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EnKF radianceassimilation
JeffWhitaker1 andLiliLei21NOAA/ESRL/PSD
2CIRESandNanjingUniversity
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Motivation(1)
• Hypothesis:4DEnVar(non-hybrid)andEnKFshouldperformsimilarlyifall‘extra’constraintsturnedoffinVar solver.
• Experiment:T254singleresolution4DEnVar(nostaticB,balanceconstraint)vs ‘pure’EnKF(80members,operationallocalizationsettings).
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Motivation(2)4DEnVarvs EnKF,noradiances
RMSinnovationsforbackground
Virtuallynodifference,EnKF (red)perhapsslightlybetterforhumidity
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Motivation(2)4DEnVarvs EnKF,includingradiances
RMSinnovationsforbackground
4DEnVar(blue)slightlybetter,esp inSH.Why?Hypothesis: Differenceinvertical localization
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ObservationandModel-SpaceLocalization
Modelspace:• state-space covariances aretapered.• Involvesdistances betweenstatevariablesonly.• H applied after.• EnVar algorithmsusethisform.
Observationspace:• Computed afterH applied. • Involvesdistances betweenstateandobservationspacequantities. •Muchsimpler toimplement inEnKF systems.
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Motivation(3)4DEnVarvs EnKF,radiancesonly
RMSinnovationsforbackground
4DEnVar(blue)nowsignificantly better(althoughusingdeeper localization(green)forEnKF helpsquiteabit)
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Experimentswithasimplemodel
• 1-dKuramoto-Sivashinsky equation(https://www.encyclopediaofmath.org/index.php/Kuramoto-Sivashinsky_equation),oneofthesimplestPDEsthatexhibitsspatio-temporalchaos.
• u_t +u*u_x +u_xx +d*u_xxxx =0,periodicBCson[0,2*pi*L].– Energyentersthesystematlongwavelengths viau_xx (anunstable
diffusion term)– cascadestoshortwavelengthsduetothenonlinearity u*u_x– dissipates viad*u_xxxx– Solvedwithspectralmethod,usingNFouriercollocationpoints.
• Pythoncodeavailableathttps://github.com/jswhit/pyks.git
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Naturerun:L=16,N=128,dt=0.5,d=1(semi-implicitRK3scheme)
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Dataassimilationexperiments
• 10members,assimilationevery4timesteps(2timeunits).
• Forwardobservationoperator(H)includesanaveragingkernel,eitherGaussianorboxcar(runningaverage).R=0.1
• SerialEnSRF witheitherobservationspaceormodelspace localization.Observation‘location’assumedtobecenterofaveragingkernel.
• MultiplicativeinflationusingHodyss etal2016(dx.doi.org/10.1175/MWR-D-15-0329.1)algorithm.
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‘Modulatedensemble’approach tomodel-space localizationintheEnKF
Modelspacelocalization isPloc =ρ ○ Psample (○ denoteselement-wiseproduct)
Let Psample = XXT,ρ =LLT,thenPloc =LLT○ XXT=ZZT,whereZ =L Δ XandΔdenotes ‘modulation product’(BishopandHodyss, 20081)
If X has N columns (ens.members) andL has M columns (eigenvectors), thenZ=[[X1 ○ L1, X2 ○ L1,…, XN ○ L1],[X1 ○ L2, X2 ○ L2,…, XN ○ L2],…, [X1 ○ LM, X2 ○ LM,…, XN ○ LM]]
1doi: 10.1111/j.1600-0870.2008.00372.x
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RMSanalysiserrorasafunctionoflocalizationscale
•modelspacelocalizationworksbetterforBoxcarkernel.•ob spacelocalizationworksbetter(forshortlocalizationscales)forGaussiankernel.•Why?
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Meancorrelationbetweenob priorsandstatepriors(usinglocalization=50)
•correlationbetweenstateandob spaceismaximumatedgeofBoxcarkernel(notcenter).
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Whyisob spacelocalizationbetterinsomecircumstances?
