dart tutorial sec’on 7: some addi’onal low-order models · dart tutorial sec’on 7: slide 9...

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The Na’onal Center for Atmospheric Research is sponsored by the Na’onal Science Founda’on. Any opinions, findings and conclusions or recommenda’ons expressed in this publica’on are those of the author(s) and do not necessarily reflect the views of the Na’onal Science Founda’on. ©UCAR DART Tutorial Sec’on 7: Some Addi’onal Low-Order Models

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Page 1: DART Tutorial Sec’on 7: Some Addi’onal Low-Order Models · DART Tutorial Sec’on 7: Slide 9 preassim.nc preassim.nc preassim.nc true_state.nc models/lorenz_96/work/ Lorenz 96

TheNa'onalCenterforAtmosphericResearchissponsoredbytheNa'onalScienceFounda'on.Anyopinions,findingsandconclusionsorrecommenda'onsexpressedinthispublica'onarethoseoftheauthor(s)anddonotnecessarilyreflecttheviewsoftheNa'onalScienceFounda'on.

©UCAR

DARTTutorialSec'on7:SomeAddi'onalLow-OrderModels

Page 2: DART Tutorial Sec’on 7: Some Addi’onal Low-Order Models · DART Tutorial Sec’on 7: Slide 9 preassim.nc preassim.nc preassim.nc true_state.nc models/lorenz_96/work/ Lorenz 96

LowOrderModelsinDART

Model Size Features

lorenz_63 3 Chao'c,nearlyintegralaSractor,bifurca'ons

lorenz_84 3 MorecomplexaSractor,notasperiodic

9var 9 Transientoff-aSractordynamics

lorenz_96 40(variable) Higherdimensionalsystem.ASractordimension13with40variablesandstandardforcing.

forced_lorenz_96 80(variable) Allowsassimila'onofmodelparameter(seeSec'on20)

lorenz_96_2scale 396(variable) Twoprimaryinterac'ngspa'al/temporalscales

lorenz_04 variable Mul'scaledynamics

DARTTutorialSec'on7:Slide2

Page 3: DART Tutorial Sec’on 7: Some Addi’onal Low-Order Models · DART Tutorial Sec’on 7: Slide 9 preassim.nc preassim.nc preassim.nc true_state.nc models/lorenz_96/work/ Lorenz 96

Lorenz84Model

ASractornotsheet-like.Raresignificantdevia'ons.Trajectoriesalongdevia'onsdon’t‘mesh’backupwiththerestoftheaSractor.Thisbehaviorcanbechallengingforcertainfiltervariants.

−20

24

−20

2

−1

0

1

2

x DARTTutorialSec'on7:Slide3

models/lorenz_84/work/

Page 4: DART Tutorial Sec’on 7: Some Addi’onal Low-Order Models · DART Tutorial Sec’on 7: Slide 9 preassim.nc preassim.nc preassim.nc true_state.nc models/lorenz_96/work/ Lorenz 96

Lorenz84Model

3-variables:Parameters

a=0.25,b=4,f=8,g=1.25

cansetfrommodel_nml

−20

24

−20

2

−1

0

1

2

x

dx1dt

= −x22 − x3

2 − ax1 + af

dx2dt

= x1x2 − bx1x3 − x2 + g

dx3dt

= bx1x2 + x1x3 − x3

DARTTutorialSec'on7:Slide4

models/lorenz_84/work/

Page 5: DART Tutorial Sec’on 7: Some Addi’onal Low-Order Models · DART Tutorial Sec’on 7: Slide 9 preassim.nc preassim.nc preassim.nc true_state.nc models/lorenz_96/work/ Lorenz 96

Lorenz84Model

Exercise:cd models/lorenz_84/workcsh workshop_setup.cshEachstatevariableisobservedeveryonceeveryhour.Observa'onalerrorvarianceis1.UseMatlabtoexaminetheoutput.There’sanewtypeoffilterchallengerepresentedhere.Canyouiden'fyit?Canyouproposewaystoaddressitwithtechniqueslearnedtodate?

DARTTutorialSec'on7:Slide5

Page 6: DART Tutorial Sec’on 7: Some Addi’onal Low-Order Models · DART Tutorial Sec’on 7: Slide 9 preassim.nc preassim.nc preassim.nc true_state.nc models/lorenz_96/work/ Lorenz 96

−0.4−0.200.20.4

−0.5

0

0.5

−0.2

−0.1

0

0.1

0.2

9VariableModel

ThreegroupsofvariablesVariables1-3:DivergenceVariables4-6:Vor'city.Variables7-9:Height.Ingeneral,divergenceissmall.Heightandpressuresimilar.HeightandpressurehaveaSractorsimilartoLorenz63.

