22ndInternationalTOVSStudyConference

Saint-Sauveur,Québec,Canada31October–6November2019

Abstracts

Session1:Directreadout(oralpresentations)1.01 20yearsoftheNWPSAFPresenter:NigelAtkinson,MetOfficeAuthors:NigelAtkinson,SamPullen,JamesHocking,RogerSaunders,JohnEyre,PascalBrunel,PascaleRoquetAttheendof2018,theNWPSAFcelebratedanimportantmilestone–20yearssincetheagreementwassignedforthecreationoftheSatelliteApplicationFacilityonNumericalWeatherPrediction.CoordinatedandfundedbyEUMETSAT,theNWPSAFwasledbytheUKMetOfficewithECMWF,MétéoFranceandKNMIaspartners.Followingaperiodofdevelopment,theNWPSAFbecameoperationalin2004,andhasbeenprovidingsatellitedataprocessingsoftwareandmonitoringservicestouserseversince.InthelatestphaseDWDjoinedtheSAF,replacingKNMIwhichconsolidateditsNWPactivitiesintheOSISAF.ThesoftwarepackagesAAPPandRTTOVhavebeenNWPSAFsoftwaredeliverablesforthewholeofthattime,andevolvedthroughmanydifferentversionstosupportnewsatellitesandinstruments.WehavealsoseenthedevelopmentofdirectbroadcastretransmissionservicesbasedonAAPP,pioneeredbyEUMETSATintheirEARSservices,andsupportedbyNWPSAFmonitoringfacilities.Thetalkwillpresentsomehighlightsofthelasttwodecades,andwillalsolookforwardtoupcomingNWPSAFdevelopments,suchassupportforinstrumentsonEPSSecondGenerationandMeteosatThirdGeneration.1.02 StatusoftheDirectBroadcastNetworkforgloballycoordinatedreal-timeacquisition,processingandfastdeliveryofsatellitedirectreadoutdata,aninitiativeoftheWorldMeteorologicalOrganisationPresenter:PascalBrunel,MeteoFrance(forMikaelRattenborg)Authors:MikaelRattenborg(WMO),PascalBrunel(MeteoFrance)andWernerBalogh(WMO)TheDirectBroadcastNetwork(DBNet)isahighlysuccessfulcollaborativeundertakingoftheWorldMeteorologicalOrganizationanditsMembers.TheDBNetsystemprovidesfastacquisition,processinganddeliveryofsatelliteproductsfromdirectreadoutdata,primarilyforNumericalWeatherPrediction(NWP)applicationswithstringenttimelinessrequirements.Sinceabout10years,soundingdatafromtheATOVSsuiteofinstrumentshasbeenacquiredbyreceivingstationsaroundtheglobe,whichhasimprovedtheavailabilityandimpactofsatellitesoundingdataonshort-termregionalandglobalNWP.DBNetisnowbeingextendedtocovertheacquisitionofadvancedsatellitesounderdatafrominstrumentssuchasMETOP/IASIandSNPP/CrIS.ThepaperwillpresenttheDBNetstatusandimplementationplans,withparticularemphasisonthenumerousareaswherefeedbackisrequiredfromtheITSCcommunitytoguideitsfurtherdevelopment.1.03 TheDBNetCloudServiceforprovidinglow-latencysounderdatatoNWPcentersPresenter:LiamGumley,SpaceScienceandEngineeringCenter,UniversityofWisconsin-MadisonAuthors:LiamGumley,BruceFlynnTheWMODirectBroadcastNetwork(DBNet)isaworldwidegroupofDBantennaoperatorswhoacquireandprocessinfraredandmicrowavesounderdatafromoperationalmeteorologicalsatellitesinlowearthorbit.ThecalibratedandgeolocatedLevel1BdataareconvertedtoBUFRformatandthendisseminatedwithlowlatencytoNWPcentersforassimilationinregionalandglobalmodels.

ThetraditionalDBNetapproachrequireseachparticipatingDBantennasitetoinstall,configure,operate,andmaintainthevarioussounderdataprocessingpackages,includingAAPP,OPS-LRS,CSPPSDR,andFY3DB.Furthermore,eachsitemustconverttheresultingLevel1BdatatoanapprovedBUFRformatwithGTSheaders,nametheBUFRfilesaccordingtoconventionsspecifiedforDBNet,andthendisseminatethefilesviaGTSoraretransmissionservicesuchasEUMETCAST.ThisapproachhasbeensuccessfulwhenutilizedbymeteorologicalandsatelliteagenciessuchasMeteoFrance,EUMETSAT,NOAA,MetOffice,IMD,NSMC,INPE,Roshydromet,andBoM.However,itcanbedifficultforDBantennaoperatorstomaintainthesecapabilitiesoverthelongtermandensurethattheyadapttonewsatellites,sensors,andsoftwarepackages.AsDBsoftwarepackagesevolveandbecomemorecomplex,theyrequiremorecapableserverstorunthemandneweroperatingsystemstohostthem.SomeDBNetoperatorsdonothavetheabilitytoupdatetheirhardwareandoperatingsystemstomeettherequirementsofnewDBsoftwarepackageversions,withtheresultthateveniftheycanreceivethesounderdatafromanewsatellite,theyarenotabletoprocessit.Inaddition,therearemanyDBantennasitesthatareownedandoperatedbyotherentities,suchasuniversitiesandremotesensingagencies.ThesesiteshavethepotentialtocontributetoDBNet(especiallyinareaswhereDBNetcoverageispoorornon-existent)butitcanbechallengingforoperatorsatthesesitestoinstallandmaintaintherequiredsoftwaresystems.TheDBNetCloudServicewillprovideacentralizedcloud-basedingest,processing,anddeliveryserviceforinfraredandmicrowavesounderdata.TheserviceisintendedtoprovideaneasyandconvenientwayforDBantennaoperatorstocontributesounderdatatotheWMODBNetwithouthavingtoinstall,configure,validate,operate,andmaintainthelatestversionsofthevarioussounderdataprocessingpackages.ItwillprovidealocalaccesspointforantennaoperatorstouploadtheirLevel0datafilesimmediatelyafterasatelliteoverpass(theabilitytouploadafileafterapassisprovidedbyallmajorDBantennavendors).UponreceiptofanewLevel0datafile,theDBNetCloudServicewillimmediatelystartsprocessingthedatausingthelatestrecommendedversionoftherelevantsounderdataprocessingsoftware(e.g.,AAPP,OPS-LRS,CSPPSDR).AfterLevel1Bproductshavebeencreated,conversiontoBUFRisdoneusingtherecommendedformatting,metadata,andnamingconventionsdescribedintheWMOGuidetoDBNet.Finally,theBUFRproductswillbedeliveredtoanendpoint(e.g.,NOAA,EUMETSAT)fordisseminationonGTSandEUMETCAST.TheDBNetCloudServicewillmakeiteasierforexistingDBNetantennasitesandoperatorstocontributesounderdatatoDBNetwithlowlatency,highreliability,andgooddataquality.ItwillalsoallownewantennasitestocontributedatatoDBNetwithrelativelylittleeffort.TheservicewillensurethatNWPcentersreceivehighqualityinfraredandmicrowavesounderdatafromallDBNetsitesregardlessofsitelocationoroperator.ThispresentationwillprovideanoverviewoftheDBNetCloudServiceimplementation,currentprototypestatus,andbenefitstotheDBandNWPcommunities.1.04 TheUtilityofCSPPAtmosphericSoundingProductsPresenter:KathleenStrabala,UW-Madison/SSEC/CIMSSAuthors:KathleenStrabala,LiamGumley,ElisabethWeisz,JamesDavies,GeoffCuretonTheCommunitySatelliteProcessingPackage(CSPP)isaNOAAfundedeffortthatprovidesportablestandalonesoftwaretocreateamyriadofpolarorbitermeteorologicalsatellitecalibratedandlevel2scienceretrievalproducts.Althoughtheprimarygoalofthiseffortistosupportdirectbroadcastoperationaldecisionmakers,itcanalsobeusedbystudentsandresearcherswhowanttoprocessandinvestigatespecificevents.ThecurrentsuiteofCSPPsoftwareincludesfourdifferenttechniquesforderivingtemperatureandmoisturesoundings.TheNOAA/NESDIS/STARUniqueCombinedAtmosphericProcessingSystem(NUCAPS)algorithmcombineshyperspectralandmicrowaveinstruments,CrIS/ATMSonNOAA-20andS-NPPsatellitesaswellasIASI/AMSUA/MHSonMetop-AandMetop-B.TheseretrievalsarecurrentlybeingusedoperationallybytheUSNationalWeatherServiceforwiderangingapplicationsincludingidentifyingregionsofdestabilizationinthemid-latitudesandareasofverycoldairaloftinthepolarregions.NUCAPSistheofficialNOAAsoundingproductforJPSS.TheCSPPNOAA/NESDIS/STARMicrowaveIntegratedRetrievalSystem(MIRS)softwareprovidesmicrowaveretrievalsfrom7differentinstrumentsincludingNOAA-20andS-NPPATMS,andMetop-AandMetop-BAMSU-AandMHS.CSPPalsoincludestheUniversityofWisconsinsingleField-of-View(FOV)hyperspectralinstrumentstatisticaldualregressionretrievalsfromCrIS,IASIandtheAIRSinstrumentontheAquasatellite.TheseretrievalsarebeingusedincombinationwithGOES-Rtemperatureandmoistureretrievalstoidentifyregionsofrapidatmosphericdestabilization.Finally,thelegacyInternationalATOVSProcessingPackage(IAPP)isalsosupportedbyCSPPtoallowthosewhowanttocreateconsistentclimate

qualityretrievalsfromNOAA-15,NOAA-16,NOAA-18,NOAA-19,Metop-AandMetop-BHIRS,AMSU-AandMHSinstruments.Thispresentationwillincludeanumberofexamplesonhowtheretrievalsarebeingusedbytheenvironmentaldecisionmakingcommunity.AsisthecasewithallCSPPretrievalsoftware,itisfreelydistributed,includesuptodatealgorithms,ispre-compiledfor64-bitIntelLinuxoperatingsystems,isdesignedspecificallytobeeasytoinstallandoperate,andrunsefficientlyonmodernhardware.Session1:Directreadout(posterpresentations)1p.01 CurrentstatusandplansofDirect-readoutLEOsatellitedataprocessinginNMSC/KMAPresenter:HyunjongOh,NationalMeteorologicalSatelliteCenterofKoreaMeteorologicalAdministration(forDahyeBae)Authors:DahyeBae,HyunjongOh,AhyoungShin,YongsangKimNationalMeteorologicalSatelliteCenter(NMSC)/KoreaMeteorologicalAdministration(KMA)isprocessingvariousdirect-readoutLow-Earth-Orbit(LEO)satellitedatasuchasAdvancedTIROSOperationalVerticalSounder(ATOVS),InfraredAtmosphericSoundingInterferometer(IASI),AdvancedTechnologyMicrowaveSounder(ATMS)andCross-trackInfraredSpectrometer(CrIS)radiancedataforNWPdataassimilationandweatheranalysis.Currently,NMSCisoperatingATOVSandAVHRRPre-processingPackage(AAPP),CommunitySatelliteProcessingPackage(CSPP)andInternationalATOVSProcessingPackage(IAPP)fordirectreadoutdataprocessing.KMAhasprovidedthedirect-readoutATMSandCrISlevel1CdataofSuomi-NPP(NPOESSPreparatoryProject)satelliteviaGTSforDirectBroadcastNetwork(DBNet)activitysince2018,andisworkingonprocessingthedirect-readoutATMSandCrISdataofNOAA-20satellitewhichwillbesharedviaGTStoo.Inthispaper,wedescribethecurrentstatusandfutureplansofKMA’sdirect-readoutLEOsatellitedataprocessingtosupportNWPassimilationincludingthequalitycheckactivities.1p.02 CSPPLEOforJPSS,Metop,NOAA,andFY-3satellites:NewfeaturesandenhancementsPresenter:LiamGumley,SpaceScienceandEngineeringCenter,UniversityofWisconsin-MadisonAuthors:LiamGumley,KathyStrabala,ScottMindock,NickBearson,JamesDavies,GeoffCuretonTheCommunitySatelliteProcessingPackage(CSPP)forlowearthorbit(LEO)satelliteshascontinuedtoevolvewiththeadditionofsupportfornewsatellitesandsensors;updatesandimprovementstoexistingproducts;andsupportfornewgeophysicalproducts.SupportforthenewNOAA-20operationalsatellite(launchedinNovember2017)hasbeenaddedtotheCSPPLEOsuitetoallowcreationofgeolocatedandcalibratedsensordatarecords(SDRs)forATMS,CrIS,andVIIRS.NOAA-20supportwasalsoaddedtoatmosphericprofileretrievalsoftwarepackagesincludingNUCAPS,MIRS,andHSRTV.Newgeophysicalproductgenerationsoftwarepackagesforfloodandwildfiredetection(bothsupportingNOAA-20VIIRS)wereaddedtotheCSPPLEOsuite,andthePolar2GridimagecreationtoolkitwasupdatedtofullysupportNOAA-20VIIRSandMetop-CAVHRR.Anewatmosphericprofileretrievalsystem(IASI-NUCAPS)wasintroducedforMetop-AandMetop-BIASI.NewCSPPLEOreleasesinclude

• VIIRScloud,aerosol,cryosphere,andlandsurfacegeophysicalproductsforSNPPandNOAA-20;• ACSPOSSTproductupdatestosupportNOAA-20VIIRSandMetop-CAVHRR;• CLAVR-xcloudproductupdatestosupportNOAA-20VIIRSandMetop-CAVHRR;• GAASPAMSR2cryosphereproducts;• Polar2GridsupportforFY-3B/CVIRRandFY-3DMERSI-2imagery

Thispresentation/posterwillreviewthestatusofthecurrentCSPPLEOsoftwaresuiteandprovideexamplesofnewcapabilitiesandproductsthathavebeenrecentlyadded.

1p.03 GenerationofdirectreadoutsoundingdataproductsattheBureauofMeteorologyPresenter:DavidHoward,BureauofMeteorologyAuthors:DavidHoward,FionaSmith,SusanRennie,LeonMajewski,NigelAtkinsonTheBureauofMeteorologyreceivesdatafromeightlocalreceptionstations;sixontheAustraliancontinentandtwoonAntarctica.Wearenowacquiringdirect-readoutdatafromATOVS,IASI,CrIS,ATMSandAIRSwhichisprocessedtoL1dusingAAPPforassimilationinourNWPmodels.Thedataisofparticularvalueforoursixcity-scaleconvectivemodelconfigurations(seeposterbySmithetal)becauseofitsimprovedtimelinessoverglobaldatastreams.Thisposterwillgivesomeexamplesofthedatainuseinourcitymodels.WewillalsobemakingthisdataavailabletotheinternationalcommunityviaDBNetduring2020.1p.04 CurrentStatusandFuturePlanondirectreadoutactivityinMSC/JMAPresenter:MasamiMoriya,JapanMeteorologicalAgencyAuthors:MasamiMoriyaMeteorologicalSatelliteCenter(MSC)ofJapanMeteorologicalAgency(JMA)hasreceivedandprocesseddirectbroadcastdatafromLowEarthOrbit(LEO)satellitesformorethanfiftyyears.InJMA,theseproductshavebeenutilizednotonlyformonitoringvolcanicash,theAsiandust,seasurfacetemperatureandseaicebutalsofornumericalweatherprediction(NWP)throughassimilationinNWPdivision.OnDecember2018,MSCbegantoprocessdirectbroadcastdatafromNOAA20.Atpresent,MSCprocessesdirectbroadcastdatafrom6LEOsatellitesintotal,i.e.NOAA-18,19,20,S-NPPandMetop-A,B.DirectbroadcastdatafromMetop-Cisalsoplannedtobereceivedinthenearfuture.BesidesthedirectbroadcastdatareceivedatKiyosestationinJapan,MSCalsoprocessesthosereceivedatSyowastationinAntarcticawiththecooperationoftheNationalInstituteofPolarResearchofJapan.TheseproductsareveryimportantforJMA’smeteorologicaloperation.Ontheotherhand,someofourproductsrelatedtoCrIS,ATMSandATOVSaresharedwithotherNWPcentersviatheDirectBroadcastNetwork(DBNet)andcooperativeorganization(Wisconsin/CIMSS).Thiscontributestometeorologicaloperationinothercountries.ThispresentationwillshowcurrentstatusandfutureplanofJMA/MSCLEOactivities.1p.05 IASIcloudmaskcomparisonbetweenglobalbroadcastandlocalprocessingPresenter:MathieuAsseray,CNRM/CEMSAuthors:MathieuAsserayThecomparisonbetweenglobalEUMETSATcloudmaskandlocalMAIAcloudmaskappliedwithAVHRRprojectioninIASIpixelallowsustolocalizethemaindifferencesoncloudfractionbyusingthetworetrievals.ThefirststepofthecomparisonconsistsindisplayingcloudfractioninIASIpixelmapsforglobalandlocalgranulesinordertohighlightthelocalizationofthedifferencesforeachmask.Thestudyhasbeenleadonmanysingleorbitssituation,inJanuary2019.Anhistogramisestablishedforeachcaseandpresentthenumberofdifferentpixelaccordingthespreadglobal-local.Afterprocessingonthewholestudiedgranule,thefirstresultsindicatethatmostofpixelspresentnoorlowdifferences(between-0.05%and+0.05%ofcloudfraction).Howeverthehistogramshowsanoverestimationofthelocalcloudmaskandthemapshowssomeseaandlandinfluencesontheoverestimation.theAVHRRcloudtypefromMAIAandtheSEVIRIcloudtypeproductaremappedforeachsituation.Theyareusedtoidentifythenatureofthecloudstructures.Thesecomparisonsshowaneventualcorrespondencebetweencloudfractiondifferenceandcloudtype,especiallywithlowlevelfractionalclouds.Furtherstudywillbeleadinordertovalidatethisassumption.Meanwhiletheseresultswillberuninproductiononthepurposeofforecastandresearch.Thesecondstepconsistsinprocessingmonthlydataandproducingasimilarlyhistogrambutbyusingalongerperiodandmoreorbits.Thisworkwillhelptounderstandthesea/landandday/night/twilight/sunriseeffectsonthecloudfractiondifference.Thiswillhelpalsotobestunderstandthedifferencesbetweenthetwomasksbylookingatthealgorithmsusedbyeachofthem.TheuseoftheindependentAVHRRproductswillhelpinunderstandingthedifference.1p.06 PortingtheOPSAVHRRclustersalgorithmtoVIIRSinCrISfootprintPresenter:PascaleRoquet,Météo-FranceAuthors:PascaleRoquetAtITSC-XX,wasfirstexpressedarecommendationthattheAVHRRclusteralgorithmavailableinAAPPshouldbeusedforallhyperspectralsounders.InOPS,thesoftwarewhichprocessesIASI,amethodcalled"Nuees

