Joint Center for Satellite Data Assimilation Update and...

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JointCenterforSatelliteDataAssimilationUpdateandOverview

BenjaminJohnson,AER@NOAA/JCSDATomAuligné,Director,JCSDAWithcontributionsfrommultipleJCSDApartnersandcollaborators

DescriptionoftheJCSDA

JCSDACoreTeam

NASAGSFC

NOAANWS

U.S.AirForce

NOAAOAR

NOAANESDIS

U.S.Navy

ExternalResearch

Community/Academia

DescriptionoftheJCSDA

JCSDACoreTeam

NASAGSFC

NOAANWS

U.S.AirForce

NOAAOAR

NOAANESDIS

U.S.Navy

ExternalResearch

Community/Academia

Mission

…to accelerate and improve the quantitative use of research and operational satellite data in weather, ocean, climate and environmental analysis and prediction models.

VisionAn interagency partnership working to become a world leader in applying satellite data and research to operational goals in environmental analysis and prediction

Sciencepriorities

Radiative Transfer Modeling (CRTM), new instruments, clouds and precipitation, land surface, ocean, atmospheric composition.

JCSDAManagementStructure

ExecutiveTeamDirector(Auligne)*

PartnerAssociateDirectors(Baker,Gelaro,Zapotocny,Benjamin,Derber)

ChiefAdministrativeOfficer(Yoe)

ManagementOversightBoardNOAA/NWS/NCEP(Lapenta (Chair))

NASA/GSFC/EarthSciencesDivision(Pawson)NOAA/NESDIS/STAR(Kalb)

NOAA/OAR(Atlas)Dept.oftheAirForce/AirForceDirectorofWeather(Col.Gremillion)

Dept.of theNavy/N84andNRL(Capt.SauerandHansen)

AgencyExecutivesNASA,NOAA,DepartmentoftheNavy,andDepartmentoftheAirForce

AdvisoryPanel

ScienceSteeringCommittee

StrategicGoals

1. Expandcapabilitiesinassimilatingsatellitesensors

2. Spearheadacommunitydataassimilationinitiative

3. Addressscientificfrontierstooptimizetheuseofsatellitedata

4. Delivernewandimprovedtoolstosupportobservingsystemimpactassessments

5. Fosterimprovedorganizationalmanagement,interagencycoordinationandoutreachstrategies

PrioritizedNewSatellitesandSensors§ NewSensorsDataAssimilation

(newQC,erroroptimization,impactassessmentonNOAAforecastsystems)• JPSS1– ATMSandCrIS

• (LaunchdateNLTQ2FY17)• GOES-R– ABI(AMVwindsandradiances)

• (LaunchdateOctober2016)• COSMIC2

• (LaunchdateQ2FY17)§ HIMAWARI-8AHI(DryrunforGOES-RABI)§ GPM/GMI(clear-skyalreadyoperational)§ Megha-Tropiques SAPHIR(WVSounder)§ ISS-RAPIDSCAT(Scatterometer)§ GCOMWAMSR2§ SMAP§ JASON3

§ ExistingSensorsoptimization:(QC,Surface-sensitivechannelsassimilation,pre-processing,dynamicemissivity,etc)§ ATMS,SSMIS,AMSU,MHS

GOALS• Nationalunifiednext-generationDataAssimilationsystem• Increase scienceproductivityandcodeperformance

• Increase R2Otransition rate fromacademiccommunity

STRATEGY• Modularcodeforflexibility,robustnessandoptimization• Mutualizemodel-agnosticcomponentsacross

– Applications (atmosphere,ocean,stronglycoupled,etc.)– Models&Grids (operational/research,regional/globalmodels)– Observations (past,currentandfuture)

• Collectivereductionof“entropy”ofinformation

JointEffortforDataassimilationIntegration(JEDI)

JCSDACoreTeamPlannedProjectStructure

• Project#1:CRTM– ScienceProjectManagerandSoftwareEngineer– Draftworkplanunderconstruction

