior
ThesamecolorschemeisusedthroughoutALLTutorialmaterials.
ColorScheme
DARTTutorialSec'on1:Slide8
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ThisproductisclosedforGaussiandistribu'ons.
−4 −2 0 2 40
0.2
0.4
0.6
Prob
abilit
y
Prior PDF
Obs. Likelihood
p A | BC( ) = p(B | AC)p(A |C)p(B |C)
= p(B | AC)p(A |C)p(B | x)p(x |C)dx∫
ProductofTwoGaussians
DARTTutorialSec'on1:Slide9
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ThisproductisclosedforGaussiandistribu'ons.
−4 −2 0 2 40
0.2
0.4
0.6
Prob
abilit
y
Prior PDF
Obs. Likelihood
Posterior PDF
p A | BC( ) = p(B | AC)p(A |C)p(B |C)
= p(B | AC)p(A |C)p(B | x)p(x |C)dx∫
ProductofTwoGaussians
DARTTutorialSec'on1:Slide10
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N (µ1,∑1)N (µ2 ,∑2 ) = cN (µ,∑)
Productofd-dimensionalnormalswithmeansandandcovariancematricesand
isnormal.
µ1 µ2∑1 ∑2
ProductofTwoGaussians
DARTTutorialSec'on1:Slide11
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Productofd-dimensionalnormalswithmeansandandcovariancematricesand
isnormal.
Covariance:
Mean:
N (µ1,∑1)N (µ2 ,∑2 ) = cN (µ,∑)
∑ = (∑1−1+∑2
−1)−1
µ = (∑1−1+∑2
−1)−1(∑1−1µ1 +∑2
−1µ2 )
µ1 µ2∑1 ∑2
ProductofTwoGaussians
DARTTutorialSec'on1:Slide12
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Productofd-dimensionalnormalswithmeansandandcovariancematricesand
isnormal.
Covariance:
Mean:
Weight:
N (µ1,∑1)N (µ2 ,∑2 ) = cN (µ,∑)
∑ = (∑1−1+∑2
−1)−1
µ = (∑1−1+∑2
−1)−1(∑1−1µ1 +∑2
−1µ2 )
µ1 µ2∑1 ∑2
We’llignoretheweightunlessnotedsinceweimmediatelynormalizeproductstobePDFs.
ProductofTwoGaussians
c = 1(2∏)d /2 ∑1 +∑2
1/2 exp − 12
µ2 − µ1( )T (∑1 +∑2 )−1 µ2 − µ1( )⎡
⎣⎤⎦
⎧⎨⎩
⎫⎬⎭
DARTTutorialSec'on1:Slide13
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Productofd-dimensionalnormalswithmeansandandcovariancematricesand
isnormal.
Covariance:
Mean:
Weight:
N (µ1,∑1)N (µ2 ,∑2 ) = cN (µ,∑)
∑ = (∑1−1+∑2
−1)−1
µ = (∑1−1+∑2
−1)−1(∑1−1µ1 +∑2
−1µ2 )
µ1 µ2∑1 ∑2
Easytoderivefor1-DGaussians;justdoproductsofexponen'als.
ProductofTwoGaussians
c = 1(2∏)d /2 ∑1 +∑2
1/2 exp − 12
µ2 − µ1( )T (∑1 +∑2 )−1 µ2 − µ1( )⎡
⎣⎤⎦
⎧⎨⎩
⎫⎬⎭
DARTTutorialSec'on1:Slide14
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ThisproductisclosedforGaussiandistribu'ons.
−4 −2 0 2 40
0.1
0.2
0.3
Prob
abilit
y Prior PDF
Thereareotherfamiliesoffunc'onsforwhichitisclosed…But,forgeneraldistribu'ons,there’snoanaly'calproduct.
ProductofTwoGaussians
p A | BC( ) = p(B | AC)p(A |C)p(B |C)
= p(B | AC)p(A |C)p(B | x)p(x |C)dx∫
DARTTutorialSec'on1:Slide15
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−4 −2 0 2 40
0.1
0.2
0.3
Prob
abilit
y Prior PDF
Obs. Likelihood
ProductofTwoGaussians
p A | BC( ) = p(B | AC)p(A |C)p(B |C)
= p(B | AC)p(A |C)p(B | x)p(x |C)dx∫
ThisproductisclosedforGaussiandistribu'ons.
Thereareotherfamiliesoffunc'onsforwhichitisclosed…But,forgeneraldistribu'ons,there’snoanaly'calproduct.
DARTTutorialSec'on1:Slide16
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−4 −2 0 2 40
0.1
0.2
0.3
Prob
abilit
y Prior PDF
Obs. Likelihood
Posterior PDF
ProductofTwoGaussians
p A | BC( ) = p(B | AC)p(A |C)p(B |C)
= p(B | AC)p(A |C)p(B | x)p(x |C)dx∫
ThisproductisclosedforGaussiandistribu'ons.
Thereareotherfamiliesoffunc'onsforwhichitisclosed…But,forgeneraldistribu'ons,there’snoanaly'calproduct.