Dynamiques"groupstheAVHRRpixelsineachIASIfieldofregardsbyclustersofhomogeneousscenes.IntheIASIBUFRfile,foreachIASIFOV,theusercanretrievethenumbersofpixelsbyclustersaswellasthemeanandthestandarddeviationoftheAVHRRradiances.TheseinformationcanbeusedinordertoimprovethedataassimilationofIASIradiancesinnumericalweatherpredictionmodels.InAAPP,thismethodwasadaptedin2018toVIIRSandCrISforS-NPPandJPSSNOAA20satellites.ThisposterdescribeshowthiswasdoneandhowadirectbroadcastusercanusetheclustersoftwareforS-NPPandJPSSNOAA20.1p.07 CSPPSDRandCSPPVIIRSASCI,Havingyourcakeandeatingittoo!Presenter:ScottMindock,SSEC/CIMSSAuthors:ScottMindock,RayGarcia,GraemeMartin,KathyStrabala,NickBearson,LiamGumley,AllenHuangTheCSPPSDRTheCSPP(CommunityScienceProcessingPackage)TeamatSSEC/CIMSShascreatedtheCSPPVIIRSASCI1.0softwaretosupportSNPPandJPSS-1Level2productcreation.TheCSPPVIIRSASCIpackagecreatesEnterpriseLevel,Cloud,Ice,Snow,AshandAerosolproductsusingNOAA/NESDISEnterpriseLevelalgorithms.TheCSPPSDRpackagecreatesLevel1(SDRs)fromSNPPandJ01RDRsacquiredfromDirectBroadcast.CSPPSDRandCSPPASCIworktogetherwithyourantennasystemtocreateaneasytouseandmaintainprocessingsystem.IfyoumissionrequiresCrISandyouwantCSPP!IfyouneedATMSyouneedCSPP!VIIRSisyourswithCSPP,andnowwithCSPPASCIyoucanhaveClouds,Snow,IceandAerosols,withtheCSPPeaseandreliability.1p.08 Polar2GridandGeo2Grid:OpenSourceSoftwareforCreatingHighQualityImagesPresenter:KathleenStrabala,UW-Madison/SSEC/CIMSS(forDavidHoese)Authors:DavidHoeseandKathleenStrabalaCreatinghighqualityimagesfrommeteorologicalinstrumentsonpolarorbiterandgeostationarysatellitesposessignificantchallenges,includinghowtoreadthedata(inputformats),thetypeofinstrumentthatobservedthedata(Imager,Sounder,etc.),whatsoftwaregeneratedthedatafiles,andthewhattoolwillbeusedtodisplaytheendproduct(outputformats).Tosimplifythisprocess,NOAAhasfundedtheCooperativeInstituteforMeteorologicalSatelliteStudies(CIMSS)attheUniversityofWisconsintocreateapairofopensourcecommandlinetoolscalledtheCommunitySatelliteProcessingPackage(CSPP)LEOPolar2GridandCSPPGeoGeo2Grid.ThesetoolkitsprovideaneasyinterfaceforreprojectingandreformattingimagerinstrumentsandaselectednumberofscienceproductsintoavarietyofoutputformatsincludingGeoTIFF,theUSNationalWeatherServiceAWIPSNetCDFformat,NinJo,BinaryandKMZ.Commandsarecarriedoutthroughsimplebashshellscriptsthatwrapunderlyingself-containedpythoncode.Thesetoolshandleallofthecomplexityinvolvedinthisconversionincludingresamplingtocustomuniformgridsorregionsofinterest,perceptualenhancements,atmosphericcorrections,andRGB,includingtruecolor,imagecreation.Whilethetoolsprovidesimpleinterfaces,theydonotsacrificeperformanceandcancompletetheconversionsinsecondsonlargeswathsofdatabytakingadvantageofnewopensourcetoolssuchasDaskparallelprocessing.Polar2GridisthetoolkitcurrentlyusedatdirectbroadcastreceivingstationstoprovidePolarOrbiterimageryfromVIIRS,MODISandAVHRRimagertotheUSNationalWeatherServiceForecastofficesintheContinentalUnitedStates,aswellasAlaska,Hawaii,PuertoRico,andGuamwithinminutesofanoverpassofthesatellites.Geo2Gridversion1.0softwarewhichsupportsGOES-16andGOES-17ABIandHimawari-8AHIwasreleasedon1March2019.Bydefault,imagesofallABI/AHIimagerbandsplustrueandfalsecoloroutputimagefilesaregeneratedatthehighestpossibleresolutionwitheachexecutionoftherunscript.Creationoftruecolorimageryincludesatmosphericcorrection,bandsharpening,andthecreationofapseudo-greenbandfortheABIinstrumentsaspartofthedefaultgeo2grid.shexecution.AsisthecasewithallCSPPLEOandGeosoftware,itisfreelydistributed,ispre-compiledfor64-bitIntelLinuxoperatingsystems,isdesignedspecificallytobeeasytoinstallandoperate,andrunsefficientlyonmodernhardware.1p.09 CommunitySatelliteProcessingPackageforGeostationaryData(CSPPGeo)Level2ProductsandImageGenerationforDirectBroadcastPresenter:GraemeMartin,UW-MadisonSSEC/CIMSSAuthors:GraemeMartin,LiamGumley,NickBearson,JessicaBraun,AlanDeSmet,GeoffCureton,DaveHoese,RayGarcia,TommyJasmin,ScottMindock,EvaSchiffer,KathyStrabala

TheCommunitySatelliteProcessingPackageforGeostationaryData(CSPPGeo)projectisfundedbytheNOAAGOES-RProgramtocreateanddistributesoftwareallowinguserstoprocessdirectbroadcastdatareceivedfromgeostationaryweathersatellites.CSPPGeosoftwareeasytoinstallandrun,withnocompilationorinstallationofexternaldependenciesrequired.ThesoftwareisprovidedfreeofcostduetotheongoingsupportofNOAA.TheUSGOES-16andGOES-17satellitesaresupported,inadditiontothelegacyGOES-13and-15satellitesandtheJapaneseHimawari-8satellite.CapabilitiesincludeprocessingofrawGRBdatareceivedfromtheGOES-16and-17,generatingproductsfromtheAdvancedBaselineImager(ABI),GeostationaryLightningMapper(GLM)andspaceweatherinstrumentsinrealtime.Level2geophysicalproductscanbegeneratedfromABIdatausingresearchversionsoftheoperationalGOES-Rsciencealgorithms,runninginanapplicationknownastheNOAAAlgorithmIntegrationTeam(AIT)Framework.Level2productscanbegeneratedfromAdvancedHimawariImager(AHI)datausingadaptedGOES-RalgorithmsrunningintheGeostationaryCloudAlgorithmTestbed(GEOCAT).TherecentlyreleasedGeo2Gridsoftwareallowsgenerationofhigh-quality,full-resolutionsingle-bandandcompositeRGBimagesincludingtruecolor,naturalcolor,airmass,ash,dust,fog,andnightmicrophysics.TrueimagescontainRayleighcorrection,artificialgreenbandandsharpeningto500m.ThispresentationwillfocusoncurrentCSPPGeoLevel2productandimagegenerationcapabilities,andtouchonfutureplans.Session1:RadiativeTransferandCommunitySoftware(oralpresentations)1.05 RecentAdvancesintheCommunityRadiativeTransferModelPresenter:BenjaminJohnson,JCSDAAuthors:BenjaminT.Johnson(JCSDA),PatrickStegmann(JCSDA),ThomasGreenwald(JCSDA),JamesRosinski(JCSDA),EmilyLiu(EMC),MingChen(ESSIC),AndrewCollard(EMC),TongZhu(CIRA)TheJointCenterforSatelliteDataAssimilation(JCSDA)CommunityRadiativeTransferModel(CRTM)isafast,1-Dradiativetransfermodelusedinnumericalweatherprediction,calibration/validation,etc.acrossmultiplefederalagenciesanduniversities.ThekeybenefitoftheCRTMisthatitisasatellitesimulator,inthatitprovidesahighlyaccuraterepresentationofsatelliteradiancesbymakingappropriateuseofthespecificsensorresponsefunctionsconvolvedwithaline-by-lineradiativetransfermodel(LBLRTM).CRTMcoversthespectralrangesconsistentwithallpresentoperationalandmostresearchsatellites,fromvisibletomicrowave(L-Band).Thecapabilitytosimulateultravioletradiancesarebeingaddedoverthenexttwoyears.ThistalkwillfocusonrecentadvancesintheabilityoftheCRTMtosimulatesatelliteradiances,inparticularimprovementsincloudyradiancesimulation,aerosolimpactedradiances,improvementsinsurfaceemissivitymodeling,theadditionoffullpolarizationsupport(experimental),updatedcoefficientfilesinsupportofnewandupcomingsensors,L-Bandsupport,activesensorforwardmodeling(space-basedradarandlidar),andmanyotherfeaturescomingonlineoverthenextyear.ThesechangesrepresentasignificantandnecessaryexpansionoftheCRTMcapabilitiestoallowittocontinuetoperforminanall-weather,all-surface,all-sensorenvironment.1.06 RTTOVdevelopmentstatusPresenter:JamesHocking,MetOfficeAuthors:PascalBrunel,AnaFernandezdelRio,AlanGeer,StephanHavemann,JamesHocking,ChristinaKöpken-Watts,CristinaLupu,MarcoMatricardi,PascaleRoquet,DavidRundle,RogerSaunders,LeonhardScheck,OlafStiller,EmmaTurner,JérômeVidotSinceITSC-21,twonewminorreleasesofRTTOV,v12.2andv12.3,weremadeavailabletousers.Theseupdatesincludevariousdevelopments.RTTOVhasbeenextendedtoenablesimulationsinthefar-infraredupto100microns,andinthesub-mmtosupportMetopSGICI.Microwavecoefficientfilesarenowavailablebasedonmeasuredspectralresponsefunctions.TheMFASISfastmodelforvisiblecloudysimulationshasbeenincorporatedintoRTTOVenablingitsuseforreal-timeapplications.Newopticalpropertiesareavailableforcloudliquidwater,icecloudandaerosolsinvisible/infraredscatteringsimulations,andanewtoolisavailableforuserstogeneratecustomaerosolopticalpropertyfilesforusewithRTTOV.Updatestothemicrowavescatteringmodel,RTTOV-SCATT,includenewoptionsfortheliquidwaterpermittivityparameterisation(thesameoptionsarealsoavailableinRTTOVforcloudliquidwaterabsorption),theoptiontouseoptical

propertiesfornon-sphericalparticlesfromtheARTSsingle-scatteringdatabase,andanewoptionaloutputstructureisavailablecontainingtheinformationnecessaryforall-skysurfaceemissivityretrievals.ImprovementstothetreatmentofthesurfaceincludethenewCAMELclimatologyatlas(basedonamulti-yearclimatology),anewsolarseaBRDFmodel,andnewoptionsrelatedtoLambertianvsspecularsurfaces.PC-RTTOVhasbeenupdatedtoenablesimulationswithadditionalvariabletracegasesandOPACaerosols.TheimplementationoftheHTFRTCfastradiativetransfermodelhasbeensubstantiallyimprovedandextended,forexample,toenableallvariabletracegasesandoptionallyoutputovercastradiances.Anoverviewofthenewcapabilitieswillbepresentedalongwithalookaheadtoplanneddevelopmentsforthenextmajorrelease,RTTOVv13,inSeptember2020.1.07 Comparisonsofoceanradiativetransfermodelswithsatelliteobservationsfrom1.4to89GHzPresenter:LiseKilic,ParisObservatoryAuthors:LiseKilic,CatherinePrigent,StephenEnglish,JacquelineBoutin,ThomasMeissnerandSimonYuehSatelliteobservationsarerequiredtomonitor,understandandpredictthestateoftheoceanandatmosphereandtoquantifytheenergyandhydrologicalcycles.Theoceansexchangelargeamountsofheat,moistureandgaseswiththeatmosphereattimescalesfromdays(e.g.,mixingassociatedwithatmosphericstorms),toyears(e.g.,ElNiño)andtocenturies(climatechange).Theseasurfacetemperature(SST),oceanwindspeed(OWS)andseasurfacesalinity(SSS)arefundamentalvariablesforunderstanding,monitoringandpredictingthestateoftheoceanandatmosphere.Theyareneededtocorrectlydescribeair-seainteractionsoccurringatdifferentscales,uptooceanicmesoscale,andtodrivecoupledocean-atmosphereNumericalWeatherPrediction(NWP)models.TheanalysisoftheseoceanparametersfrompassivemicrowavesatellitemeasurementsrequireaRadiativeTransferModel(RTM)inordertointerpretthesatelliteBrightnessTemperatures(TBs)intermsofSST,SSS,andOWS.UsuallyoceanRTMsaredevelopedforaspecificapplicationand/orinstruments,i.e.aselectedrangeoffrequenciesandincidenceangles.Forthefirsttime,withtheCopernicusImagingMicrowaveRadiometer(CIMR)mission(Kilicetal.,2018),1.4GHz(L-band)observationswillbecombinedwith6.9,10.6,18.7and36.5GHz(C,X,Ku,andKa-bands)observationsandwillprovidecoincidentSST,SSS,andOWSmeasurements.Therefore,anoverviewoftheexistingRTMsworkingatthesefrequenciesandacomparisonbetweenthemisneeded.Inthisstudy,weproposetocomparethreedifferentoceanRTMsfrom1.4to89GHztosatelliteobservationsfromSMAPandAMSR2.ThiscomparisonexerciserequiredthedevelopmentofadatasetofsatelliteobservationsfromSMAPandAMSR2,collocatedwithsurfaceandatmosphericparameters.ConsistentECMWFERA-InterimandMercatorreanalysisdataarechosen.Thedatabasesamplestheglobaloceansoverayear.TheselectedoceanRTMsare(1)theLOCEANRTMdevelopedfortheSMOSmission(Dinnatetal.,2003andYinetal.,2016)(2)theFASTmicrowaveEmissivityModel(FASTEM)parameterizedfromafullphysicalmodel(Liuetal.,2011),(2)theRemoteSensingSystem(RSS)empiricalmodel,fittedwithSSM/IandWindSatobservationsbetween6and9GHz(MeissnerandWentz2004,2012)andwithAquariusat1.4GHz(Meissneretal.,2014,2018).ThesimulationswerecarefullycomparedtotheobservedTBs.Firstly,globalsystematicerrorsbetweensimulationsandobservationswerecomputed.Thebiasestendtoincreasewithfrequency,andaregenerallyhigherathorizontalthanatverticalpolarizations.Thisispartlyduetotheincreasingeffectoftheatmosphericcontributionwithfrequency(essentiallyundetectedclouds),especiallyathorizontalpolarization.PartofitcanalsostemfromAMSR2calibrationissues.Secondly,theanalysisfocusedontheaccuracyoftheRTMsasafunctionofthekeyoceanvariables,SST,SSS,andOWS(oncetheglobalbiasesaresubtracted).Majordiscrepancieswiththeobservationswerefoundatfrequenciesabove1.4GHz,forOWShigherthan7m/s,withtheLOCEANandtheFASTEMmodels,withdifferencesstronglyincreasingwithincreasingOWS.Possiblemodelimprovementswerediscussed.Theanalysistendedtoshowthatafrequencydependenceneedstobeaddedtothefoamcovermodelor/andonthefoamemissivitymodel.Thestudyalsostressedthatthesetwocomponentshavetobeconsideredconsistentlyandjointly,alloverthefrequencyrange.Effortsshouldbedevotedtothemodelingofthefoamcontribution,takingintoaccounttheOWS,butalsothefrequencydependence,andpossiblythewavedissipativeenergy.