•Localizationapplied toPbremovessomeofnegativesidelobes.•H operatorapplied tolocalizedcovariancethenproducestoolargeavaluefor K..•SeeLeiandWhitaker2015:DOI:dx.doi.org/10.1175/MWR-D-14-00413.1•Symptom(seenwithsomesatellite obs):single-obexperimentwith/withoutlocalization, increment islargerwith localizationthanwithout.
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Conclusions(simplemodelexpts)
• Modelspacelocalizationperformsbetterwhencorrelationbetweenob priorandmodelpriorsisnotmaximumatcenterofaveragingkernel(nominal’oblocation’).AdvantageisevenlargerthanshownbyCampell etal2009(dx.doi.org/10.1175/2009MWR3017.1).– Obspacelocalizationwith‘empiricallocalizationfunctions’(ELFs)canworkinthiscircumstance…
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ExampleofEmpiricalLocalizationFunction(ELF)withBoxcarKernelH (Lorenz40variablemodel)fromAndersonandLei2013:
http://dx.doi.org/10.1175/MWR-D-12-00330.1
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Conclusions(simplemodelexpts)
• Modelspacelocalizationperformsbetterwhencorrelationbetweenob priorandmodelpriorsisnotmaximumatcenterofaveragingkernel(nominal’ob location’).
• Ifcorrelationismaximumatcenterofaveragingkernel,ob spacelocalizationcanperformbetteriftherearenegativesidelobesincorrelationandlocalizationscaleisshort(LeiandWhitaker2015:DOI:dx.doi.org/10.1175/MWR-D-14-00413.1).
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EnKF radianceassimilationwithmodel-spacelocalization
• Asbefore,withGFST25480memberensemble,butusingmodelspacelocalization
• ‘Modulatedensemble’approachusedtoimplementmodel-spacelocalization intheverticalonly.
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‘Modulatedensemble’approachtomodel-spaceverticallocalization• Assume localization isseparable. Performhorizontal localization inob
space,vertical inmodel space.• Truncatethevertical localizationmatrix,retainingtheM eigenvectors
thatexplain90-95%ofthevariance (M isO(10)forthecurrentoperational configuration,N=80).The‘modulated ensemble’ thencontains MN members. - Horizontal localizationstillcomputed inobservation space.- TheEnKF algorithm isunchanged,exceptthatverticallocalizationisturned
off,and‘modulated’ensemble (inmodelandobservation space) isingestedinsteadof‘raw’ensemble.
- OnlytheoriginalNensemblemembers areupdatedintheDA,usingcovariances derived fromthefullMN membermodulatedensemble.
- Notstraightforward toimplementinLETKF,sinceanalysisweightsapplytofullMNmember ensemble (notoriginalNmemberensemble).
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Results(radiance-onlyassimilation)
• UsingmodelspacelocalizationintheEnKF improvestheuseofradiancedata.
• Performancesimilarto4DEnVar.
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Howmanymembersareneededtoturnoffverticallocalization?
• Since12eigenvectorsofverticallocalizationmatrixexplain99%ofvariance,thissuggeststhat80*12=960membersshouldbesufficient.
• Wehaveruna960memberLETKFensemblewithoutverticallocalization(veryefficientinLETKF,sinceanalysisweightscanbecomputedforentirecolumnatonce).
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4DEnVar(80members)vsLETKF(960members):radiancesonly
• Significantimprovementfromincreaseensemblesize/eliminationofverticallocalization.
• Performancesuperiorto4DEnVarwith80members.
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Conclusions• CaremustbetakenwhenassimilatingradianceobservationsintheEnKF withobservation-spacelocalization.
• O(1000)membersshouldbeenoughtoobviatetheneedforverticallocalization.
• Modelspacelocalizationimprovestheassimilationofradianceobservations.CanbeimplementedinEnKFusing‘modulatedensembles’,butwithasignificantincreaseincost.– Alternately,empiricallyderivedlocalizationfunctions(ELFs,Leietal2016:dx.doi.org/10.1002/2016MS000627) foreachinstrument/channel.
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ExamplefromLeietal2016:AMSU-AChannel9