• Project#2:NewandImprovedObservations– Prioritizedlistofnewsensors+Readinessactionplans– Cloud-and-precipitation-affectedradiances– Radiancesoverland

• Project#3:JEDI– ScienceProjectManagerandSoftwareEngineer– UnifiedForwardOperator(atmosphere,ocean,sea-ice,etc)– JCSDAmemberofGSI/EnKF DAReviewCommittee

• Project#4:ObservingSystemImpactAssessment– JCSDAObservingSystemAssessmentStandingCapability(JOSASC)– CommercialWeatherDataPilot(CWDP)project

14thJCSDATechnicalReviewMeeting&ScienceWorkshoponSatelliteDataAssimilation;May31-June2,2016;MossLanding,CA:

• 30presentations,14posters

• Topics:PartnerOverviews(NRL,NESDIS,NCEP,NASA/GMAO,OAR,USAF),RadiativeTransferModeling,Sensor-specificDA,CloudsandPrecipitation,LandDA/SurfaceModeling,OceanDA,NewSensorDA,andImprovementsinDAtechniques

• DAcommunityandpartnersarenowfirmlyintothe“4DVar”world,witheachoperationalcenterusingsomevariationofthe4DVarframework(e.g.,4DEnVar).Manytalkscenteredaroundimplementation/debugging/modeladjustments,testing.

• Simultaneouslyrapidlyacceleratingtheassimilationofall-skysatelliteradiances:thegoalistomake“optimal”useoftheinformationcontentinsatelliteobservationstoprovideaccurateguidance totheanalysisandforecastmodels.

• http://www.jcsda.noaa.gov/meetings_Wkshp2016_agenda.php

JCSDAAnnualMeetingReview

JCSDAAnnualMeetingReviewCloudandPrecipitationDAActivitiesattheannualmeeting:

CRTM:UpdatedScatteringDatabaseforIRandMW,includingnon-sphericalicehydrometeors(B.Yi,P.Yang,TAMU)

ActiveRadarForwardOperatorforCRTM(B.Johnson,AER)(seeposterP2.14)

Retrieval-basedAssimilation:1DVARpreprocessingusingMIIDAPS(K.Garrett,NOAA)3DVARIWP/LWPretrievalassimilation(T.C.Wu,CIRA)

Nearreal-timeregionalandglobal3-dcloudpropertiesfromsatellites(W.SmithJr.,NASALRC)

Radiance-basedAssimilation:AMSR2TBAll-SkyUsingGEOS-5(J.Jin,etal.,NASAGMAO)

(N.B.:Mostresearchgroupspursueradiance-basedassimilation)

CASMGoals:(1) Forward-modelvariousactivesensorplatforms

(currentlysat.radar),extendingtoscatterometer,altimeters,andlidar

(2) Provideaphysicalbasisfor1-Dvariationalretrievals(e.g.,MIIDAPS)ofactiveobservations;

(3) Enhancedataassimilationcapabilitiesbyprovidingincreasedaccesstoactivesensordatasets

CommunityActiveSensorModule(CASM)B.Johnson(AER/NOAA),S.Boukabara (NOAA),K.Garrett(RTi/NOAA),PaulvanDelst(NOAAEMC)

(a)CASM(Sim.)Zm [dBZ]@Ku-band(14GHz)

(b)GPMDPR(Obs.)Zm [dBZ]@Ku-band(14GHz)

(c)CASM(Sim.)Zm [dBZ]@Ka-band(35GHz)

(d)GPMDPR(Obs.)Zm [dBZ]@Ka-band(35GHz)

ApplicationsofMIIDAPS1DVAR-DataFusion-

ConvergenceofRemoteSensingtechniqueswithDataAssimilationfornear-realtime,hourlyglobalanalyses.

Background6-hrForecast

ObservationsSat/In-situ

Preprocessing/BackgroundAdjustment

DataAssimilation

Postprocessing

MIIDAPS..otheralgors

GSI(3DVAR)

PostprocessingAlgors.