ColdSSTswerealsoidentifiedasasourceofdisagreementbetweenthesimulationsandtheobservations,regardlessofthemodel.Thisisacriticalissue,especiallyatverticalpolarizationat6GHzwhichisthekeychannelfortheSSTanalysisfromsatellite.Largeuncertaintiesstillexistinthemodelingofthedielectricconstantsofseawater,particularlyatlowSSTs.Newlaboratorymeasurementsofthedielectricpropertiesofoceanwaterhaverecentlybeenundertakenat1.4GHz:theirextensiontohigherfrequenciesshouldbeencouraged,insistingontheuncertaintyestimationandwithspecialattentiontothe6GHz.ReferencesE.P.Dinnat,J.Boutin,G.Caudal,andJ.Etcheto.IssuesconcerningtheseaemissivitymodelingatLbandforretrievingsurfacesalinity.RadioScience,38(4),aug2003.ISSN00486604L.Kilic,C.Prigent,F.Aires,J.Boutin,G.Heygster,R.T.Tonboe,H.Roquet,C.Jimenez,andC.Donlon.Expectedperformancesofthecopernicusimagingmicrowaveradiometer(cimr)foranall-weatherandhighspatialresolutionestimationofoceanandseaiceparameters.JournalofGeophysicalResearch:Oceans,123(10):7564–7580,2018.Q.Liu,F.Weng,andS.J.English.Animprovedfastmicrowavewateremissivitymodel.IEEETransactionsonGeoscienceandRemoteSensing,49(4):1238–1250,2011.ISSN01962892.T.MeissnerandF.J.Wentz.TheEmissivityoftheOceanSurfaceBetween6and90GHzOveraLargeRangeofWindSpeedsandEarthIncidenceAngles.IEEETrans.Geosci.RemoteSens.,50(8):3004–3026,Aug2012.ISSN0196-2892.T.Meissner,F.J.Wentz,andL.Ricciardulli.Theemissionandscatteringofl-bandmicrowaveradiationfromroughoceansurfacesandwindspeedmeasurementsfromtheaquariussensor.JournalofGeophysicalResearch:Oceans,119(9):6499–6522,2014.X.Yin,J.Boutin,E.Dinnat,Q.Song,andA.Martin.Roughnessandfoamsignatureonsmos-mirasbrightnesstemperatures:Asemi-theoreticalapproach.Remotesensingofenvironment,180:221–233,2016.1.08 AdvancesoftheCommunitySurfaceEmissivityModels(CSEM)inSupportofNWPDataAssimilationPresenter:MingChen,CICS-ESSIC,UniversityofMarylandAuthors:MingChen,KevinGarrettandYanqiuZhuTheCommunitySurfaceEmissivityModels(CSEM)developedatNOAA/NESDIS/STARisafeature-richsurfaceradiativetransfermodellingsysteminsupportofthedataassimilationofsurfacesensitivechannelobservations,thequalitycontrolofsoundingchannels,andthephysicalretrievalofsurfacefeatures.WiththeOOPdesign,CSEMmaybecoupledwithmultiplehostmodels,e.g.,CRTM,RTTOV,orusedasastand-aloneresearchtool.NewmodelsandmodelimprovementsmaybeeasilyimplementedasoptionsintotheCSEMalgorithmrepositoryandcomparedwithotherexistingkindredmodels,andultimatelytransferredforvariousoperationalapplications(e.g.,dataassimilation).ThefirstversionofCSEM(CSEMV1.0.0)hasbeenintegratedwithCRTMREL-2.3.xandtestedinFV3GSI.CSEMV1.0.0includesanumberofnewandexpandedmodelcapabilitiesinadditiontoalltheexistingCRTMsurfaceRTmodules.CSEMwillbereleasedwiththenextmajorCRTMrelease3.0.0.Thispresentationfocusesonourlatestmodelingeffortsthathavebeencarriedouttosupportthemicrowaveradiancedataassimilationovernon-snowlandsurfaces,whichincludesthemodelphysicsimprovementstoaccountforthethermalheterogeneityofthelandcoversandtheunderlyingsoil,theimplementationofthetangentlinearandadjointmodelsforsensitivityanalysisandvariationaldataassimilationoflandsurfaceskintemperature,soilmoistureandessentiallandcoverparameters.ThemodelvalidationandcalibrationatglobalscalewillbeparticularlyaddressedinreferencetothemonthlyaveragedemissivityretrievalatlasTELSEMandthereal-timeanalyticalemissvityretrievalfromGSI.Duetothelargedatadimensionalityatglobalscale,machinelearningisutilizedinthemodelparameteroptimizationandthequantificationofmodeluncertainties.Themodelimprovementsmaysignificantlyreducethemodelbiasrootedinthemodelbuilt-inparameters,e.g.,leafthicknessandthedominantinclinationangleofcanopyleaves.WiththecasestudiesatATMSchannels,comprehensiveanalysisontheimprovedmodelperformanceinGSIwillbedemonstratedinthepresentation.

1.09 Howitcouldbepossibletoevaluatethespectroscopicparameters:theexampleofthenewreleaseofGEISA-2019Presenter:RaymondArmante,LMD/CNRSAuthors:R.Armante,N.Scott,A.Chédin,L.CrepeauThelatestreleaseofGEISAin2015,includinglineparameters,cross-sectionsaswellasaerosols,hasbeendescribedinJacquinetetal[1].GEISAandassociatedmanagementsoftwarefacilitiesareimplementedandfreelyaccessibleontheAERIS/ESPRIatmosphericdatacenterwebsitegeisa.aeris-data.fr.Itisusedon-lineinvariousdomainslikeatmosphericphysics,planetology,astronomyandastrophysics.TheactualcontextofmanagementandcontentsofthenewreleaseofGEISA-2019versionareindependentlypresentedinaposterbyArmanteetal.WithmoreandmoresophisticatedinstrumentslikeIASI,IASI-NG,CrIs,OCO2,…andrequiringhigherspectralandradiometricperformances,theneedsintheprecisionofthespectroscopicparametersaremoreandmoreimportant.Today,theGEISAdatabasestaysthereferenceforcurrentorplannedTIR/NIRspacemissions,suchasforIASI,IASI-NG,MERLIN,Microcarb.BasedonastrongexperienceinCAL/VALactivitiesatLMD,wehavedevelopedavalidationchain,calledSPARTE[2],aimingtocomparethedifferencesbetweenresultsofmodelsimulationsandsatelliteobservationsremotedata.Thesimulationsaremadewiththeradiativetransferalgorithm4AOPdevelopedandvalidatedatLMD(seededicatedposterArmanteetal).Forthethermalinfrared,wehaveusedtherichnessoftheobservationdataprovidedbyspacebornsatelliteinstrumentsIASIA,BandC(2006,2012,2019).IntheNearInfraRed,wehaveusedallthepotentialofoneofthehighestresolvedinstrumentscalledTCCON.Forthefirsttime,SPARTEhasmadeitpossibletoevaluateGEISA-2015beforeitspublicdistributionviatheAERIS/ESPRIatmosphericchemistrydatacenterwebsite.Thenextversion(GEISA-2019)beingplannedformid-2019,wehaveinparallelappliedtheSPARTEchaintoassessthequalityofthenewreleaseofmaincontributors,invariousdomainsliketheR6manifoldofCH4(MERLIN),the2.1,1.6and0.76µmbandsforCO2,O2,H2O(MicroCarb,OCO-2),andthe1.27µmofO2(MicroCarb).ThispresentationwillbefocusonthemainresultswehaveobtainedintheevaluationofthespectroscopicparametersfortheIASI/IASI-NGspectralintervals.References:[1]N.Jacquinet-Husson,etal.J.Mol.Spectrosc.,327,31-72,http://dx.doi.org/10.1016/j.jms.2016.06.007(2016)[2]R.Armanteetal,J.Mol.Spectrosc.,327,180-192,http://dx.doi.org/10.1016/j.jms.2016.04.004(2016)1.10 AnalternativemethodtoquantifyNLTEradiancesPresenter:ZhenglongLiAuthors:ZhenglongLi,W.PaulMenzel,JamesJung,AgnesLim,andJunLiThe4.3umCO2shortwaveInfrared(SWIR)radiancesaremoresensitivetotemperaturethanthe15umCO2longwaveIR(LWIR)ones.However,noneoftheoperationalcentersisassimilatingtheSWIRradiancesfromhyperspectralIR(HIR)sounders,suchasAIRS,IASI,andCrIS.OneimportantreasonistheNon-LocalThermodynamicEquilibrium(NLTE)impactontheSWIRCO2channels,whichmaycontributemorethan10Kinobservedbrightnesstemperature(BT).Inrecentyears,significantprogresshasbeenmadeinfastradiativetransfer(RT)simulatingdaytimeNLTEemission.Despiteoftheoverallgoodagreementbetweenobservations(O)andcalculationsusingNWPfieldasbackground(B),studieshaveshownunexplainedlargediscrepanciesoverwinter-sidehighlatituderegion,inbothdaytimeandnighttime.ThisstudyintroducesanewalternativemethodtoquantifytheSWIRNLTEradiances,thedifferencesbetweentheobservedandtheNLTE-freeSWIRradiances,whichcanbeaccuratelypredictedfromLWIRonesduetochannelcorrelations.ThistechniqueisappliedtoprocesstheCrISfullspectralresolution(FSR)radiances,andthecomparisonsbetweenOandBusingECMWFanalysisprofilesarecarriedout.TheresultsshowthatthenewmethodcharacterizestheNLTEradianceswellwithcomparablestandarddeviations(STD)ofthedifferencesbetweenOandBasCRTMandNEdTduringthedaytime,andsmallerSTDsduringnight,indicatingnighttimeNLTEisnotnegligible.Thenewmethodalsoshowssmallerbiases(lessthan0.3Kinabsolutevalue)thanCRTMsimulation(mostlymorethan-1K).DetailedanalysisofthebiasesshowthatthenewmethodoverestimatestheNLTEby0.5to1.0KduetoCRTMLTEbiasintrainingprocess,andCRTMunderestimatesbothLTEandNLTEby0.5to1.0K.Thenew

alternativemethodcanbeusedtogetherwithRTNLTEsimulationtoimprovethequalityofSWIRradianceassimilation.Session1:RadiativeTransferandCommunitySoftware(posterintroductions)1p.10 EffectsofFieldofViewInhomegeneitiesinRadiativeTransferPresenter:XavierCalbetAuthors:XavierCalbetTheeffectsinradiativetransferofwatervaporinhomogeneitiesor,equivalently,turbulencewithintheFieldofView(FOV)ofmicrowaveandinfraredsoundersisexplored.Thiseffectisnotnegligibleandshouldbeaccountedforifconsistencybetweenmodellingandmeasurementsystemsistobeachieved.Thisphysicaleffectisfurtherexploitedandretrievalsoftemperature,watervaporprofilesandalsoturbulenceareexplored.Retrievalsofturbulenceataglobalscalecouldbeextremelyusefulinmanyapplicationareas.1p.11 CRTMInfraredSeaSurfaceEffective-Emissivity(IRSSE)ModelUpgradeStatusPresenter:NicholasNalli,IMSGInc.atNOAA/NESDIS/STARAuthors:NicholasR.Nalli,J.Jung,B.Johnson,T.Zhu,M.Chen,E.Liu,andL.ZhouForsatelliteIRremotesensingapplications,thesurfaceemissivity/reflectancespectrummustbespecifiedwithahighdegreeofabsoluteaccuracy;a0.5%uncertaintycanresultin?0.3–0.4KerrorinLWIRwindowchannels.Inthemid-2000stheJointCenterforSatelliteDataAssimilation(JCSDA)supportedthedevelopmentofanIReffective-emissivity(IRSSE)modelfortheCommunityRadiativeTransferModel(CRTM)inanefforttoobtainimprovedagreement(overconventionalemissivitymodels)withsurfacebasedradianceobservations(viz.,MAERIspectra)overtheusualrangeofsatellitezenithangles,IRwavelengths,andsurfacewindspeeds.However,althoughtherewasaknowndependenceonsurfacetemperature,itwasnotuntilrecentfindingsofLiuetal.(2017JCSDAWorkshop)thatasignificantsystematicbias(asmuchas1K)wasrevealedtooccuronaglobalscaleincoldwaters(i.e.,theNorthAtlanticandSouthernOceans).Thishasbroughtattentionbacktothisissue,whichhassinceledtoFY19-FY20JCSDAAOPsupportformodelupgradestoaddressthisproblem,inadditiontootherupgrades(e.g.,reductioninresidualbiasesintheSWIRband).ThispresentationwillprovideanoverviewtheCRTMIRSSEmodelalongwiththeupgradeplanandprogress.1p.12 EvaluationoftheRTTOVintheECMWFNWPsystemPresenter:CristinaLupu,ECMWFAuthors:CristinaLupu,AlanGeer,MarcoMatricardiThepostergivesanoverviewoftheevaluationoftheRTTOVradiativetransfermodelintheECMWFsystem.RTTOVhasbeenupdatedtoversion12.1intheIFSmodelcycle45r1(5June2018)andtoversion12.2intheoperationalmodelcycle46r1(11June2019).ThelatestNWP-SAFreleasedversion12.3hasbeenalsoevaluatedforinclusioninthenextIFSmodelcycle.TheseareabroadscientificandtechnicalupgradewhichallowsRTTOVtousethemostaccuratesciencepossibleandpreparesthewayforfuturesensors(e.g.,bandcorrectionsareimplementedforallmicrowavesensorsimprovingaccuracyofsimulatedmicrowaveradiances;thescatteringradiativetransferpackagedoesitsradiativetransferintermsofradianceimprovingaccuracybyseveraltenthsofaKelvininsomechannels;simulationsforinfraredsensorswithupdatedconcentrationsofCO2tocurrentvaluesinthemixedgastransmissionsandadifferenttrainingsetofdiverseatmosphericprofilesareunderway).AnoverviewoftheperformanceintheIFSwillbepresentedalongwithalookaheadtofutureevaluationofplannedRTTOVdevelopments(e.g.,newopticaldepthpredictors).1p.13 RTTOVforhyperspectralfarinfrared(FIR)instruments:theFORUMexamplePresenter:PascalBrunel,Meteo-France(forJeromeVidot)Authors:JeromeVidot,PascalBrunel,JamesHocking,MarcoMatricardiandRogerSaundersThefastradiativetranfermodelRTTOVisdevelopedintheframeoftheEUMETSATNWP-SAFprojectfortheassimilationofsatelliteobservationsinNWPmodels.RTTOVisalsomoreandmoreusedforsatelliteretrievalsaswellasforpredictedsatelliteimagery.Intheinfrared,RTTOViscurrentlyabletosimulatemulti-spectralorhyperspectralinstrumentsbetween3.3and50microns.However,therearescientificintereststoextendthecapabilityofRTTOVinthefarinfrared(FIR)upto100microns.ThisisavaluablechallengefortheRTTOVteamandwepresenthereitsapplicationfortheFar-infraredOutgoingRadiationUnderstandingandMonitoring(FORUM)hyperspectralinstrument.FORUMwillmeasureinthe100-1600cm-1(6.25–100micron)rangeatanexpectedspectralresolutionof0.3cm-1.Butthisextensionisnotstraightforwardandasthisextensioncover

differenttopicstheywillallneedparticularattention.Thefirsttopicisrelatedtotheatmospherictransmittancecalculationwhichisbasedonpredictorsandcoefficients.TheRTTOVcoefficientsaretrainedfromline-by-linesimulationswithLBLRTMknowingtheinstrumentspectralresponsefunction.SinceLBLRTMcoverthespectralregionbetween50and100microns,theRTTOVcoefficientsforFORUMwerecalculatedandtheaccuracycomparedtocurrenthyperspectralIRsounders.WealsoshowthecapabilityofRTTOVtosimulatecloudyradiancesandJacobiansbyextendingthecurrentcloudopticalpropertiesandsurfaceemissivitymodelstoFIR.1p.14 DevelopmentofanactivesensormodulefortheRTTOV-SCATTradiativetransfersimulatorPresenter:PhilippeChambon,Météo-FranceAuthors:PhilippeChambon,AlanGeerActivemicrowavesensorsarebecomingwidelyusedobservationswithintheNumericalWeatherPredictioncommunity,eitherforvalidatingmodelforecastsorforassimilationpurposes.Likefortheforwardsimulationofpassivemicrowaveobservations,radardatasimulationsrequiretomakeassumptionsonthescatteringpropertiesofhydrometeors.Withtheobjectiveofsimulatingbothactiveandpassivemicrowaveinstrumentswithinasingleframeworkusingthesameradiativetransferassumptionsintoawidely-usedtoolintheNWPcommunity,anactivesensormoduleiscurrentlyunderdevelopmentwithintheRTTOV-SCATTsoftware.ThefirstsimulationsoftheGPM/DPRinstrumentaswellastheCloudsat/CPRinstrumentwiththissimulatorwillbeshown,basedontheAROMEmodelrunningoperationallyatMétéo-FranceoverfivedomainsintheTropics.Inparticular,somemodelbiaseshighlightedwiththesefirstcomparisonswillbediscussed.1p.15 ProgresstowardsaPolarizedCRTMPresenter:BenjaminJohnson,JCSDA(forPatrickStegmann)Authors:PatrickStegmann,BenjaminJohnson,andTomGreenwaldInthispresentationwesummarizetheprogressinthedevelopmentofapolarizedCRTMversionforthereleaseREL-3.0.TheCRTMisafastandaccuratescalarradiativetransfermodel[1]specif-icallydevelopedforsatelliteradiancedataassimilationinnumericalweathermodels.Forthispur-pose,theCRTMincludesspecifictangent-linear,adjoint,andJacobian(K-matrix)functionsinadditiontothebaselineforwardmodel.InordertoextendtheCRTMtocomputeasubsetorallelementsoftheStokesvectortwonewradiativetransfermodelscurrentlystandincompetition.ThefirstmodelisastraightforwardextensionofthecurrentdefaultscalarAdvancedDoubling-AddingmodeloftheCRTM[2]developedbyDr.QuanhuaLiuandthesecondmodelisaSmall-AngleApproximationcode[3]developedatTexasA&MUniversity.OtherissuestoextendtheCRTMtowardspolarizedradiationarethecomputationofMüllermatri-cesforthehydrometeorandaerosolscatteringpropertiesandtheprovisionofnewpolarizedsur-faceemissivities.References[1]Ding,S.,P.Yang,F.Weng,Q.Liu,Y.Han,P.VanDelst,J.Li,andB.Baum,2011:Validationofthecommunityradiativetransfermodel.J.Quant.Sp.Rad.Trans.112,1050-1064.[2]Liu,Q.,andC.Cao,2019:AnalyticexpressionsoftheTransmission,Reflection,andsourcefunctionforthecommunityradiativetransfermodel.J.Quant.Sp.Rad.Trans.226,115-126.[3]Sun,B.,andG.W.Kattawar,2017:AnImprovedSmall-AngleApproximationforForwardScatteringandItsUseinaFastTwo-ComponentRadiativeTransferMethod.J.Atm.Sci.74,1959-1987.1p.16 The4A/OPmodelfromNIRtoTIR:newdevelopmentsandvalidationresultswithintheframeofinternationalspacemissionsPresenter:RaymondArmante,LMD/CNRSAuthors:R.Armante,V.Capelle,N.A.Scott,A.Chédin,E.Jaumouillé,P.Lafrique,D.Jouglet,C.Pierangelo,BojanSic,MahmoudElHajj,L.Chaumat,E.Durand,N.MeilhacetJ.ZeghoudiTheAutomatizedAtmosphericAbsorptionAtlas(4A)istheLMD(LaboratoiredeMétéorologieDynamique)fastandaccurateline-by-lineradiativetransfermodelforthecomputationoftransmittances,radiancesandJacobians(http://ara.abct.lmd.polytechnique.fr/index.php?page=4a),currentlycoveringtheNearIRtotheThermalIRspectraldomain.Ithasbeendeveloped,improvedandvalidatedbyLMD,CNESandNOVELTIS.Regularlyupdatedorextendedtoprocessmoreandmorespectrallyresolvedcloudfreeobservationsaswellasobservationsinvolvingscatteringeffectsbycloudsoraerosols(throughDISORT,LIDORTorVLIDORT),the“operational”version4A/OPisfreelydistributedtoregisteredusers.

