joint center for satellite data assimilation update and...
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JointCenterforSatelliteDataAssimilationUpdateandOverview
BenjaminJohnson,AER@NOAA/JCSDATomAuligné,Director,JCSDAWithcontributionsfrommultipleJCSDApartnersandcollaborators
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DescriptionoftheJCSDA
JCSDACoreTeam
NASAGSFC
NOAANWS
U.S.AirForce
NOAAOAR
NOAANESDIS
U.S.Navy
ExternalResearch
Community/Academia
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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.
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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
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StrategicGoals
1. Expandcapabilitiesinassimilatingsatellitesensors
2. Spearheadacommunitydataassimilationinitiative
3. Addressscientificfrontierstooptimizetheuseofsatellitedata
4. Delivernewandimprovedtoolstosupportobservingsystemimpactassessments
5. Fosterimprovedorganizationalmanagement,interagencycoordinationandoutreachstrategies
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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
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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)
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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
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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
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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)
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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)
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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
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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
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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
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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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16
Questions?
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AwesomeBackupSlides
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t0 ti tn
obs
obs
obs
obs
obs
PreviousForecast CorrectedForecast
3DVar
Time
X
AssimilationWindow
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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
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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
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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).