TraditionalRemoteSensingProducts Added-valueProductsTemperature(P)Humidity(P)CloudLiquid(P)Rain(P)CloudIce(P)Snow(P)CloudTopHeightCloudTopPressureCloudTopTemp.U-Wind(P)

V-Wind(P)SurfacePressureRainfallRateSnowfallRateSurfaceEmissivityLST/SSTSea-Ice-ConcentrationSea-IceAge

SnowCoverSnow-WaterEquiv.AerosolOpt.DepthTotalColumnO3

SoilMoistureSurfaceType

CAPECINLiftedIndexStreamFunction(ψ)Vorticity (ζ)VerticalVelocity(ω)Divergence(D)Geopotential Height(Z)FreezingLevelTrends

Adj.Background

MIIDAPS ApplicableSensorsMicrowave Infrared

NOAA-18AMSU/MHSNOAA-19 AMSU/MHSMetop-AAMSU/MHSMetop-BAMSU/MHSSNPPATMSF16-F19SSMI/SGPMGMIGCOM-W1AMSR2MTSAPHIR

NOAA-18 AVHRRNOAA-19AVHRRMetop-AIASIMetop-BIASISNPPCrISGOESSND/IMGMeteoSat SEVIRIHimawari-8AHI

Analysis

MIIDAPS1DVARprimarilyusedtoperformadjustmenttoDA(GSI)backgroundfieldfortemperature,moistureandhydrometeor

variables,usingMWandIRsatellitemeasurements

Slidecourtesy:S.Boukabara,K.Garrett,E.Maddy,B.Johnson,L.Liu,andE.Jones

AnalysisTPWvs ECMWF(w/BkgAdj)AnalysisTPWvs ECMWF(noBkgAdj)

ApplicationsofMIIDAPS1DVAR-ImpactofBackgroundAdjustment-

SatelliteCoverage12/23/1512UTC

183GHzConvergence(noBkgAdj) 183GHzConvergence(w/BkgAdj)AnalysisTPWvs ECMWF(w/BkgAdj)

Slidecourtesy:S.Boukabara,K.Garrett,E.Maddy,B.Johnson,L.Liu,andE.Jones

EducationandOutreachv SummercolloquiumonsatelliteDA(3-yearcycle)

v Jul-Aug.2015:FortCollins

v AnnualJCSDAScienceWorkshopv May2015:CollegeParkv May2016:MossLanding

v JointWorkshopswithProgramsandInternationalPartnersv Dec.2015:3rd JointJCSDA-ECMWFWorkshop:“Cloud&precip radianceDA”v Jan2016:JCSDASymposium@AMSAnnualMeetingv March2016:JointNCAR-JCSDAWorkshop:“Blueprintsfornext-genDA”v Jan2017:JCSDASymposium@AMSAnnualMeeting

v JCSDANewslettersv HighlightachievementsbyJCSDAscientists(internal/external)v Disseminateresultsandpromotecollaboration

v JCSDAwebsite:http://www.jcsda.noaa.gov/v Allpresentations/postersfrompreviousmeetings,documentation,

newsletters

Conclusions

• JCSDA =multi-agency,distributedcenterenablingpartnerstoshareeffortsandresultstoaccelerate,enhance,andexpanduseofsatellitedatainoperationalpredictionsystems

• KeystoSuccessInclude– DevelopmentandadoptionofCommonTools(CRTM)– R2OsupportedbyO2Rinfrastructure(R2O2R2….)– Effectivecommunicationb/wpartners,R&Ocommunities

• FutureOutlook– Exploringmeanstobemorecollaborativeinplanningandexecution– Transparencyofeffortsamonggroupsensuresthatduplicationofeffort

(technicalandscientific)isminimized,projectcoordination->efficiency– Collaborationwithsensor-specificscienceteamswillenablemorerapid

algorithminnovation(RT,QC,Physics,etc.).

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Questions?