4A/OPiscurrentlyadoptedbyresearchandoperationalgroupsinvolvedinforwardandinverseradiativetransferproblemsforsimulationscoveringawidespectralrange[20µm–0.75µm].ExtensiontoUV/Visisplannedforanextversion.Inparticular,4A/OPisthereferencemodelusedbyCNESforseveralin-flight(IASI/MetOp)orplannedspacemissions(IASI-NG,MicroCarbandMERLIN).Withintheframeofresearchandoperationalapproaches,newdevelopmentsandvalidationstudieshavebeenperformed.Thisposterwilldescribeanddiscussrecentlyimplementedadditionalorupdatedcapabilitiesas:

• Theimpactoftheuseofthelast(2019)updatedversionofthespectroscopicdatabaseGEISAhttp://ara.abct.lmd.polytechnique.fr/index.php?page=geisa-2

• TheimprovementofthemodellingoftheH2O,N2andO2continua.• Thenewdevelopments(astheLSImethod)madetosignificantlyreducethecomputationtimeinthe

scatteringmodewithmasteredandlimitedimpactontheaccuracy.• Thedevelopmentofamoreflexible4A/OP-Userinterfaceofferingawiderchoiceofinput/output

possibilities(infiniteresolutionversusISRFconvolvedJacobians,selectionoftheemissivitydatabase,wavenumber/wavelengthunits,…).

• Adescriptionofthevalidationstudies(approach,results)madeonasemi-operationalbasis,andbasedontime/spacecolocationsbetweentheARSAdatabaseand/orECMWFanalysesandIASIintheinfrared(includingMetOpA,BandC)aswellasinthenear-infraredusingTCCONstations/observations.

1p.17 StatusofthenewGEISA-2019spectroscopicdatabasePresenter:RaymondArmante,LMD/CNRSAuthors:R.Armante,A.Perrin,N.Jacquinet,N.Scott,A.Chédin,L.CrepeauTheaccuracyofmolecularspectroscopyinatmosphericresearchhasenteredinanewphaseintheframeofremotesensingapplications(meteorology,climatology,chemistry)withtheadventofhighlysophisticatedandresolvedinstrumentations.ThelatestreleaseofGEISAin2015,includinglineparameters,cross-sectionsaswellasaerosols,hasbeendescribedinJacquinetetal[1].Forthefirsttime,thecorrespondinglineparameterssub-databasehasbeenintensivelyvalidatedusingthepowerfulapproachoftheSPARTEchain[2]developedatLMD.Thischainhadanimportantimpact,particularlyisthereleaseofmoleculesasH2O,CO2andCH4.GEISAandassociatedmanagementsoftwarefacilitiesareimplementedandfreelyaccessibleontheAERIS/ESPRIatmosphericdatacenterwebsitegeisa.aeris-data.fr.Itisusedon-lineinvariousdomainslikeatmosphericphysics,planetology,astronomy,astrophysic.Today,theGEISAdatabaseisthereferenceforcurrentorplannedThermalIR/NearIRspacemissions,suchasforIASI,IASI-NG,MERLIN,Microcarb.Wehavenowinitiatedthenextreleaseplannedforthemid2019.Onthisposter,wewillpresentthestatusofthisnewGEISA2019release.ExamplesofvalidationswehavealreadymadeformajormoleculessuchasH2O,CO2andCH4willbepresentedinadedicatedposter/presentation.EspeciallyneededbythespatialagencieslikeCNES,itisimportanttoestimatewhichcouldbetheprecisionofthespectroscopicparameters(foragiveninstrument)toreachitsscientificobjectives.ApartofthisposterwillbereservedtoshowhowatLMDwehavetriedtoanswertothisquestion.References:[1]N.Jacquinet-Husson,etal.J.Mol.Spectrosc.,327,31-72,http://dx.doi.org/10.1016/j.jms.2016.06.007(2016)[2]R.Armanteetal,J.Mol.Spectrosc.,327,180-192,http://dx.doi.org/10.1016/j.jms.2016.04.004(2016)1p.18 ComparisonofTwoDataResamplingAlgorithmsforProcessingofMicrowaveSoundingObservationsPresenter:HuYang,UniversityofMarylandAuthors:HuYangFormicrowavesoundinginstrument,dataresamplingprocessingcanbeusedtoreducethespatialresolutiondifferencebetweenobservationsfromdifferentchannelsanddifferentinstrument,andgenerateonesingle

datastreamforNWPapplications.Whichisimportantforcorrectlyclassifyingcloud-affecteddataindataassimilation.Currently,therearetwodifferentdataresamplingalgorithmsbeingdevelopedformicrowaveinstrument,oneisbasedonantennapatternB-Goptimumcostfunctionmethod,whichhasbeenusedtogenerateATMSremappingSDRdataset,anotherisbasedonModulationTransferFunction,whichisadoptedbyEUMETSATAAPPpackage.Inthiswork,wewillevaluatetheimpactoftworemappingalgorithmsonSDRdataqualitybasedonthesub-pixelhighresolutionsimulatedatasets,forbothresolutiondowngradeandenhancementcases.ResultsofthestudyareexpectedtobeusefulforNWPusercommunitytofullyexplorethebenefitofmicrowavesoundinginstrumentobservations.1p.19 AutomatedIdentificationofAnomalousSSMISBrightnessTemperaturesUsingaNeuralNetworkPresenter:EricSimon,UCAR/NRL-MontereyAuthors:EricSimon,SteveSwadleySpuriousanomaliesandradiofrequencyinterferenceappearwithsomeregularityintheSSMISK-bandbrightnesstemperaturedata.Variousimagesofknownincidentsshowthatsomeinvolvejustoneorafewisolatedscenes,someappearasnarrowstreaksspanningseveralscans,andothersarelargerclustersthatappearregularlyinspecificgeographicareas.Radiativetransfermodelscanbeusedtofindsceneswherebrightnesstemperaturesdifferfromabackgroundmodel,butitwouldbecomputationallyprohibitivetoemploythisonalonghistoryofdata.Instead,weidentifiedpossibleincidentsusingobservedbrightnesstemperatureswhichareclassifiedasexcessivelyhighorlow,aswellasabnormallylargedeparturesfromsurroundingscenes.Thistechniquefoundmorethan16,000suspectedincidentsfrom2004-2018.Uponreview,manyoftheseweretheresultoftemporaryinstrumentcalibrationissueswhichwedidnotwanttofocuson,yetfilteringtheseoutusingconventionalmethodsprovedchallenging.SotoidentifytheincidentsaConvolutionalNeuralNetworkwastrainedonarandomsubsetofmanuallylabeledincidents,whichhadsomeinitialsuccess.Nextbyusinguncertaintysampling,ortakingtheincidentsofwhichtheneuralnetworkwastheleastcertain,theneuralnetworkwasfurthertrained,resultinginamuchimprovedperformance.Reapplyingthismodeltothe16,000incidentsthemodelidentified12,000incidentsnotrelatedtocalibrationissues.Toexplaintheanomalousincidents,avarietyofpossibletrendsorcorrelationswereconsidered,includingtimeofday,seasonoftheyear,satellitenumber,multi-yeartrend,lookangle,andgeographicregion.Whilemanyoftheseprovedinsignificant,nearlyallincidentsexhibitlookangledependency.Additionallysomelocationshadamulti-yeartrend,orpersistedformultipleyearsbutnotoverthewholerecord.Analysisbasedonlookangleandmulti-yeartrendoffersevidenceforpossiblecausesofandrelationshipsbetweensomeincidents,butalsoraisesadditionalquestions.Insightstopotentialcauseswillbepresentedanddiscussedinthisstudyaswellastheapplicabilitytoothersensorsandplatforms.Session2:Calibration,Validation(oralpresentations)2.01 IASIon-boardMETOP-C:instrumentstatusL1calibration/validationresultsPresenter:LauraLeBarbier,CNESAuthors:LauraLeBarbier,JordiChinaud,ElsaJacquette,ClaireMaraldi,LaurenceBuffet,OlivierVandermarcq,ClémencePierangelo,AntoinePenquer,RémiBraun,BernardTournier,AnaisVincensini,OcéaneLasserre,YannickKangah,BernardDelatteIASI(InfraredAtmosphericSoundingInterferometer)isakeypayloadinstrumenton-boardMETOPsatellites.IthasbeendevelopedbyCNESwithThalesAleniaSpaceasindustrialprimecontractor,intheframeworkofacooperationagreementwithEUMETSAT.ThreeidenticalIASIflightmodelswerebuilt.Thefirsttwoones,launchedon-boardMetop-AandMetop-Brespectivelyin2006and2012,havebeenfullyoperational.Thelastinstrument,waslaunchedon-boardMetop-Cthe7thofNovember2018fromKourou.TheL1Calibration/Validation(Cal/Val)activitiesconsistmainlyinthecharacterizationoftheinstrumentperformanceandthetuningoflevel0andlevel1processingparameters.IASI-CL1Cal/ValwasperformedfromDecember2018toJune2019.ItinvolvedtheIASICenterofExpertiseinCNESToulousewiththesupportofEUMETSATfortheoperations.ThefulldisseminationofL1productsbyEUMETSATdedicatedtonumericalweatherprediction,atmosphericchemistrymonitoringandclimatestudies,startedthen.Inthispresentation,wewillgiveastatusofIASI-CinstrumentandillustratethehighlevelofqualityofL1datareachedattheendoftheCal/Valintheradiometric,spectralandgeometricdomains.

2.02 EUMETSATActivitiesforIASI-CCommissioningPresenter:DorothéeCoppens,EUMETSAT(forStephanieGuedj)Authors:StéphanieGuedj,MayteVasquezandDorothéeCoppensTheInfraredAtmosphericSoundingInterferometeronboardMetopC(IASI-C)delivereditsfirstspectrumonthe12/12/2019.WithIASIbeingfullyactivatedandingoodhealth,thecalibrationandvalidation(Cal/Val)phases,alsocalledthecommissioningphasehasbeenstarted.EUMETSATisworkingtogetherwiththeCNES/TECtoprovide1)adetailedperformancecharacterisationwithregardtothemissionrequirements,2)anevaluation/validationofthemeasurementwithindependentinstrumentsand3)atuningoftheonboard/grounddataprocessingparameters.Aspartofthecooperation,EUMETSATisgeneratingdailyreportstoinsuretheoperationalmonitoringofIASI-Clevel0andlevel1products.Itincludesawiderangeofinformationsuchastheinstrumentmode,dataqualityflagsandgaps,housekeepingdataandprocessingparametersrelatedtospectral,radiometricandgeometriccalibration.Mostofanomaliesorchangesalongthefullprocessingchainisreflectedandcharacterizedinthesereports.ResultsarepermanentlydiscussedwithCNES/TECandaselectionispresentedintheposter.Inaddition,EUMETSATisprovidingrigorousstatisticsoninter-pixelscomparison(intermofNeDT),andontheso-calledOBSminusCALC.Inthelatter,OBServedradiancesarecomparedwithCALCulatedradiancesthatusetheRTTOVradiativetransfermodelandECMWFatmosphericprofilesasinput.Moreover,toolshavebeendevelopedtocomputeradiancesmassiveaverageallowingthecomparisonbetweenIASI-A,-B,-CandotherindependentinstrumentssuchasCrIS,HIRASorevenAVHRR.AspartoftheCal/Val,EUMETSATisalsoanalysingsomevaluablefeedbackfromaselectionofuserpartners(ECMWF,Météo-France,Met-Office,LMD,NOAA…)thatwillbenefitfromtheEarlyDisseminationofIASI-C,probablyaroundmid-March2019.ThefullyoperationaldisseminationofIASI-CdatawillstartattheendoftheCal/Valactivities,mostlikelyaroundJune2019.2.03 StudiesoftheCrISNoiseandCalibrationCovariancesPresenter:DavidTobin,CIMSS/SSEC/UW-MadisonAuthors:DavidTobin,JoeTaylorThisposterwillshowresultsofinvestigationsofCrISobservationcovariance.ThiswillincludetheCrISsensornoiseanditscovariancebasedonanalysisoflargeensemblesofinternalcalibrationblackbodyanddeepspaceviewdata,includingtheeffectsofthecalibrationprocessinganduserapodization.ItwillalsoincludevariouscontributionstotheCrIScalibrationuncertaintyanditsassociatedcovariancecomputedforarangeofatmosphericconditions.These(relativelysmall)contributionsto"observation"errorandcovariancewillbecomparedtonoiseandcovarianceestimatescomputedfromlargeensemblesofcolocatedclearskyCrISobservationsandclearskycalculatedspectra,withagoalofestimatingtheadditionalcontributionsduetotheRTmodel,undetectedclouds,andmodelrepresentation.2.04 FY-3DHIRASRadiometricCalibrationandAccuracyAssessmentPresenter:ChunqiangWu,CMAAuthors:ChunqiangWu,ChengliQi,XiuqingHu,MingjianGu,TianhangYang,ZhongdongYangandPengZhangTheHigh-spectralInfraredAtmosphericSounder(HIRAS)isaFouriertransformspectrometeronboardthefourthPolar-orbitingFengYun3satellite(FY-3D).TheFY-3DHIRASprovidesinterferogrammeasurementsofEarthviewradiancespectrainthreeinfraredspectralbandsat29cross-trackpositions,eachwitha2×2arrayofFieldOfViews(FOVs).TheHIRASlevel1radiancedatacoversthespectralbandsfrom650cm-1to1135cm-1(Long-waveBand,LW),1210cm-1to1750cm-1(Mid-waveBand,MW),and2155cm-1to2550cm-1(Short-waveBand,SW)withaspectralresolutionof0.625cm-1.Theradiometriccalibrationalgorithmandthemethodsofrefiningthenonlinearityandthepolarizationcorrectioncoefficientsonorbitaresummarizedinthispaper.Thenonlinearitycorrectioncoefficientsarederivedbyminimizingthespreadoftheresponsivityfunctionsderivedfromthemeasurementsoftheinternalcalibrationtargetwithvaryingtemperatures.ThepolarizationcorrectioncoefficientsarederivedfromthecoldspaceobservationsandtheroutineEarthscenemeasurements.TheradiometricaccuracyisassessedbycomparingtheHIRASmeasurementstothecollocatedCross-trackInfraredSounder(CrIS)observationsandradiancesimulations.Theresultsshowthat,comparedtoCrIS,theradiometricdifferencesareabout0.3Kand0.7KfortheLWandMWbands,respectively,and0.5K

