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

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

©UCAR

DARTTutorialSec'on20:ModelParameterEs'ma'on

Page 2: DART Tutorial Sec’on 20: Model Parameter Es’maon

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

DARTTutorialSec'on20:Slide2

Page 3: DART Tutorial Sec’on 20: Model Parameter Es’maon

ModelParameterEs'ma'on

Fromtheensemblefilterperspec-ve:

Justaddanyparametersofinteresttothemodelstatevector;Proceedtoassimilateasbefore.Possibledifficul-es:

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

(unlessweaddsome):couldleadtofilterdivergence.3.  Parametersmaynotbestronglycorrelatedwithany

observa'ons.

DARTTutorialSec'on20:Slide3

Page 4: DART Tutorial Sec’on 20: Model Parameter Es’maon

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( )

DARTTutorialSec'on20:Slide4

Page 5: DART Tutorial Sec’on 20: Model Parameter Es’maon

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

DARTTutorialSec'on20:Slide5

Page 6: DART Tutorial Sec’on 20: Model Parameter Es’maon

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.

Page 7: DART Tutorial Sec’on 20: Model Parameter Es’maon

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!

DARTTutorialSec'on20:Slide7

Page 8: DART Tutorial Sec’on 20: Model Parameter Es’maon

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?

DARTTutorialSec'on20:Slide8

Page 9: DART Tutorial Sec’on 20: Model Parameter Es’maon

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'on20:Slide9


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