AwesomeBackupSlides

t0 ti tn

obs

obs

obs

obs

obs

PreviousForecast CorrectedForecast

3DVar

Time

X

AssimilationWindow

NEMS/ESMF

Atm Dycore(TBD)

Wave(WW3/SWA

N)

SeaIce(CICE/SIS2/KIS

S)

Aerosols(GOCART

)

Ocean(HYCOM/MOM

)

Land Surface(NOAH)

Atm Physics(GFS)

Atm DA(GSI)

Obs.Pre-processor• Reading• Dataselection• BasicQC

Solver• Variational/EnKF• Hybrid

CODBMS:CommunityObservationDataBaseManagementSystem

Background& Obs.Error

Observations

Model

• Verification• Modelpost-proc.• Cal/Val,Monitoring• Retrievals• SimulatedObs.

CODBMS(obs +modelequivalent)

UnifiedForwardOperator(UFO)

• ModelInitialConditions• ObservationImpact(OSE,OSSE)• Situationalawareness• Reanalysis

DATAASSIMILATIONCOMPONENTSforAtmosphere,Ocean,Waves,Sea-ice,Land,Aerosols,Chemistry,Hydrology,Ionosphere

AnalysisIncrements

126-h track forecast initialized at 2012/08/31 00UTC

126-h MSLP forecast initialized at 2012/08/31 00UTC

(a) (b)

(c) (d)

126-h Max 10-m Wind forecast initialized at 2012/08/31 00UTC

126-h Storm Size forecast initialized at 2012/08/31 00UTC

(d) (e) (f)

(g) (h) (i)

30h

18h

(a) (b) (c)

Rain Rates CTL (d02) AddWC (d02)

18h

TheGSICapabilitytoAssimilateTRMMandGPMHydrometeorRetrievalsinHWRFTing-ChiWu,Milija Zupanski,LouieGrasso,PaulaBrown,ChrisKummerow,andJohnKnaff

Theobservationoperators(hsolid andhliquid)aredefinedasaverticalintegrationofwatervapormixingratioinexcessofsaturationwithrespecttoiceandliquid.hsolid andhliquid =f(T,P,qv)=f(T,Ps,q)

Control(ClearSkyDA) change T,P,q based onhsolid hliquid

All-SkyDAChallengesAll-SkyDA(intheU.S.)isstillverymuchinits“infancy”.Clouddetectionalgorithmsrelyonsingle-

channelscatteringindicesand/orpolarizationindices,oruseexistingcloudmasksderivedfromIRobservations.NOAAhas<10peopleactivelyworkingonAll-SkyDA– mostJCSDAotherpartnergroupshave~4(orfewer)peopleworkingontheissuefromvariousaspects.Weneedmore,withappropriateguidedfocus.JCSDAseekstoprovidetheframeworkforacceleratingtheseefforts.

DAandsubsequentforecastmodificationshavebeentraditionallydesignedwithcontinuous/slowlyvaryingvariablesinmind(TemperatureandHumidity),addingnon-linearheterogeneousvariables(bothphysicallyandradiatively)intothissystemisproblematic– frombothascienceperspectiveandaprogramming/implementationperspective.

Mostongoingactivitiesinvolvingall-skyDAseekto:(a)Improveerrorcharacterisation:Forecasterrorcovariance,correlatedobservationerrors,addressingnon-linearities,anddealingwiththerealityofnon-Gaussianerrors.(b)minimizetheobservationerror(y– H(x))throughtheimprovementofobs.QCandRTmodelaccuracy;(c)conditionthebackgrounderrortoallowforastrongerobservationalimpact;(d)makeadjustmentstothemodelphysicstoprovideimprovedphysicalconsistencythroughoutthesystemandtoaccountforsub-gridvariability;Eachimprovementhasacomputationalcost: havingveryfast,highlyoptimizedsystemsisdesirable.Mucheffortisspentonimprovingcomputationalefficiencywithinindividualmodelgroups(RT,DA,CRM,Forecast).