fortheCOabsorptionandwindowregionsintheSWband.TheconsistencyoftheradiometriccalibrationamongthefourFOVsisestimatedtobewithin0.2Kformostofthespectraldomain.SomeremainingissuesfortheFY-3DHIRASarealsodiscussed.2.05 CheckingBeamPerformanceofHIRSandMHSWiththeMoonPresenter:MartinBurgdorf,UniversitätHamburgAuthors:MartinJ.Burgdorf,ThomasG.Müller,MarcPrange,andStefanA.BuehlerThetimeneededbytheMoontomovethroughthedeepspaceviewofamicrowavesounderdependsonitsbeamsize.WeanalysedseveralMoonintrusionsfromdifferentinstrumentsandcalculatedtheFWHMoftheirbeamsforallchannelswithanaccuracyof0.02degrees.Significantdiscrepanciesofupto20%werefoundtothevaluesinreportsfromgroundtests.WedeterminedalsothebrightnesstemperatureoftheMoonineachchannelandbycombiningthesevalueswiththebeamsizes,wecouldputconstraintsonthebeamefficiencies.Againwefounddisagreements,inthiscaseofupto4%,betweenourmeasurementsandtheresultsobtainedonground.WecomparethebrightnesstemperaturesoftheMoonbetween89and190GHz,calculatedwithournew,improvedvaluesforthebeamperformance,withthepredictionsbyawidelyusedthermophysicalmodelanddemonstratethatitdoesnotreproducecorrectlythedifferencebetweenwaxingandwaningMoon.AsimilarinvestigationwascarriedoutwithappearancesoftheMooninthedeepspaceviewofHIRS/2,/3,and/4onvariousplatforms.Differentvaluesforthediametersofthefield-of-viewoftheseinstrumentscanbefoundindifferentdocuments,andwecouldruleoutsomeofthemonthebasisofthebrightnesstemperaturesoftheMoontheywouldimply.Thesizeofthefield-of-viewofHIRS/4differsamongallsatellitesbylessthan2percentforchannelsoperatinginthethermalinfrared.BecauseofthehighaccuracyofthebrightnesstemperatureoftheMoonthatisachievablewithmeteorologicalresearchsatellitesinthethermalinfraredandwithmicrowaves,thesemeasurementscanprovideuniqueconstraintsonmodelsofthebulkcompositionofitsregolith.Session2:Calibration,Validation(posterintroductions)2p.01 HIRASon-orbitperformanceandfuturedevelopmentPresenter:ChengliQi,NationalSatelliteMeteorologicalCenterAuthors:ChengliQiHighspectralInfraredAtmosphericSounder(HIRAS)isaFourierTransformMichelsoninterferometerinstrument,whichisthefirsthyperspectralsounderinaseriesofChineseFengYun3(FY-3)polarorbitmeteorologicalsatelliteandwaslaunchedon15November2017.HIRASprovidesinfrared(IR)measurementsofradiancespectrainthreespectralbands:thelongwaveIR(LWIR)bandfrom650to1136cm-1,middlewaveIR(MWIR)bandfrom1210to1750cm-1,andshortwaveIR(SWIR)bandfrom2155to2550cm-1,withspectralresolutionof0.625cm-1.Thereare29stepsscanobservationsineachscanlineandwith2×2fieldofviewsineachobservation.HIRASwasinoperationalstatusatJan,2019.ForoperationalL1products,theradiancenoiselevelsmeetthespecifications,theabsolutespectralfrequencybiasesarelessthan3ppmforallthethreebands,andspectralbiasstandarddeviationsarelessthan3ppmintheLWIRandMWIRbandsandareabout3~5ppmintheSWIRband.TheradiometriccalibrationuncertaintieswereassessedbythecomparisonsoftheradiancespectrabetweenHIRASandotherIRhyper-spectralsensorsondifferentsatellites.Theradiancedifferencesofthecross-sensorcomparisonsareingenerallessthan0.3,0.7and1.0KintheLWIR,MWIRandSWIRbands,respectively.HIRASisprovidingawealthofdataofhighaccuracyandresolutiononatmospherictemperatureandhumiditywithwhichtoimproveweatherprediction,andalsoonvariouscomponentsoftheatmospheretofurtherourunderstandingofatmosphericprocessesandtheinteractionsbetweenatmosphericchemistry,pollutionandclimate.ThethirdbatchinFY-3seriesconsistsoffoursatellitesandHIRASwillflyonthreeofthem,inwhichthefirstisFY-3Eonanearly-morningorbitwithlocaltimeofdescendingnodeisaround6:00A.Mandplantolaunchon2020.Theresultsfromtheorbitsimulationandtheobservingsystemexperiments(OSE)indicatethattheearly-morningorbitsatellitetogetherwiththemorningorbitandtheafternoonorbitsatellitescanprovidetheinitialmeteorologicalfieldforthenumericalweatherprediction(NWP)modelwithoutanyblankleftontheglobalscaleevery6hourssothattheforecastperiodandtheforecastaccuracycanbeimprovedforboththe

hemisphericandtheregionalscales.InthenewbatchHIRASwillupgradetoHIRAS-II,withimprovementsindetectorsfrom2×2changeto3×3,andtheNEdTaswellascalibrationaccuracyspecificationsaremuchimproved.2p.02 RadiometricandspectralintercomparisonofIASI-CwithotherinfraredsoundersPresenter:JordiChinaud,CNESAuthors:JordiChinaud,LauraLeBarbier,ClaireMaraldi,LaurenceBuffet,AntoinePenquer,BernardDelatte,Jean-ChristopheCalvel,ClaireBaqué,BernardTournier,YannickKangah,RémiBraun,AnaïsVincensini,OlivierVandermarcq,StéphanieGuedj,DorothéeCoppensIASI(InfraredAtmosphericSoundingInterferometer)hasbeendevelopedbyCNESwithThalesAleniaSpaceasindustrialprimecontractor.ItisakeypayloadinstrumentonMetopsatellites,aseriesofthreepolarorbitingmeteorologicalsatelliteswhichformthespacesegmentcomponentoftheoverallEUMETSATPolarSystem.IASI-AandIASI-Bhavebeenoperationalforyears.Thethirdandlastin-flightmodel,IASI-C,waslaunchedonboardMetop-Cthe7thofNovember2018fromKourou.IASI-CCalibration/Validation(CalVal)activitiesmainlytookplaceattheIASICenterofExpertiseinCNESToulousefromDecember2018toJune2019.AttheendoftheCalVal,IASI-Cspectrawillofficiallybedeliveredtousersworldwidefornumericalweatherprediction,atmosphericchemistrymonitoringandclimatestudies.WewilldetailhowIASI-Cradiometricandspectralperformanceshavebeenvalidatedagainstothersounders.Inparticular,theradiometricintercomparisonofIASI-CwithIASI-A,IASI-B,AIRSandCrIS-N20usingcommonobservations,massiveaveragingofspectra,ordoubledifferenceswillbedetailed.ItdemonstratestheverygoodcalibrationofIASI-Cdata,andconfirmsthefactthatIASIsoundersareareferenceintheinfrareddomain.2p.03 LatestImprovementsforCrISSensorDataRecordsPresenter:YongChenAuthors:YongChen,DenisTremblay,FlavioIturbide-Sanchez,JoeTaylor,XinJin,MarkEsplin,andDaveTobinTheCross-trackInfraredSounder(CrIS)onboardtheSuomiNationalPolar-OrbitingPartnership(S-NPP)SatelliteandNOAA-20haveprovidedthehyperspectralinfraredobservationsforprofilingatmospherictemperature,moistureandgreenhousegasesglobally.CrISisawellcalibratedinstrumentthroughitsexcellentinstrumentdesign,well-conductedpre-launchandpost-launchvalidations.TheexcellentperformancesofCrISincludenoisebelowspecification,highspectralandradiometricaccuracy,highgeolocationaccuracy,aswellaslong-termstability.PreviousstudiesdemonstratedthattheCrISSensorDataRecords(SDRs)datanotonlymeetcalibrationrequirements,makingitanexceptionalassetforweatherapplications,butalsoverystableforclimateapplications.CrISSDRsforS-NPPandNOAA-20weredeclaredtothevalidatedstatusonFebruary20,2014andOctober2,2018,respectively.TheoperationalCrISSDRdataqualityiscontinuouslybeingmonitoredandimproved.Importantalgorithmimprovementshavebeencarriedoutrecently,includingtheoptimizationofthespikedetectionandcorrectionalgorithm(operationalimplementedonOctober3,2018),optimizationofthelunarintrusiondetectionalgorithm(operationalimplementedonDecember17,2018),andtheimplementationofthepolarizationcorrectionalgorithm,whichcurrentlyisinevaluationstage(plannedoperationalimplementationinlater2019).Inthisstudy,theimprovementsintheCrISSDRdataqualitywillbepresented.Thespecificareasofimprovementtobecoveredinthisworkareasfollows.1)Thespikedetectionandcorrectionalgorithm,whichdetectsandcorrectstheinterferogramshitbythehighenergyparticles,andreducethedistortedearthviewradiancespectraespeciallyintheSouthAtlanticAnomaly(SAA)region.2)Thenewlunarintrusion(LI)detectionalgorithm,whichhasthemajorpurposetoremovethedeepspace(DS)spectracontaminatedbylunarcontributioninthecalibrationDSslidingwindow,andasaresulttoimprovethequalityoftheESradiancesduringlunarintrusionevents.First,thenewalgorithmefficientlyfindsacontamination-freeDSspectrumintheDS30-scancalibrationmovingwindowtouseasthereferencespectrum.Second,basedonthephasecharacteristicsofthecomplexrawDSspectraduringLIevents,thelunarintrusionband-dependentthresholdswerederivedtoeffectivelyrejectthecontaminatedDSspectraandmakethevalidDSwindowsizeconsistentamongtheCrISthreebands.3)Therecentlydevelopedpolarizationcorrectionalgorithm,whichisforcorrectingthecalibrationbiasduetotheinstrumentpolarizationeffectfortheearthradiances.Evaluationresultsshowthatpolarizationcorrectionslightlyreducesthebrightnesstemperaturedifferencebetweenobservationandsimulationinnumericalweatherpredictionmodels,makes

thebrightnesstemperaturedifferencemoresymmetricaroundnadirFORsthanwithoutthecorrection.TheseimprovementsintheESradianceswillhavepositiveimpactstothedownstreamusersforprofilingglobalatmospherictemperature,moisture,greenhousegasesaswellasmonitoringtheclimatetrending.2p.04 ImplementationofaPolarizationCorrectionfortheCross-trackInfraredSounder(CrIS)SensorPresenter:JoeTaylor,UW-SSECAuthors:JoeK.Taylor,HenryE.Revercomb,DavidC.Tobin,RobertO.Knuteson,MichelleL.Feltz,GraemeMartin,YongChen,FlavioIturbideThepotentialforpolarizationerrorscontributingsignificantlytotheuncertaintybudgetofinfraredremotesoundingsensorshasbeenwellrecognizedanddocumented,particularlyduetopolarizationdependentsceneselectmirrorsandgratingbasedinstruments.TheissueisequallyapplicabletoFTSbasedsensors.Rotationofasceneselectmirroristypicallyusedtodirectcalibrationorsceneradianceintoaremotesensinginstrument.TheCrISsensorutilizesa“barrel-roll”sceneselectmirrorthatrotatesaboutanaxisthatis45°fromthemirrornormal,preservingtheangleofincidenceatthemirrorandopticalaxisforallcalibrationandsceneviews.Itiswellknownthatthereflectiononaninclinedsurfacewillalwaysinducesomepolarization.Asafirstordereffect,thepolarizationinducedbythe45°sceneselectmirrorwillbeconstantforallsceneselectmirrorrotationangles,sincetheincidentangleatthescenemirrorisconstantregardlessofrotationangle.Secondly,thepolarizationinducedbytheothercomponentsintheinstrumentopticalchain,includingtheinterferometerandaft-optics,isnotdependentonthepositionofthe45°scenemirror,andisconstantforallscenemirrorpositions.However,theplaneofreflectionatthescenemirrorrotateswiththescenemirrorrotation,anditisreasonabletoassumethattheinstrumentitselfwillhavepolarizationsensitivity.Asaresult,itcanbeassumedthattherotationofthereflectionplaneatthescenemirrorwillcreateamodulationofthesignalmeasuredatthedetector.Earlyanalysis,whichonlyincludedtransmissioneffects,priortothelaunchofSNPPCrISindicatedthatthiswasnotexpectedtobeasignificanteffectforCrIS.However,whenthepolarizedemissionfromthescenemirrorisincludedintheanalysis,theeffectbecomesnon-negligibleforcoldscenesandacorrectioniswarranted.ApolarizationcorrectionwillbeaddedtoboththeNOAAandNASACrISprocessingin2019.ThemodelforthepolarizationinducedcalibrationbiasandtheassociatedcorrectionispresentedfortheCrISinstrument,alongwithdetailsofthemodelparameterdetermination,andtheimpactofthecorrectiononthecalibratedradiancesforarangeofscenetemperaturesandtypes.2p.05 ProgressoftheMetop-CAMSU-ALunarContaminationCorrectionAlgorithmatNOAA/STARPresenter:JunyeChen,GSTAuthors:JunyeChenandBanghuaYanTheEuropeanMetOp-Csatellite,launchedonNovember7th,2018,carriesthelastoneoftheAdvancedMicrowaveSoundingUnit(AMSU-A)instrumentson-boardaseriesofPolarOrbitingEnvironmentalSatellites(POES),includingNOAA-15,16,17,18,19,MetOp-A,B,C.NOAA/STARundertakesthemajorcal/valworkfortheUSinstrumentson-boardMetOp-C,includingAMSU-A.TheLunarContaminationCorrectionisoneofthemostchallengingtasksintheMetOp-CAMSU-Acal/valactivities.Originally,theAMSU-ALunarContaminationCorrectionalgorithmwasdevelopedbyKigawaandMo(2002),andwasimplementedintheNOAAAMSU-AL1-Boperationsystem.AsacalibrationeffortfortheMetOp-CAMSU-A,thelunarcontaminationcorrectionalgorithmhasbeenrevisitedandadvanced.Inthispresentation,wewillcomprehensivelyreviewtheLunarContaminationCorrectionprocess,thealgorithmimprovement,andthepre-launchandpostlaunchcoefficientsestimation.EmphasiswillbeputonthederivationoftheLunarcoefficientsinpre-launchphasebasedonantennapatterndataandinpost-launchphasebasedontimeseriesofdeepspacecoldcountswhenlunarcontaminationhappens.CorrespondingvalidationandcomparisonoftheLunarContaminationCorrectionresultsbetweenthosebasedonthepre-launchcoefficientsandthepost-launchcoefficientswillillustratethebigimprovementfromthepost-launchcalibrationwork.

2p.06 Thecommonre-calibrationtechnologyforlong-termFY-3microwavesoundingdataPresenter:GuSongyan,NationalSatelliteMeteorologyCenter,CMAAuthors:GUSongyan,WangZhenzhan,ZhangShengwei,GuoYang,HeJieyingThemicrowaveatmosphericsoundingpayloadsofFengyun3seriessatellitehavebeengaveus11yearsdatabase.Thecommontechnologyofmicrowavesoundinghistorydatare-calibrationwillbreakthroughthekeytechnologiessuchascompositeanalysisofmicrowavepayloadchanneldecay,multi-payloadspace-timeandspectrummatching,thechangingtrendofcalibrationparametersanditsphysicalmechanismofresponse,andthefinere-calibrationofmicrowavehistoricaldataoflongtimeseriessatellites.Wewillbreakthroughthecouplinganalysisofmicrowaveloadon-orbitantenna-feedsystemandthereconstructiontechnologyofradiometersystem'snon-linearresponse,establishingthefull-linkanddynamicresponsemodelofmicrowaveradiometeron-orbitandtheevolutionmodelofon-orbittime-varyingcharacteristics,developingthereferencetransfermodelbasedoncosmicbackgroundmicrowaveradiation,andrealizingmulti-satelliteradiationreferencewithSNOTechnologyBasedonthereanalysisdata.2p.07 StatusofS-NPP/CrISSDRProductAftertheLossoftheMWIRBandPresenter:YongChen,GlobalScience&Technology,Inc,NOAA/STAR(forFlavioIturbide-Sanchez)Authors:FlavioIturbide-Sanchez,YongChen,DaveJohnson,DaveTobin,LarrabeeStrow,LawrenceSuwinski,ClaytonButtles,JoePredina,DenisTremblay,WarrenPorter,XinJinandBanghuaYanOnMarch26,2019,theJointPolarSatelliteSystem(JPSS)InterfaceDataProcessingSegment(IDPS)stoppedproducingtheoperationalSuomiNationalPolar-orbitingPartnership(SNPP)Cross-trackInfraredSounder(CrIS)SensorDataRecord(SDR)product,duetoanidentifiedanomalyontheMid-wave(MW)Infra-Red(MWIR)band.TheCrISSDRproductisaJPSSKeyPerformanceParameter(KPP)DatapresentlybeingassimilatedintoNumericalWeatherPrediction(NWP)modelsbyweatherforecastcenters,includingECMWFandNCEP.TheCrISSDRdataiscriticalforthederivationofglobalsoundingdata,includingthermodynamicparameters,andtracegasspecies.ThelossoftheSNPP/CrISMWIRbandhasrepresentedthelossofSNPPproductsderivedbytheNOAAUniqueCombinedAtmosphericProcessingSystem(NUCAPS),whichistheofficialNOAAsystemretrievingverticaltemperature,andwatervaporprofilesfromtheprocessingofCrISandATMSSDRs.AftertheMWIRanomaly,theCrISSDRTeamhasbeenworkingtoensurethattheLong-wave(LW)andShort-wave(SW)IRbandsareoperatingnominal,andmeetingtheJPSSrequirements.AnomalyinvestigationresultshavehelpedtoidentifyafailureontheSNPP/CrISMWSignalProcessor(SP)circuitcardassembly(CCA).TheSNPP/CrISinstrumenthasbeenoperatingusingSide-1electronicssincefirstsciencedatawasproducedonJanuary18,2012.CommandingtheinstrumenttooperateusingtheredundantSide-2electronicshasbeenidentifiedasthemainoptiontorecovertheMWband,withminimalrisk.TheresultsofperformingtheswitchtoSide-2redundantcircuitrywillbereportedaspartofthiswork,aswellasreportingthemostup-to-datestatusofthequalityoftheSNPP/CrISSDRsciencedata.Session2:Calibration,Validation(oralpresentations)2.06 AnassessmentofdatafromtheGIIRSinstrumentPresenter:ChrisBurrows,ECMWFAuthors:ChrisBurrows,TonyMcNally,MarcoMatricardi,ReimaEresmaaGIIRSisahyperspectralinfraredsounderwhichiscarriedontheChinesesatelliteFY-4A,andisthefirstinstrumentofitskindonageostationaryplatform.Therefore,itisaprecursortoIRSwhichwillbepartoftheMeteosatThirdGenerationseries.Full-resolutionspectraldatafromGIIRSbecameavailableinJanuary2019,andthispresentationwilldescribetheresultsofthesubsequentworkofanalysingthesedata,inparticular,comparingtheobservationswithsimulationsbasedonECMWFmodelfields.Beforethesedatacanbeassimilated,itisnecessarytoperformacarefulassessmentofthedata,andconsideraspectssuchasqualitycontrol,cloudandaerosoldetection,channelselectionetc.andthestatusoftheseassessmentswillbepresentedhere.2.07 RetrospectiveCalibrationofHistoricalChineseFengyunSatelliteDataPresenter:PengZhang,NationalSatelliteMeteorologicalCenterAuthors:PengZhang,XiuqingHu,SongyangGu,LinSun,NaXu,LinChenThefirstChinesemeteorologicalsatellitewaslaunchedin1988.Sofar,theChinesemeteorologicalsatellitehasbeencontinuouslyobservedfornearly30years.Satellitereplacementandon-boardsensorsupgrademake