DARTTutorialSec'on1:Slide17
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Don’tknowmuchaboutproper'esofthissample.Maynaivelyassumeitisrandomdrawfrom‘truth’.
Ensemblefilters:Priorisavailableasfinitesample.
−4 −2 0 2 40
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Prob
abilit
y
Prior Ensemble
ProductofTwoGaussians
p A | BC( ) = p(B | AC)p(A |C)p(B |C)
= p(B | AC)p(A |C)p(B | x)p(x |C)dx∫
DARTTutorialSec'on1:Slide18
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Howcanwetakeproductofsamplewithcon'nuouslikelihood?
Fitacon'nuous(Gaussianfornow)distribu'ontosample.−4 −2 0 2 40
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Prob
abilit
y
Prior PDF
Prior Ensemble
ProductofTwoGaussians
p A | BC( ) = p(B | AC)p(A |C)p(B |C)
= p(B | AC)p(A |C)p(B | x)p(x |C)dx∫
DARTTutorialSec'on1:Slide19
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Observa'onlikelihoodusuallycon'nuous(nearlyalwaysGaussian).
IfObs.Likelihoodisn’tGaussian,cangeneralizemethodsbelow.Forinstance,canfitsetofGaussiankernelstoobs.likelihood.
−4 −2 0 2 40
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Prob
abilit
y
Prior PDF
Obs. Likelihood
Prior Ensemble
ProductofTwoGaussians
p A | BC( ) = p(B | AC)p(A |C)p(B |C)
= p(B | AC)p(A |C)p(B | x)p(x |C)dx∫
DARTTutorialSec'on1:Slide20
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ProductofpriorGaussianfitandObs.likelihoodisGaussian.
Compu'ngcon'nuousposteriorissimple.BUT,needtohaveaSAMPLEofthisPDF.
−4 −2 0 2 40
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Prob
abilit
y
Prior PDF
Obs. Likelihood
Posterior PDF
Prior Ensemble
ProductofTwoGaussians
p A | BC( ) = p(B | AC)p(A |C)p(B |C)
= p(B | AC)p(A |C)p(B | x)p(x |C)dx∫
DARTTutorialSec'on1:Slide21
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Therearemanywaystodothis.
Exactproper'esofdifferentmethodsmaybeunclear.Trialanderrors'llbestwaytoseehowtheyperform.Willinteractwithproper'esofpredic'onmodels,etc.
−2 −1 0 1 2 30
0.2
0.4
0.6Posterior PDF
Prob
abilit
y
SamplingPosteriorPDF
DARTTutorialSec'on1:Slide22
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EnsembleAdjustment(Kalman)Filter
−4 −2 0 2 40
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Prior Ensemble
Prob
abilit
y
SamplingPosteriorPDF
DARTTutorialSec'on1:Slide23
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EnsembleAdjustment(Kalman)Filter
Again,fitaGaussiantosample.
−4 −2 0 2 40
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0.4
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Prior Ensemble
Prob
abilit
y
Prior PDF
SamplingPosteriorPDF
DARTTutorialSec'on1:Slide24
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EnsembleAdjustment(Kalman)Filter
ComputeposteriorPDF(sameaspreviousalgorithms).
−4 −2 0 2 40
0.2
0.4
0.6
Prior Ensemble
Prob
abilit
y
Prior PDFObs. Likelihood
Posterior PDF
SamplingPosteriorPDF
DARTTutorialSec'on1:Slide25
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EnsembleAdjustment(Kalman)Filter
Usedeterminis'calgorithmto‘adjust’ensemble.1. ‘Shil’ensembletohaveexactmeanofposterior.2. Uselinearcontrac'ontohaveexactvarianceofposterior.
−4 −2 0 2 40
0.2
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0.6
Prob
abilit
y
Posterior PDF
SamplingPosteriorPDF
DARTTutorialSec'on1:Slide26
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EnsembleAdjustment(Kalman)Filter
−4 −2 0 2 40
0.2
0.4
0.6
Prob
abilit
y
Posterior PDF
Mean Shifted
Usedeterminis'calgorithmto‘adjust’ensemble.1. ‘Shil’ensembletohaveexactmeanofposterior.2. Uselinearcontrac'ontohaveexactvarianceofposterior.
SamplingPosteriorPDF
DARTTutorialSec'on1:Slide27
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EnsembleAdjustment(Kalman)Filter
−4 −2 0 2 40
0.2
0.4
0.6
Prob
abilit
y
Posterior PDF
Mean ShiftedVariance Adjusted
Usedeterminis'calgorithmto‘adjust’ensemble.1. ‘Shil’ensembletohaveexactmeanofposterior.2. Uselinearcontrac'ontohaveexactvarianceofposterior.
SamplingPosteriorPDF
DARTTutorialSec'on1:Slide28
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p isprior,u isupdate(posterior),
isstandarddevia'on,overbarisensemblemean.
EnsembleAdjustment(Kalman)Filter
−4 −2 0 2 40
0.2
0.4
0.6
Prob
abilit
y
Posterior PDF
Mean ShiftedVariance Adjusted
xiu = xi
p − x p( ) i σ u /σ p( ) + x uσi=1,...,ensemblesize.