DARTTutorialSec'on7:Slide6

models/9var/work/

Page 7: DART Tutorial Sec’on 7: Some Addi’onal Low-Order Models · DART Tutorial Sec’on 7: Slide 9 preassim.nc preassim.nc preassim.nc true_state.nc models/lorenz_96/work/ Lorenz 96

9VariableModel

!Xi =UjUk +VjVk−v0aiXi +Yi +aizi

!Yi =UjYk +YjVk−Xi−v0aiYi

!zi =Uj zk−hk( )+ z j−hj( )Vk−g0Xi−K0aizi +Fi

Ui =−bjxi + cyi

Vi =−bkxi−cyi

Xi =−aixi

Yi =−aiyi

Xisdivergence,Yisvor'city,ZisheightAllparameterscanbeadjustedfrommodel_mod.nml

i=1,2,3 j =mod(i, 3)+1 k =mod(i+1,3)+1

DARTTutorialSec'on7:Slide7

Page 8: DART Tutorial Sec’on 7: Some Addi’onal Low-Order Models · DART Tutorial Sec’on 7: Slide 9 preassim.nc preassim.nc preassim.nc true_state.nc models/lorenz_96/work/ Lorenz 96

9VariableModel

WhenperturbedofftheaSractor,mimics‘gravitywaves’.Transient,highfrequencyoscilla'onsdominatedivergencevariables.Canalsoappearinheightandpressurevariables.cd models/9var/workcsh workshop_setup.cshY1,Y2,Y3(the‘vor'city’variables)areobservedonceevery6hoursObserva'onalerrorvarianceis0.4.UseMatlabtoexaminetheoutput.Howdodifferentfilterkindsinteractwith‘gravity’waves?

DARTTutorialSec'on7:Slide8

Page 9: DART Tutorial Sec’on 7: Some Addi’onal Low-Order Models · DART Tutorial Sec’on 7: Slide 9 preassim.nc preassim.nc preassim.nc true_state.nc models/lorenz_96/work/ Lorenz 96

Lorenz96(40-variable)Model

0 1 2 3 4 5 6 7 8 9 10−5

0

5

10

15

model time (240 timesteps)

Lorenz_96 state varnum 1 Ensemble Members of ./Prior_Diag.nc

True StateEnsemble MeanEnsemble Members (20)

0 1 2 3 4 5 6 7 8 9 10−10

0

10

20

model time (240 timesteps)

Lorenz_96 state varnum 2 Ensemble Members of ./Prior_Diag.nc

True StateEnsemble MeanEnsemble Members (20)

0 1 2 3 4 5 6 7 8 9 10−10

0

10

20

model time (240 timesteps)

Lorenz_96 state varnum 3 Ensemble Members of ./Prior_Diag.nc

True StateEnsemble MeanEnsemble Members (20)

−10

0

10

20−10−5051015−8

−6

−4

−2

0

2

4

6

8

10

12

state variable # 1

The Attractor

state variable # 2

stat

e va

riabl

e #

3

1 2 3 True_State.nc Lorenz_96 true state

DARTTutorialSec'on7:Slide9

preassim.nc

preassim.nc

preassim.nc

true_state.nc

models/lorenz_96/work/

Page 10: DART Tutorial Sec’on 7: Some Addi’onal Low-Order Models · DART Tutorial Sec’on 7: Slide 9 preassim.nc preassim.nc preassim.nc true_state.nc models/lorenz_96/work/ Lorenz 96

Lorenz96(40-variable)ModelASractordimension13bysomemeasureswith40variablesandstandardforcing(forcing=8.00in&model_nml).Starttoexploremodelsizesclosertoensemblesize.Canexaminepossibledegeneracyissueswithsamplecovariance.Naiveapplica'onofsmallensemblesdivergesinmanycases.

DARTTutorialSec'on7:Slide10

Page 11: DART Tutorial Sec’on 7: Some Addi’onal Low-Order Models · DART Tutorial Sec’on 7: Slide 9 preassim.nc preassim.nc preassim.nc true_state.nc models/lorenz_96/work/ Lorenz 96

Lorenz96(40-variable)Model

cd models/lorenz_96/workcsh workshop_setup.csh40observa'ons,randomlylocatedinspace,equallyspacedin'me.Observedonceanhour;observa'onalerrorvarianceis1.0.UseMatlabtoexaminetheoutput.Neednewtechniquestofixproblemseenhere.Forplot_ens_.me_series,plot_ens_mean_.me_series:

Canselectsubsetofvariablestoplot,Defaultselec'onofvariables1,13,and27areapproximately

equallyspacedaroundthecyclicdomain.

DARTTutorialSec'on7:Slide11

Page 12: DART Tutorial Sec’on 7: Some Addi’onal Low-Order Models · DART Tutorial Sec’on 7: Slide 9 preassim.nc preassim.nc preassim.nc true_state.nc models/lorenz_96/work/ Lorenz 96

1.   FilteringForaOneVariableSystem2.   TheDARTDirectoryTree3.   DARTRunAmeControlandDocumentaAon4.   HowshouldobservaAonsofastatevariableimpactanunobservedstatevariable?

MulAvariateassimilaAon.5.   ComprehensiveFilteringTheory:Non-IdenAtyObservaAonsandtheJointPhaseSpace6.   OtherUpdatesforAnObservedVariable7.   SomeAddiAonalLow-OrderModels8.   DealingwithSamplingError9.   MoreonDealingwithError;InflaAon10.   RegressionandNonlinearEffects11.   CreaAngDARTExecutables12.   AdapAveInflaAon13.   HierarchicalGroupFiltersandLocalizaAon14.   QualityControl15.   DARTExperiments:ControlandDesign16.   DiagnosAcOutput17.   CreaAngObservaAonSequences18.   LostinPhaseSpace:TheChallengeofNotKnowingtheTruth19.   DART-CompliantModelsandMakingModelsCompliant20.   ModelParameterEsAmaAon21.   ObservaAonTypesandObservingSystemDesign22.   ParallelAlgorithmImplementaAon23.  Loca'onmoduledesign(notavailable)24.  Fixedlagsmoother(notavailable)25.   Asimple1DadvecAonmodel:TracerDataAssimilaAon

DARTTutorialIndextoSec'ons

DARTTutorialSec'on7:Slide12