theoldandnewobservationdataunevenintermsofaccuracy,stabilityandconsistency,andcannotmeetthebasicneedsoflong-termsequenceclimateandenvironmentalchangeresearch.Toenhancethecapabilityonthespace-basedessentialclimatevariable(ECV),anewNationalKeyResearch&DevelopmentProgramofChinawasfundedsince2018tore-calibratethehistoricalChineseEarthObservationsatellitedataincludingtheChineseFengyunMeteorologicalSatellites(FY),theChineseHaiyangOceanicSatellites(HY),andtheChineseZiyuanResourceSatellites(ZY).Inthispaper,theprogressonthere-calibratingthe30-years’historicalChineseFYsatelliteswillbeintroduced.ThehistoricalChineseFYsatellitesincludethirteenmeteorologicalsatellites(FY-1A,FY-1B,FY-1C,FY-1D,FY-2A,FY-2B,FY-2C,FY-2D,FY-2E,FY-2G,FY-3A,FY-3BandFY-3C)andsevenvarietieson-boardedinstruments(VIRR,VISSR,MERSI,IRAS,MWTS,MWHSandMWRI).ThevicariousChinaradiancecalibrationsite(CRCS)calibration,thepseudoinvariantcalibrationsites(PICS)calibration,thedeepconvectiveclouds(DCC)calibration,andthelunarcalibrationhavebeenconsideredintheprocedureofthere-calibrationforsolarreflectancebands.Newon-boardcalibratormodelswillbebuiltforinfraredandmicrowavebandsre-calibration.Inaddition,someinitialresultsforthere-calibrationwillbereportedinthispaper.Session2:Calibration,Validation(posterintroductions)2p.08 NOAA-20CALIBRATION/VALIDATIONANDALGORITHMSIMPROVEMENTSPresenter:LihangZhou,NOAA/NESDIS/STARAuthors:L.Zhou,M.Goldberg,M.Divacarla,X.Liu,T,Atkins,andS.KalluriThesuccessfullaunchoftheJointPolarSatelliteSystem(JPSS)-1(JPSS-1,nownamedasNOAA-20)isprovidinganarrayofatmospheric,land,andoceandataproductsfromfourmajorinstruments:TheVisibleInfraredImagingRadiometerSuite(VIIRS),theCross-trackInfraredSounder(CrIS),theAdvancedTechnologyMicrowaveSounder(ATMS),andtheOzoneMappingandProfilerSuite(OMPS).TheseinstrumentsaresimilartotheinstrumentscurrentlyoperatingontheSuomiNationalPolar-orbitingPartnership(S-NPP)satellite.AccountingtothelessonslearnedthroughS-NPPproductCal/ValandbasedonthecharacterizationoftheNOAA-20instruments,theJPSSscienceteamshavedevelopednecessaryalgorithmupgradesforNOAA-20algorithms.ThescienceteamshavealsobeenperformingtheNOAA-20Cal/ValthroughtheEarlyOrbitCheckout(EOC),IntensiveCal/Val(ICV),andLong-TermMonitoring(LTM)phasesaccordingtotheCalValplans.TheIntegratedCalibrationandValidationSystem(ICVS),andtheS-NPPLongTermMonitoringSystem(LTM)developedforS-NPPhavebeenupgradedtotheNOAA-20forspacecraft/sensorhealthandsatelliteproductdisplay/monitoring,respectively.TheJPSSscienceteamshavecompletedthetransitionofthesciencealgorithmstotheEnterpriseAlgorithms,whichusethesamescientificmethodologyandsoftwarebasetocreatethesameclassificationofproductfromdifferinginputdata.Inthispaper,wepresentanupdateoftheNOAA-20andSNPPCal/Valandanoverviewofthealgorithms’improvementsfortheNOAA-20dataproducts.Inaddition,theoperationalimplementationstatusesofJPSSenterprisealgorithmsforproductgenerationandsciencedataproductreprocessingarealsogoingtobebriefed.2p.09 EvaluationofusingmeasuredSRFsintheradiativetransferformicrowavesoundersatECMWF,UKMetOffice,andDWDPresenter:DavidDuncan,ECMWFAuthors:DavidDuncan,EmmaTurner,PeterWeston,NielsBormann,RobinFaulwetter,ChristinaKoepken-WattsMeasuredspectralresponsefunctions(SRFs)havebeengatheredandimplementedinRTTOVforsomecurrentlyoperationalmicrowavesounders(ATMS,AMSU-A),withthegoalofimprovingdatausagebyutilisingmoreaccurateradiativetransfermodellingintheassimilation.TheeffectofusingtheseupdatedSRFs,incontrastwithprevious‘tophat’SRFs,isassessedinthedataassimilationsystemsofthreeNWPcentres:ECMWF,UKMetOffice,andDWD.ResultsareshownintermsofO-B(observedminusbackground)andbiascorrectionstatisticsforthethreecentresandcomparedbetweensensors.Comparisonofthestatisticsfromdifferentcentresprovidesameasureoftheinfluenceofbiasinthebackgroundfieldsinthisassessment.WhilemostSRFchangesarenotdrastic,theimpactsonsimulatedradiancesatuppertroposphericandstratospheric

channelsaresignificant.TheresultingchangesinassimilationtrialscanbeevaluatedusingotherobservationswithsensitivityintheupperatmospheresuchasGPS-RO,hyperspectralIR,andradiosondes.2p.10 NOAA-20CrISNoiseAssessmentPresenter:YongChen,NOAA/GST(forDenisTremblay)Authors:DenisTremblay,YongChen,FlavioIturbide-Sanchez,XinJin,ErinLynchThisworkreportsontheon-orbitperformanceoftheCrossTrackInfraredSounder(CrIS)thatiscurrentlyflyingon-boardtheNOAA-20satellitethatwaslaunchedintoorbitonNovember18th2017.Thenoiseequivalentdifferentialradiance(NEdN)isonecomponentoftheCrISinstrumentalperformance.TheoperationalalgorithmestimatestheNEdNbytakingintoaccountthecalibrationmeasurementsoftheinternalcalibrationtarget(ICT)andthedeepspace(DS)viewsonly.TheICTradianceiscalculatedovertheslidingwindow,thatcontains30scans,andtheNEdNisestimatedbycalculationthestandarddeviationateachfrequencybins.TheNEdNmeettherequirementswithmargintotheexceptionofMWIRFOV9whichisborderline.AnalternativenoisecalculationmethodologyusesthePrincipalComponentAnalysis(PCA)thatusesonlyEarthsceneviews.TheresultsshowsthattheoperationalNEdNisunderestimatedforLWIRFOV5by30%.TheNEdNishigherforhotEarthscenecomparedwiththeoperationalNEdNbyupto50%.ThefullcovariancematrixcalculatedwithPCAshowscorrelatednoiseforoff-diagonalfrequenciesduetotheinstrumentlineshapeeffects.Moreover,correlatednoisewasfoundatthe668cm-1frequency.Accuratenoiseestimatesisveryimportantfordownstreamproducts.Itisusedasweightingfunctionofthevariouschannelsthatareassimilatedintotheweatherforecastingsystemandtheretrievaloftracegases.2p.11 CalibrationofNOAA-20ATMSPresenter:Quanhua(Mark)Liu2p.12 NOAA-JPSSdedicatedradiosondedatabaseinsupportofsatellitedatacalibration/validationPresenter:BominSunAuthors:BominSun,AnthonyReale,Cheng-ZhiZou,XavierCalbet,ManikBali,andRyanSmithTheGlobalClimateObservingSystem(GCOS)ReferenceUpperAirNetwork(GRUAN)isareferenceobservingnetworkdesignedtoprovidefullycharacterizeddatarecordsforupper-airclimatechangedetection.AconcertedefforttoutilizeGRUANtosupplementtheGlobalSpace-basedInter-CalibrationSystem(GSICS)inthemonitoringandassessmentofenvironmentalsatellitesensorswasinitiatedattheGSICSAnnualmeetingin2017.ThosesensorsincludetheCross-trackInfraredSounder(CrIS),theInfraredAtmosphericSoundingInterferometer(IASI),theHigh-resolutionRadiationSounder(HIRS),theAdvancedTechnologyMicrowaveSounder(ATMS)andtheAdvancedMicrowaveSoundingUnit(AMSU).Inthiswork,thefeasibilityofusingGRUANobservationstomonitorsatellitesensordataareexploredintwoareas.OneistocomparetheGRUANtemperatureobservationswithpolarsatellitemicrowavedataintrendsandinter-annualvariability.ThesatellitemicrowavedatasetincludescalibratedfundamentalClimateDataRecords(FCDRs)generatedbyNOAACenterforSatelliteApplicationsandResearch(STAR).ThesecondistounderstandtheconsistencyofGRUANradiosondehumidityobservationswithsatellitewatervaporsensitivesensordata.ThisisachievedbyusingRadiativeTransferModel(RTM)simulationtoconvertGRUANatmosphericprofilesintotheradiancespaceforcomparisonwithcollocatedhyperspectralinfraredsensordata.Collocationuncertaintyanduncertaintyinsatellitesensor,GRUANandRTmodelaretakenintoaccountintheassessment.ThisworksupportsGSICSandGRUANobjectivestomonitormicrowaveandinfraredsensorsfromspacebasedplatformsincludingthedeterminationofabsoluteaccuracyofthesensors.

Session3:NWPCentreReports(posterintroductions)3p.01 RecentupdatestotheECCCGlobalandRegionalPredictionSystemsPresenter:AlainBeaulne,ECCCAuthors:A.Beaulne,J.St-James,S.Laroche,S.Heilliette,I.Mati,S.Macpherson,M.Reszka,E.Lapalme,T.Milewski,M.Deshaies-Jacques,L.GarandObservationsfromrecently-launchedgeostationaryandpolar-orbitingsatellitesareassessedinpreparationforoperationalimplementationinECCC(EnvironmentandClimateChangeCanada)globalandregionalpredictionsystems.Morespecifically,fromtheGOES-Rseries,GOES17AMVs(AtmosphericMotionVector)replacethosefromGOES15andCSRs(ClearSkyradiance)fromthethreewatervapour(WV)sensitivechannelsofGOES16areadded.Likewise,theevaluationincludesobservationsfrominstrumentsonboardtheJPSS(JointPolarSatelliteSystem)NOAA20satellite,inparticularAMVsretrievedfromVIIRS(VisibleInfraredImagingRadiometerSuite),radiancesfromATMS(AdvancedTechnologyMicrowaveSounder)aswellastheCrIS(Cross-trackInfraredSounder)FSR(FullSpectralResolution)product.AsfortheSNPP(SuomiNationalPolar-orbitingPartnership)satellite,radiancesfromtheCrISFSRproductarealsoconsideredforoperationalimplementationasareplacementfortheNSR(NominalSpectralResolution)product.MarinewindvectorsretrievedfromtheASCAT(AdvancedScatterometer)instrumentonboardthelastsatelliteintheEUMETSAT(EuropeanOrganisationoftheExploitationofMeteorologicalSatellites)PolarSystemprogramme,MetOp-C,arealsoexamined.Inaddition,ground-basedGPS(GlobalPositioningSystem)measurementsofzenithtotaldelay(ZTD)fromtheU.S.SuomiNetnetworkprovidedbytheUniversityCorporationforAtmosphericResearch(UCAR)areevaluatedasareplacementforthepreviously-receivedNOAAobservations.Cumulativeeffectsoftheaboveobservationsareassessedbyexaminingobservation-minus-backgrounderrors,aswellasforecastscoresagainstbothradiosondeobservationsandanalyses.3p.02 OverviewofradiancedataassimilationdevelopmentsatDWDsinceITSC-21Presenter:ChristinaKöpken-Watts,DWDAuthors:Ch.Köpken-Watts,R.Faulwetter,O.Stiller,A.Walter,S.May,M.Pondrom,K.Raykova,R.PotthastThisoverviewposterdescribestheupgradestoDWD’soperationalglobalICONandhybridensemblebasedEnVardataassimilationsystemintroducedoverthelasttwoyearsinthefieldofsatelliteradianceassimilation.AparticularfocusofthelastyearswasonusinghumiditysensitiveradianceswhichcouldneverbeassimilatedwithbenefitinthepreviousNWPsystem.Now,allcurrentoperationalinstruments,IRandMWhumiditysoundersaswellasMWimagershavebeentechnicallyimplemented.Afterextensivetesting,IRhumiditychannelsfromthehyperspectralIASI,andthegeostationaryimagersSEVIRI,GOES,AHI,ABI,aswellasfromtheMWsounderchannelsfromMHS,ATMS,SSMIS,GMIhavebeenintroducedoperationallywithasoundpositiveimpact.TestswithMWHS-2areongoingandMWimagersarecurrentlyusedforvalidationwithassimilationtestsabouttostart.Furtherupgradesaddressedaretuningofthehorizontalthinningforseveralinstruments,theintroductionoffullobservationerrorcovariancematricesandanupdateofcloud-screeningforIASI.ThenewNOAA-20andMETOP-Cinstrumentshavebeenevaluatedandresultsfromrecentdataimpactexperimentsaddressingtheimpactofbroaddatacategoriesareshown.Alastsectionwillbrieflypresentongoingdevelopmentsthatarenotdescribedinotherconferencecontributions,e.g.concerningtheretrievalofemissivityforIRandMWtoenhancedatausageoverland.3p.03 ProgressandplansfortheuseofradiancedataintheNCEPglobalandregionaldataassimilationsystemsPresenter:AndrewCollard,IMSG@NOAA/NCEP/EMCAuthors:AndrewCollard,YanqiuZhu,HaixiaLiu,EmilyLiu,KristenBathmann,LiBi,RussTreadon,JimJung,DarylKleist,CatherineThomas,XuLi,XiaoyanZhangandErinJonesSincethelastInternationalTOVSStudyConferenceinDecember2017,therehasbeenonemajoroperationalupgradestothedataassimilationsystematNCEP(inJune2019).ThisupgradewasprimarilytoreplacethespectraldynamicalcoreoftheforecastmodelwiththeFiniteVolumeCubed-Sphere(FV3)coredevelopedbytheNOAAGeophysicalFluidDynamicsLaboratory(GFDL).Themostsignificantdataassimilationadvancesinthisperiodwere:

1. UpgradeATMStoassimilateradiancesinall-skyconditions

2. IncludeIASIhumiditychannels3. Usefullspectralresolution(FSR)dataforCrISincludingtheuseofhumiditychannels4. AddtheuseifSaphirandMeteosat-11SEVIRIradiances

AssimilateradiancesfromAMSU-A,MHSandIASIonMetOp-CThenextscheduledupgradetotheGlobalForecastSystemisinJanuary2021whichwillbefocussedonincreasingthenumberofmodellevelsfrom64to127,butwillincludeDAupgradessuchastheintroductionofspectrallycorrelatedobservationerrorsandimproveduseofcloudyradiances.3p.04 NCMRWFNWPStatusPresenter:SIndiraRani,NCMRWFAuthors:S.IndiraRani,JohnP.George,V.S.Prasad,SumitKumar,BushairM.T.,BuddhiPJangid,ALodh,GibiesGeorge,andE.N.RajagopalTheNCMRWFNWPSystemsareusedforbothoperationalandresearchpurposes.NCMRWFistheAnalysiscentreforNationalWeatherService,IMD.Recentlyupdatedtheassimilationandforecastsystemandincludedradiancesfromstateoftheartmeteorologicalsatellites.3p.05 RecentchangesintheuseofpassivesoundingdataintheECMWFNWPsystemPresenter:NielsBormann,ECMWFAuthors:NielsBormann,StephenEnglish,MohamedDahoui,PhilBrowne,MassimoBonavita,ChrisBurrows,DavidDuncan,ReimaEresmaa,AlanGeer,EliasHolm,HeatherLawrence,PeterLean,KatrinLonitz,CristinaLupu,MarcoMatricardi,TonyMcNally,KirstiSalonen,PWestonThepostergivesanoverviewofthestatusoftheassimilationofpassivesoundingdata,andhighlightsrecentrelevantchangesintheECMWFNWPsystem.Atthetimeofwriting,ECMWFassimilateradianceobservationsfrom20MWinstruments,5hyperspectralIRsounders,and5geostationarysatellites.AdditionssinceITSC-21havebeenNOAA-20,Metop-C,aswellasGOES-16andMeteosat-11.TherehavebeentwomajorsystemupgradessinceITSC-21,cycles45r1(5June2018)andcycle46r1(11June2019).KeychangesfortheuseofIRsounderdatahavebeentheassimilationofnon-surface-sensitiveinfra-red(IR)channelsoverland(45r1),asubstantialincreaseinthenumberofassimilatedWVchannelsforIASI(46r1),updatestotheaerosoldetection(46r1),aswellasanoverhauloftheassimilationofgeostationaryradiances(extendeddisk,slant-path,correlatedobservationerror,46r1).HighlightsforMWradiancescoverinter-channelobservationerrorcorrelationsforATMS(46r1),activationofconstrainedvariationalbiascorrectionforthetop-mosttemperature-soundingchannelonAMSU-AandATMS(45r1),updateofthepermittivitymodelinRTTOV-SCAT(46r1),andtheadditionofSSMIS-F17150hGHzandGMI166v/hGHzchannels(46r1).Inaddition,theRTTOVradiativetransfermodelhasbeenupdatedtoversion12(in45r1)and12.2(in46r1),togetherwithanupdatetotheMWcoefficientfiles.Cycle46r1alsosawtheintroductionofcontinuousdataassimilation,amajorreconfigurationofourdataassimilationsuitethatallowsustoassimilatemoreobservationsandincreasesthebenefitofobservationswithshorttimeliness.Otherrelevantdataassimilationchangesincludeamoveto50membersintheEnsembleofDataAssimilations(EDA)whichallowsabetterestimationofbackgrounderrors,aswellastheintroductionofweaklycoupleddataassimilationforsea-surfacetemperatureinthetropics,continuingthetrendtowardsmorecouplingindataassimilationandawiderEarthSystemapproach.3p.06 OngoingdevelopmentsonsatelliteradianceassimilationatMétéo-FrancePresenter:NadiaFourrié,CNRMMeteo-FranceandCNRSAuthors:NFourrié,PhChambon,V.Guidard,OCoopmann,ZSassi,PMoll,DRaspaudandJFMahfoufAlargepartofassimilatedobservationsintheglobalmodelofMétéo-FranceARPEGEcomefromsatellitesradiances(mostlyonpolarorbitingsatellites).Satelliteradiancesarealsoassimilatedinthemeso-scalemodelAROME-FrancebutrepresentasmallerfractionofobservationswithadominanceofMSGSEVIRI.ThisposterintendstogiveanoverviewoftheradianceusageintheFrenchNumericalWeatherPredictionmodelsandthestatusofthecurrentdevelopments.TherelativeweightofeachradiancetypewillbegivenintermsofdegreesofsignalforFreedomandthesummaryofrecentchangesindatausageinthefuture2019operationalsuitewillbepresented.