SamplingPosteriorPDF
DARTTutorialSec'on1:Slide29
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EnsembleAdjustment(Kalman)Filter
−4 −2 0 2 40
0.2
0.4
0.6
Prob
abilit
y
Posterior PDF
Mean ShiftedVariance Adjusted
Bimodalitymaintained,butnotappropriatelyposi'onedorweighted.Noproblemwithrandomoutliers.
SamplingPosteriorPDF
DARTTutorialSec'on1:Slide30
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EnsembleAdjustment(Kalman)Filter
−4 −2 0 2 40
0.2
0.4
0.6
Prob
abilit
y
Posterior PDF
Mean ShiftedVariance Adjusted
Thereareavarietyofotherwaystodeterminis'callyadjustensemble.Classofalgorithmssome'mescalleddeterminis'csquarerootfilters.
SamplingPosteriorPDF
DARTTutorialSec'on1:Slide31
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1stlookatDARTDiagnos'cs
cd models/lorenz_63/work inyourDARTsandbox.csh workshop_setup.csh Doesstuffyou’lllearntodolater.matlab -nodesktop
OutputfromaDARTassimila'onin3-variablemodel.20memberensemble.Observa'onsofeachvariableonceevery‘6hours’;errorvariance8.Observa'onONLYimpactsitsownstatevariable.Forassimila'on,lookslike3independentsinglevariableproblems.Modeladvancebetweenassimila'onsisn’tindependent.
Ini'alensemblemembersarerandomselec'onfromlongmodelrun.Ini'alerrorshouldbeanupperbound(randomguess).
DARTTutorialSec'on1:Slide32
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1stlookatDARTDiagnos'cs
TrythefollowingMatlabcommands.Eachwillaskyouto:Inputnameofensembletrajectoryfile:<cr>forpreassim.nc
JustselectthedefaultfilebyhipngcarriagereturnforallMatlabexercisesfornow.
'meseriesofdistancebetweenpriorensemblemeanandtruthinblue;spread,averagepriordistancebetweenensemblemembersandmean,inred.(Recordtotalvaluesoftotalerrorandspreadforlater.)'meseriesoftruthinblue;ensemblemeanpriorinred.Figure1isseparatepanelforeachstatevariable.Figure2isthree-dimensionalplot.alsoincludespriorensemblemembersingreenforFigure1.
plot_total_err
plot_ens_mean_-me_series
plot_ens_-me_series
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SimpleExample:Lorenz633-variablechao'cmodel
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Observa'oninred.Priorensembleingreen.Observingallthreestatevariables.Obs.Errorvariance=4.0.Four20-memberensembles.
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SimpleExample:Lorenz633-variablechao'cmodel
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Observa'oninred.Priorensembleingreen.
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SimpleExample:Lorenz633-variablechao'cmodel
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Observa'oninred.Priorensembleingreen.
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SimpleExample:Lorenz633-variablechao'cmodel
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Observa'oninred.Priorensembleingreen.
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SimpleExample:Lorenz633-variablechao'cmodel
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Observa'oninred.Priorensembleingreen.Ensembleispassingthroughanunpredictableregion.
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SimpleExample:Lorenz633-variablechao'cmodel
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Observa'oninred.Priorensembleingreen.Partoftheensembleheadsforonelobe,therestfortheother..
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SimpleExample:Lorenz633-variablemodel
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Observa'oninred.Priorensembleingreen.
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UsingDARTDiagnos'cs
UsingDARTdiagnos'csfromthesimpleLorenz63assimila'on:Canyouseeevidenceofenhanceduncertainty?Wheredoesthisoccur?Doestheensembleappeartobeconsistentwiththetruth?
(Isthetruthnormallyinsidetheensemblerange?)
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1. FilteringForaOneVariableSystem2. TheDARTDirectoryTree3. DARTRun>meControlandDocumenta>on4. Howshouldobserva>onsofastatevariableimpactanunobservedstatevariable?
Mul>variateassimila>on.5. ComprehensiveFilteringTheory:Non-Iden>tyObserva>onsandtheJointPhaseSpace6. OtherUpdatesforAnObservedVariable7. SomeAddi>onalLow-OrderModels8. DealingwithSamplingError9. MoreonDealingwithError;Infla>on10. RegressionandNonlinearEffects11. Crea>ngDARTExecutables12. Adap>veInfla>on13. HierarchicalGroupFiltersandLocaliza>on14. QualityControl15. DARTExperiments:ControlandDesign16. Diagnos>cOutput17. Crea>ngObserva>onSequences18. LostinPhaseSpace:TheChallengeofNotKnowingtheTruth19. DART-CompliantModelsandMakingModelsCompliant20. ModelParameterEs>ma>on21. Observa>onTypesandObservingSystemDesign22. ParallelAlgorithmImplementa>on23. Loca'onmoduledesign(notavailable)24. Fixedlagsmoother(notavailable)25. Asimple1Dadvec>onmodel:TracerDataAssimila>on
DARTTutorialIndextoSec'ons
DARTTutorialSec'on1:Slide42