DART Tutorial Sec’on 20: Model Parameter Es’maon

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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 20: Model Parameter Es’ma’on

Transcript of DART Tutorial Sec’on 20: Model Parameter Es’maon

Page 1: DART Tutorial Sec’on 20: Model Parameter Es’maon

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

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DARTTutorialSec'on20:ModelParameterEs'ma'on

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ModelParameterEs'ma'on

Supposeamodelisgovernedbya(stochas'c)DifferenceEqua'on:

(1)whereuandwarevectorsofparameters.Also,supposewereallydon’tknowtheparametervalues(verywell).Canweuseobserva-onswithassimila-ontohelpconstrainthesevalues?Rewrite(1)as:

(2)wheretheaugmentedstatevectorincludesxt, u, and w. The modelismodifiedsovaluesofuandwcanbechangedbyassimila'on.Themodelmightalsointroducesome'metendencyforuandw.

dxt = f xt ,t;u( ) +G xt ,t;w( )dβt , t ≥ 0

dxtA = f A xt

A ,t( ) +GA xtA ,t( )dβt , t ≥ 0

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ModelParameterEs'ma'on

Fromtheensemblefilterperspec-ve:

Justaddanyparametersofinteresttothemodelstatevector;Proceedtoassimilateasbefore.Possibledifficul-es:

1.  Whereareparameters‘located’forlocaliza'on?2.  Parameterswon’thaveanyerrorgrowthin'me

(unlessweaddsome):couldleadtofilterdivergence.3.  Parametersmaynotbestronglycorrelatedwithany

observa'ons.

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Tes'ngParameterEs'ma'oninDART

DARTincludesamodels/forced_lorenz_96directory.

Eachstatevariablehasacorrespondingforcingvariable,Fi.

(3)Observa'onalerrorsforobs.insetiindependentofthoseinsetj.

(4)Canobserva'onsofsomefunc'onofstatevariablesconstrainF?

dXi / dt = Xi+1 − Xi−2( )Xi−1 − Xi + Fi

dFi / dt = N 0,σ noise( )

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Addingnamelistcontrolaspectsrequiredforexperimenta'on:

1.  reset_forcingif.true.,Fi=forcing(alsofromnamelist)foralli,t.

2.  random_forcing_amplitude forFi'metendency,

notusedifreset_forcingis.true.

Usingthese,cancreateOSSEsetswithfixed,globalFvalue.

Assimilatethesewithfilter,es'matestateandforcing.

GetanensemblesampleofFiateach'me.

Randomnoisecanbeusefulforavoidingfilterdivergence.

σ noise

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Sec'on5:6of15

Addingnamelistcontrolaspectsrequiredforexperimenta'on:

&model_nml num_state_vars = 40 forcing = 8.0 delta_t = 0.05 time_step_days = 0 time_step_seconds = 3600 reset_forcing = .false. random_forcing_amplitude = 0.10 /

Ifreset_forcing = .true.,Fi=forcing(alsofromnamelist)foralli,t.

forFi'metendency,notusedifreset_forcing = .true.

σ noise

models/forced_lorenz_96/work/

Usingthese,cancreateOSSEsetswithfixed,globalFvalue.

Assimilatethesewithfilter,es'matestateandforcing.

GetanensemblesampleofFiateach'me.

Randomnoisecanbeusefulforavoidingfilterdivergence.

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Assimila'onintheforcedLorenz96model

cdmodels/forced_lorenz_96/workcshworkshop_setup.cshUseMatlab,etc.toexamineoutput.Same40randomly-locatedobserva'onsasinlorenz_96cases.Forcingwasfixedat8.0intheperfect_modelrun.ValuesofFiaremodifiedintheassimila'on.Therewassomenoise(amplitudeof0.1)addedtothe'metendency.AmazingFact:Bestassimila1onsofstatecomewhenFivaries,evenbe7erthanwhenFiissettoexactknownvalueof8.0!

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Inmodels/forced_lorenz_96/workeditinput.nml

&filter_nml …

obs_sequence_in_name = "obs_seq.out”Ques-on:Whatwasthevalueoftheforcingintheperfect_modelrun?Youcantryanything(ethical)youwant.Feelfreetoaskforhelptotryexperimentsyoudon’tknowhowtodo.Remember:TheTruthisNOLONGERKNOWN!Consistentwiththethemeoftheworkshop…intheeventofa'e,arandomnumbergeneratorwillbeusedtodecidethewinner.Honor,fame,andfabulous(?)prizesgotothewinningteam!!!

Contest:Givenanobserva'onset,whatwasthevalueofF?

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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

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