Amongthevariousdevelopments,highlightswillbegivenontheuseofobservationerrorcross-correlationsforinfraredhyperspectralradiancesfromIASIandCrIS,theuseofthesurfacetemperatureretrievalfortheassimilationofIASIradiancesovercontinents,theassimilationof5ozoneIASIchannelsintheglobalmodel,theassimilationofall-skySAPHIRandMHSmicrowaveradiances.3p.07 RecentupgradesintheuseofsatelliteradianceobservationswithintheMetOfficeglobalNWPsystemPresenter:ChawnHarlow,MetOfficeAuthors:ChawnHarlow,BrettCandy,NigelAtkinson,JamesCameron,FabienCarminati,AmyDoherty,StephanHavemann,StefanoMigliorini,StuartNewman,EdPavelin,DavidRundle,AndySmith,FionaSmith,LauraStewart,RuthTaylor,MichaelThurlow,SimonThompsonImprovementsintheassimilationofsatelliteradianceobservationshaveledtosignificantperformancegainsintheMetOfficeglobalmodeloverthelasttwoyears.UpgradesinthetreatmentofsatelliteradiancedatahaveoccurredatmodelupgradesPS40(Feb2018),PS41(Sep2018),PS42(Mar2019)andPS43(anticipatedNov2019).Highlightsoftheseupgradesincludethefollowing:

• SignificantincreaseintheuseofAMSU-AChannels4and5duetoallskiesassimilationefforts.Thisrequireddevelopmentofanerrormodelforliquidwatereffectsonradiancesinthesechannels.RetrievalsofcloudliquidwaterareusedwithintheObservationProcessingSystem(OPS)toinflatetheerroronthesechannelsusedinassimilation.

• UpgradefromRTTOV-11toRTTOV-12.ThisincludedthecapabilitytotreatscatteringduetocloudandprecipitationinthemicrowaveviaRTTOV-SCATTwhichisimportantforfuturedevelopmentoftheassimilationof183GHzhumiditysoundingdata.InterfaceswithHT-FRTCandPC-RTTOVwereprovidedtoenabletriallingoftheirschemesbasedonprincipalcomponents.

• UseofRTTOV-12on70modellevelsinsteadof43coefficientlevels.Thiseliminatestheneedtointerpolatebetweencoefficientlevelsandmodellevelswithintheassimilationsystemandprovidesamoreconsistenttreatmentofmodelfieldstherein.

• Noveldevelopmentsinthetreatmentofmicrowavehumiditysoundingdataoverland.Thisrequiredbettertechniquesfordetectingcloudoverheterogeneouslandsurfacesandthedevelopmentofa1dvarretrievalschemeforlandemissivitieswithinOPS.

• IntroductionofnewinstrumentsincludingtwonewmicrowaveimagersFY-3CMWRIandGPMGMIandtheinstrumentsonNOAA-20:themicrowavesounderATMSandthehyperspectralIRsounderCrIS.Theseinstrumentsprovideredundancytopreviousinstrumentalsystemsaswellasgivingbetterspatialandtemporalcoverage.

• ImprovedqualitycontrolforcloudscreeningwasappliedtoGMI,AMSR,andMWRI,basedatestonthebackgrounddepartureat36Hplusanewonebasedonanomalydifferencebetween36and89GHz.

• Assimilationofgeostationaryclear-skyradiancesfromGOES-16ABI.ReplacementforGOES-13allowscontinuityofthesegeostationarymeasurementsontheeasternseaboardoftheUSaswellasaddedvalueduetosevenadditionalchannels.

TheseupgradeswereinadditiontoupgradestotheassimilationofproductssuchasAMV’s,scatterometerandRadioOccultation.TherewerealsoupgradeselsewhereintheNWPsystemincludingupdatestothemodelaswellasthedataassimilationscheme.ThelandsurfaceschemewasupgradedtoGL8whichincludedanewmulti-layersnowschemeandimprovedsurfacedrag.Thedataassimilationupgradesincludedhourlycyclingofbackgroundfieldsin4DVarinordertoobtainamoretimelyfirstguess.Therewasalsoanensembleupgradewhichyieldedbetterinformationaboutthebackgroundcovarianceswhichhadapositiveimpactontheassimilation.Thisposterwillfocusontheimprovementofsatellitedataprocessingandassimilationduringthisperiod.3p.08 SatelliteradianceassimilationattheBureauofMeteorologyPresenter:FionaSmith,BureauofMeteorologyAuthors:FionaSmith,JimFraser,DavidHoward,JinLee,LeonMajewski,SusanRennie,AndrewSmith,PeterSteinle,ChrisTingwell,YiXiaoTheBureauofMeteorologyoperatesaglobalmodel,sixcity-scalemodelsandarelocatabletropicalcyclonemodel.By2020,allofthesemodelswillincludedataassimilationincorporatingsatelliteradiancedata.Thisposterwillprovideanupdateonthesesystemsrelativetothelastconference.

Satelliteradiancedatacontinuetoprovidesignificantimpactinourforecastsystem.OurglobalconfigurationassimilatesbrightnesstemperaturesfromATOVS,IASI,ATMS,CrIS,AIRS,andfrom2019wehaveaddedAMSR-2andHimawari-8.Wearecurrentlyworkingontheadditionofall-skymicrowavedataassimilation,inlinewiththeMetOfficescheme.Thesixcapital-cityconvective-scalemodelconfigurationsandthenewTCmodelusedatafromATOVS,IASI,ATMS,CrISandAIRS,frombothglobalanddirectreadoutsources.Inthenearfuturewehopetoaddbrightnesstemperaturesand'GeoCloud'retrievalsfromHimawaritothecitysystems.3p.09 RecentupgradesofsatelliteradiancedataassimilationatJMAPresenter:NorioKamekawa,JapanMeteorologicalAgencyAuthors:NorioKamekawa,HidehikoMurata,MasahiroKazumori,IzumiOkabeThisposteroverviewsrecentupgradesofsatelliteradiancedataassimilationintothenumericalweatherprediction(NWP)systemattheJapanMeteorologicalAgency(JMA)sincethelastITSC-21inNovember2017.JMAbegantoassimilatesurface-sensitiveclear-skyradiance(CSR)datafromHimawari-8forareasoverlandandMeteosatSecondGeneration(MSG)dataforareasoverlandandoceansintoitsglobalNWPsystemonOctober182018.Inordertoassimilatesurface-sensitiveCSRdataforareasoverland,anewradiativetransfercalculationmethodinvolvingtheuseofdatafromtheWisconsinUniversitylandsurfaceemissivityatlasandlandsurfacetemperaturesretrievedfromwindow-channelCSRobservationdatawasdeveloped.JMAalsobegantoassimilatesurface-sensitiveCSRdatafromHimawari-8intoitsmesoscaleNWPsystemcoveringJapananditssurroundingareasonMarch262019,andthenewradiativetransfercalculationmethodusedinitsglobalNWPsystemwasapplied.JMAhasassimilatedradiancedatafromtheAdvancedTechnologyMicrowaveSounder(ATMS)andCross-trackInfraredSounder(CrIS)onboardNOAA-20intoitsglobalNWPsystemfollowingthoseofSuomi-NPPsince5March2019.NOAA-20dataqualityissimilartoorbetterthanthatofSuomi-NPP,andtheadditionaluseofNOAA-20dataimprovedthefirst-guessandforecastfields.TheSuomi-NPP/CrISradiancedatawasswitchedfromNSR(NormalSpectralResolution)toFSR(FullSpectralResolution).ATMSandCrISradiancedatadeliveredfromDirectBroadcastNetwork(DBNet)havebeenaddedtotheEarlyAnalysisofglobalNWPsystem(cut-offtime2h20m)andtheavailabledataareincreased.Infutureplan,GOES-16CSRdata,all-skymicrowaveradiancedata,andATOVSdataonMetop-CwillbeincorporatedintheJMAoperationalglobaldataassimilationsysteminthisyear.3p.10 OverviewofSatelliteRadianceDataAssimilationinNCEPFV3RegionalSystemPresenter:XiaoyanZhangAuthors:XiaoyanZhang,HaixiaLiu,AndrewCollardandJacobCarleyNOAA’snextgenerationNumericalWeatherPredictionsystemsarebasedontheconceptofunifyingaroundtheFiniteVolumeCubed-Sphere(FV3)dynamicalcore.TheFV3isusedforglobalNWPandwillbeforconvective-scaleapplicationsaswell.Forconvective-scaleNWPalimitedareaversionoftheFV3dynamiccoreisusedandisknownastheStandAloneRegional(FV3-SAR).Thislimitedareaconfigurationispoisedtounderpintherapidlyupdated,convectionallowingensemblesystemintheNCEPproductionsuiteastheRapidRefreshForecastSystem(RRFS)inthe2022-2023timeframe.Theearlyphaseoftestinganddevelopmentwiththe3kmFV3-SARleveragesaconfigurationsimilartothecurrentoperational3kmNAMCONUSnestwhichfeaturesa6-hourlongassimilationcycle,withhourlyforecast/analysiscomponents,endingwithafreeforecast.Theassimilationisconductedusingahybrid3DEnVarmethod.ThecurrentdevelopmentalsystemassimilatesthesamesatelliteradiancesasisdonewiththeoperationalNAM,whichfeaturesradiancesfromthefollowinginstrumentsonboardpolarorbitingsatellites:AMSUA,MHS,IASI,AIRS,andCRIS/.TheperformanceofthesesatelliteradiancedataassimilationwillbeevaluatedinthenewFV3-SARconfiguration.However,observationsthatfeaturecontinuous,low-latencycoverageoverahigh-resolutionlimitedareaCONUSdomainareofparticularimportanceintheemergingRRFSframework.ThereforeradiancesfromthenewestgenerationofGOESstandtobeparticularlyvaluableandhencethisworkalsoexaminesthe

assimilationofGOES-16radiancedata.Thenew-generationGOES-16geostationarymeteorologicalsatellitewassuccessfullylaunchedonNovember19,2016.GOES-16carriestheAdvancedBaselineImager(ABI)whichobservesEarthwith16differentspectralbands,includingtwovisiblechannels,fournear-infraredchannels,andteninfraredchannels.GOES-16providescoverageovera1000x1000kmboxwithatemporalresolutionof30seconds,andspatialresolutionof0.5to2km.Notably,thereisasignificantimprovementinspectral,spatialandtemporalresolution,eachofwhichhasbenefitsfordataassimilation.ABIClearSkyRadiance(CSR)dataproducedfromGOES-16radianceobservationhavebeendistributedtotheNumericalWeatherPrediction(NWP)communitybyNOAA/NESDIS.CSRdataarebox-averagesof15x15pixelscontaininginformationontropospherichumidityunderclear-skycondition.Inthisstudy,theimpactofGOES-16CSRdataassimilationinNOAA’sdevelopmental,3kmFV3-SARsystemwillbeinvestigated.3p.11 ProgressandplansforsatellitedataassimilationinKMAoperationalNWPsystemPresenter:EunheeLee,KMAAuthors:EunheeLee,Hye-YoungKim,YoungsoonJo,EunHeeKim,Mee-jaKim,Yong-HeeLeeTheKoreaMeteorologicalAdministration(KMA)hasrecentlyintroducedseveralupgradestotheuseofsatellitedatainitsGlobalDataAssimilationandPredictionSystem(GDAPS)basedonUnifiedModel.SincethelastITSC,KMAhasnewlyassimilatedHimawari-8CSR,MT-SAPHIR,GCOM-W1/AMSR2,FY-3C/MWHS-2,LEOGEOwindsandGNSS-ROfromTanDEM-X,TerraSAR-X,GRACE.MT-SAPHIRandAMSR2dataenhancedconvectiveactivitiesinmiddleandlowertroposphericlayersoverTropicsandtheyhadaneffectonincreasingtheinitialspecifichumidityintheDAprocess.Theimpactsforglobalmodelshowedslightlypositiveimprovementsinwindandhumidity.Thisyear,theKMAhastwoimportantissues.OneistheintroductionofanewKoreanglobalnumericalweatherprediction(NWP)model,andtheotheristheuseofthe2ndgeostationarysatelliteofKorea,Geo-KOMPSAT-2A(GK-2A)waslaunchedsuccessfullyin5Dec.2018,inaNWPsystem.TheKMAhascarriedoutaprojecttodevelopaKoreanglobalNWPmodelinordertoimprovethepredictionaccuracyoftheweatherphenomenonontheKoreanPeninsulaandtoensuretheidentityofmeteorologicaltechnology.Sincetheprojectisscheduledtoendattheendofthisyear,KMAhasputlargeeffortintointroducingthedevelopedKoreanglobalNWPmodelasanoperationalmodelofKMA.KMA’snewglobalmodelhasitsowndataassimilationsystemusinghybrid4D-EnVar.TheKMAhasbeendedicatingtoimprovethesatellitedataassimilationsystem.Themodelincludingdataassimilationsystemwasdevelopedforalimitedperiodof10years.Therefore,muchimprovementsandoptimizationisstillneeded.CurrentstatusandplansforthisnewsystemwillbebrieflyintroducedinITSC-22.ThedataofCOMS,thefirstgeostationarysatelliteofKorea,hasbeensuccessfullyassimilatedinKMANWPsystem.TheGK-2AsatelliteswillinheritthemissionofCOMStoobservetheweatherandstrengthenthenationalcapabilitytomonitorthemeteorologicalphenomenaaroundtheKoreanPeninsula.ThepreliminaryresultsfromtheassimilationofGK-2ACSR(ClearSkyRadiance)andAMV(AtmosphericMotionVectors)datashowaslightlypositiveimpactonthemiddleandhighertropospherichumidityandwindfields,significantimprovementsareshowninAsiaregion.TomakebetteruseoftheGK-2Adata,qualitycontrolanddataassimilationmethodhasbeeninvestigated.TheGK-2AdatawillbedisseminatedviaGTSfromtheendofthisyearinnear-realtime.Session4:AssimilationofGeostationaryInfraredSensors(oralpresentations)4.01 AssimilationofInfraredRadiancesfromGeostationarySatellitesatNCEPPresenter:HaixiaLiu,IMSG,NOAA/NCEP/EMCAuthors:HaixiaLiu,AndrewCollardGeostationarysatellitesprovidehightemporalandspatialresolutionimageryoftheEarthatvisibleandinfraredwavelengths,however,duetotheirdatavolumebeingtoolargeattheiroriginalpixellevel,numericalweatherprediction(NWP)centersusuallyassimilateClear-SkyRadiance(CSR)orAll-SkyRadiance(ASR)productsintheirglobalsystems.NationalCentersforEnvironmentalPrediction(NCEP)hasbeenactivelyassimilatingtheCSRdatafromtheSEVIRItwoinfraredwatervaporchannels.WestartedusingthedatafromMSG10inAugust2013andjustswitchedrecentlytotheCSRfromMSG11.TheCSRfromMSG08hasbeenavailablebutnotbeeninvestigatedyet.TheCSRfromtheAHIonboardofHimawari8hasbeenavailablebutonlymonitoredintheoperation.Sincelastyear,tohelpimprovethealgorithmtogeneratetheCSRfrom

ABI_GOES16,wehaveevaluatedseveralversionsoftheABI_GOES16CSRdatausingouroperationalconfiguration.Inthispresentation,evaluationoftheseversionsoftheGOES-16CSRdatawillbediscussed.TheCSRproductsfromSEVIRI_M08,SEVIRI_M11,AHI_Himawari8andABI_GOES16togetherformagoodcoverageinthetropicalandmiddle-latituderegions.OnJune12,2019,NCEPhasimplementedtheGlobalForecastSystem(GFS)v15whichusestheFinite-VolumeCubed-Sphere(FV3)dynamicalcoreandGFDLmicrophysics.WearegoingtoevaluatetheseCSRdataqualitynotonlyforthewatervaporchannels,butalsothesurfacechannelsthroughstudyingthestatisticalcharacteristicsofthesedatacomparedwiththesimulatedmodelequivalence(OmF)usingthenewly-implementedGFSv15model.TheassimilationexperimentwillthenbeconductedusingtheCSRdatafromonlythewatervaporchannelsofalltheabove-mentionedinstrumentsandresultswillbediscussed.Inaddition,theASRdatafromABI_GOES16hasbeendevelopedalongwiththeCSRattheUniversityofWisconsinandwillbedisseminatedatNOAA/NESDISsoon.InitiallythebaselineversionoftheASRwillbeimplementedintheNESDISoperationaldatastream,buttheenterpriseversionisavailablefortestingpurpose.BothversionsofASRBUFRdatawillbebrieflyinvestigatedanddiscussedinthispresentation.4.02 ResearchonassimilationofFY-4AAGRIradianceinGRAPESGlobalForecastSystemPresenter:HaoWang,NationalMeteorologicalCenterofCMAAuthors:HaoWangInformationaboutmoisturedistributionisveryimportfornowcastingandforecasting.TheAdvancedGeosynchronousRadiationImager(AGRI)onboardtheChinesenewstationarysatelliteFY-4A,thesecondgenerationofgeostationaryimageramongglobalmeteorologicalobservationsystem.TheAGRIonboardFY-4Ahas3visible,3near-infrared,and8infraredchannels,provideshightemporalandspatialresolutionmoistureinformationthatusefulforNWPC.NowFY-4AAGRIradianceareassimilatedinGRAPES_GFSandrelevantassimilationtechniquesandapproacheshavebeendeveloped.TheassimilationexperimentareverifiedagainstNCEPanalysisshowpositiveimpactontheverticaldistributionoftherootmeansquareerroroftheEastAsianwatervaporfield.Accordingtotheanomalycorrelationscores(ACC)ofgeopotentialheight,theassimilationexperimentillustratethattheanomalycorrelationscoresofforecast500-hPageopotentialheightsareincreasedslightlyinEastAsian,significantlyfromday1today8.AssimilationofFY-4AAGRIradiancedatahasapositiveimpactinGRAPES_GFSforecast.4.03 AssimilationofgeostationarywatervapourclearskyradianceswithanEnsembleKalmanFilterPresenter:MarcPondrom,DeutscherWetterdienstAuthors:M.Pondrom,C.Köpken-Watts,A.Rhodin,R.FaulwetterWatervapourradiancesmeasuredbysatelliteinstrumentsnotonlycontaininformationaboutthewatervapourdistributionintheatmospherebutalsoonthewindfieldthroughthedisplacementofthewatervapourstructures.Thisinformationcanbeexploitedthroughthedirecttrackingofthesemovements,donee.g.inthederivationofso-calledwatervapouratmosphericmotionvectors,butalsoinadataassimilation(DA)system.Theimpactofgeostationaryclearskywatervapourradiances(CSRs)oftheSpinningEnhancedVisibleInfra-RedInstrument(SEVIRI)onboardMeteosat8and10onspecifichumidity,temperatureandwindanalysesandforecastsoftheoperationalglobalensemblevariationalassimilationsystemoftheGermanWeatherService(DWD),hasbeeninvestigated.ThissystemconsistsoftheglobalICONmodelandanEnVardataassimilationusinga40memberLETKF(localensembletransformkalmanfilter)toestimatetheflowdependentbackgrounderrorcovariancematrices.Thestudyusesontheonehandasetofobservingsystemexperimentsperformedunderidealisedconditionswithsyntheticobservationsbasedonamodel‘nature’run(“twinexperiments”),andontheotherhandimpactexperimentswithrealdata.Inbothcases,theverificationscoresshowthattheassimilationofCSRsfromthetwowatervapour(WV)channelsat6.25and7.35µmimprovetheforecastskillsnotonlyforhumiditybutalsoforthedynamicvariables,especiallyinthehighandmiddletroposphereandintheregioncoveredbyMSG1and3.Thedifferentresultsobtainedfromtheidealisedexperimentsshowthattheimprovementisduetotheinteractionbetweenspecifichumidity,temperatureandwindthroughthemodeldynamicsandphysicsduringtheforecaststepaswellasthroughtheimprovedspatialcorrelationsandcross-correlationsbetweenvariablesoftheupdatedbackgrounderrorcovariancematrixderivedfromtheensemble.

4.04 ImpactofGeostationaryClearSkyradiancesassimilationonthewindfieldovertheIndianOceanregionPresenter:M.T.Bushair,NCMRWFAuthors:M.T.Bushair;BuddhiPrakashJangid;S.IndiraRaniandJohnP.GeorgeMeteosatsecondgeneration(MSG-2)satellite,Meteosat-8wasrelocatedtotheIndianOceanDataCoverageService(IODC)locationbyreplacingtheMSG-1satelliteMeteosat-7on01February2017.ThispaperanalysestheimpactofSEVIRIClearSkyBrightnessTemperature(CSBT)intheNCMRWFAssimilationandForecastSystem.TheimpactofWatervapor(WV)channelderivedClearSkyBrightnessTemperature(CSBT)fromgeostationarysatellitesinthe4Dvariational(4D-VAR)assimilationsystemsarevividinthehumidityfield.Inthevariationalassimilationsystems,thechangeinonevariablecannothappeninisolation;thechangeinhumidityfieldcanaffectbothtemperatureandwindfieldsaswell.ThispaperdiscussesaseriesofObservingSimulationExperiments(OSEs)analyzingtheimpactoftheradiancefromSpinningEnhancedVisibleandInfra-RedImager(SEVIRI)onboardMeteosat-8satellite,thatprovidestheIndianOceanDataCoverage(IODC)service.TheassimilationandforecastsystemusedinthisstudyisNCMRWF’sUnifiedModel(NCUM).NCUMoperationallyassimilatesbothSEVIRICSBTandtheAMVsfromMeteosat-8alongwithotherconventionalandsatelliteobservations.NCUMroutinelyassimilatesINSAT-3DsounderCSBTandtheAMVsderivedfromINSAT-3DImager.SincebothMeteosat-8andINSAT-3Dhaveapproximatelysamegeographicalcoverage,INSAT-3Dobservations,bothCSBTandAMVsarerestrainedintheOSEstoquantifytheimpactofCSBTandAMVsfromMeteosat-8.OSEsaredesignedinsuchawaythatalongwithotherglobalobservations,i)CSBTs(EXP1),ii)AMVs(EXP2),iii)bothCSBTsandAMVs(EXP3)fromMeteosat-8andiv)noCSBTsandAMVs(EXP4)fromMeteosat-8.ThefourthoneisconsideredasthebaselineexperimentandtheimpactofCSBTandAMVsareanalyzedintermsofthebaselineexperimentinbothassimilationandforecastsystem.ImpactofSEVIRIradianceassimilationiscomputedintermsofdifferentmeteorologicalparameterslikeRelativeHumidity,Temperature,andwind.Insomeexperimentsapositiveimpactonupper-levelwindfields(around200hPa)isseen,thiswasmainlyduetotheassimilationofWVchannelCSBT.AreasonablesensitivityisobservedinforecastduetotheassimilationofSEVIRICSBTinNCUMsystem.Session4:AssimilationofGeostationaryInfraredSensors(posterintroductions)4p.01 Towardstheuseofabayesianapproachfortheassimilationofall-skyIASIradiancesPresenter:NadiaFourrié,CNRMMeteo-FranceandCNRSAuthors:ImaneFarouk,NadiaFourrié,VincentGuidard,PhilippeChambonandFabriceDuruisseauThecurrentgenerationofadvancedinfraredsoundersisoneofthemostimportantsourcesofobservationsindataassimilationsystemsinnumericalweatherprediction(NWP)models.Currentlythe"all-sky"assimilationforinfraredsoundersisunderway.Asaprerequisite,theevaluationandimprovementofhomogeneousscenedetectionalgorithmsbasedonthecolocationofobservationswithotherimagerswasstudied.Differentcriteriaforselectinghomogeneousscenesareproposed.Byconductingassimilationexperimentsandevaluatingtheimpactoftheseproposedselectioncriteriaonthequalityoflong-termforecasts,oneoftheproposedtestsstandsoutfromtheothersbykeepingasignificantamountofclearskyobservationsanddemonstratingneutraltoslightlypositiveimpactsontheforecasts(Farouketal.,2019).Toaddresstheissueofall-skyradiancedataassimilation,thetwo-stepassimilationtechnique,alreadyusedforradarreflectivityassimilationinAROME(Wattrelotetal.,2014),wasevaluatedforIASIradiancesintheARPEGEmodelinacasestudy.ThismethodbasedonBayesianinversionhasrecentlybeenadaptedforsatellitemicrowaveobservations(Duruisseauetal.,2018).Inasimplifiedframework,severalsensitivitytestswerecarriedoutonthedifferentparametersofthealgorithm,withtheobjectiveofpreparingforfutureworkoninfraredall-skyassimilation.4p.02 All-SkyRadianceAssimilationforCOAMPS-TCTropicalCycloneTrackandIntensityPredictionPresenter:NancyBaker,NavalResearchLabMarineMeteorologyDivisionAuthors:Dr.NancyL.Baker,Dr.AllenZhao,Dr.YiJinandDr.JimDoyleScientistsattheNavalResearchLaboratoryMarineMeteorologyDivisionarepartneringthroughanONR-sponsoredcollaborationwithscientistsatPennStateUniversity(PSU)toenablenewdataassimilationcapabilitiestoimprovetheUSNavy’sCOAMPS®andCOAMPS-TCtropicalcycloneandotherhigh-impactstormforecasts.Theefforthastwomaincomponents,(1)implementationandtestingofthePSUensembleKalmanFilter(EnKF)dataassimilationsystem,and(2)assimilationofall-skyradiance,togetherwithairborneDopplerradarandotherin-situobservations.

ThispresentationwillpresentthedataassimilationapproachandinitialresultsforCOAMPS-TC®usingthePSUEnKFforTCinnercoreassimilationofGOES-13watervaporall-skyradiances(channels8-10)usingtheCommunityRadiativeTransferModel(CRTM),togetherwithairborneradardata,andotherin-situobservations.TheinitialgoalforthisresearchistoevaluatewhetherthisconfigurationcanimprovetheCOAMPS-TCintensityforecastsforHurricanePatricia(2015).WewillcomparetheresultswiththeCOAMPS-TCforecastsusingbothNAVGEM(NAVyGlobalEnvironmentalModel)initialandboundaryconditions,andGFSinitialandboundaryconditions.COAMPS®isaregisteredtrademarkoftheNavalResearchLaboratory.4p.03 Assimilationofcloud-contaminatedradiancesinregionalairqualitymodel:acasestudyusingGEMSsyntheticradiancedataPresenter:EbonyLee,EwhaWomansUniversityAuthors:EbonyLeeandSeonKiParkAstheimpactofairqualityonhumanhealthandsocioeconomicissuesbecomesmoreevident,weareendeavoringtomakeaccurateairqualityforecasting.Recently,manyresearcheshavefocusedontheassimilationofsatelliteobservationsintotheairqualitymodelssincesatellitedatahavetheadvantageofspatiotemporalcoverage.TheGeostationaryEnvironmentalMonitoringSpectrometer(GEMS)isplannedtobelaunchedinlate2019orearly2020,withmissionstomonitorandprovidemeasurementsofatmosphericcomposition(e.g.,O3,NO2,SO2,HCHO,andaerosols)inAsia.TheGEMSobservationsareexpectedtocontributetoimprovingtheaccuracyoftheairqualitymonitoringandforecasting.Sincethesatellite-measuredradiancedataaredeteriorated(contaminated)whencloudsarepresent,itiscommontousecloudscreeningtoremovethedeteriorateddataanduseonlyclear-skydataforassimilation.Tocompensateforthedatalossbycloudscreening,studiesontheall-skyradiancesassimilationhavebeenactivelyconductedinnumericalweatherprediction.Inthisstudy,weassimilatethesyntheticradiancesofGEMSintoWRF-Chemtoinvestigatethecharacteristicaspectsintheairqualityprediction.Thesyntheticradiancesareproducedbyaradiativetransfermodel,calledVLIDORT,byconsideringthespectralandspatialresolutionofGEMS.Toassesstheimpactofthecloudcontaminateddataandthelossofinformationbycloudscreening,wewillcomparethemodelresults:(1)byassimilatingtheradiancescalculatedunderthefullclear-skycondition;(2)byassimilatingthecloud-affectedradiances;and(3)byassimilatingtheclear-skyradiancesaftercloudscreening.Session4:All-skyAssimilationofGeostationaryInfraredSensors(oralpresentations)4.05 Evaluationandassimilationofall-skyinfraredradiancesofHimawari-8intheregionalandglobaldataassimilationsystemPresenter:KozoOkamoto,JMA/MRIAuthors:KozoOkamoto,YoheiSawada,MasaruKunii,TempeiHashino,MasahiroHayashi,MasayukiNakagawa,KeiichiKondoThisstudyinvestigatesthebenefitofassimilatinginfraredradianceobservationsfromsatellitesinall-skyconditions.Wehavebeendevelopingtheall-skyradiance(ASR)assimilationforHimawari-8intheregionalandglobaldataassimilationsystem,andplantoextendittohyperspectralinfraredsounders.Thedevelopmentincludesabandselectionbasedonobservationerrorcorrelation,acloud-dependentqualitycontrol(QC)procedureandobservationerrormodel.TheimpactofASRassimilationwascomparedwiththeCSRassimilationusingaregionalLETKFassimilationsystem.TheASRassimilationbroughtbetterfitoffirst-guesstoradiosondeobservationsandmorestableimprovementinasevererainfallpredictionduetomoresecuredatacoverage.Testingbiascorrection(BC)fortheASRassimilationshowednoadditionalpositiveimpactsovertheASRassimilationwithoutBC,suggestingaharmfulcloud-dependentbiaswasmitigatedbyincreasingobservationerrorswithcloudeffect.ThesimilardevelopmentoftheASRassimilationintheoperationalglobaldataassimilationsystemisunderway.Asthefirststep,wecomparedASRobservationswithsimulationstounderstandthereproducibilityofourglobalmodelandradiativetransfermodels.TheminimizationprocedureisexaminedintheASRassimilationintheoperationalglobal4D-Varassimilationsystemandpreliminaryresultswillbepresented.

4.06 Evaluatingtheimpactofassimilatingcloud-affectedinfraredradiancesfromGOES-16ABIontheforecastofaseverestormintheMidwestU.S.Presenter:JonathanGuerrette,NCAR,MMMAuthors:Jonathan(JJ)Guerrette,Zhiquan(Jake)Liu,ChrisSnyderConstrainingaconvection-permittingatmosphericmodelaroundthetruthrequiresadataassimilationapproachthatusesobservationsthatresolveconvectionandmicrophysicsatrelevantscalesandconsidersflow-dependentmodeluncertainties.InthisworkweuseradiancesproducedbytheAdvancedBaselineImager’s(ABI)threewater-vapor(WV)sensitiveinfraredchannelsonboardtheGOES-16satellite.TheABIWVchannelsrecordanewfull-diskimageevery15minuteswithnominal2kmresolutionatnadir.TheradiancesaresimulatedusingtheCommunityRadiativeTransferModel(CRTM)version2.3.Weuseahybrid3DEnVartechniquewith3kmgridspacingandaflow-dependentmodelerrorrepresentationintheWeatherResearchandForecastingModelDataAssimilationsystem(WRFDA).Ourstudyfocusesonaseverestormwithverifiedreportsoftornadoesandhailon1May2018,whichimpactedabroadregionspanningcentralKansastoeasternNebraska.Thestormsystemwasinitiallypredictedwithrelativelygoodlead-timebyoperationalconvection-permittingforecasts.WeinvestigatewhethertheABIradiancescanimprovethespatialpositioningofthemostintenseregionsofthestormwhilealsomaintainingorincreasingforecastlead-time.InadditiontohourlyGlobalTelecommunicationSystem(GTS)and6-hourlyGlobalNavigationSatelliteSystemRadioOccultation(GNSSRO)observations,acontrolhourlycyclingexperiment(CLRSKY)assimilatesABIpixelsthathavepassedanonlineIR-onlycloud-detectionalgorithminWRFDA.AseparateALLSKYexperimentadditionallyusesABIpixelsaffectedbyhydrometeors,whicharecurrentlyexcludedfromoperationalforecastsystems.ALLSKYusesanobservationerrorinflation(OEI)mechanismthatscaleslinearlywithacloudinessparameterevaluatedforeachpixel.OEIenablescloudypixelstobeusedthatwouldotherwisefailbackgrounderrorqualitychecksordegradetheDAanalysis.Bothexperimentsarecycledfor24hoursbeginningat00Z,20hoursbeforethefirstreportedhailand22hoursbeforethefirstreportedtornado.Wewilldiscusstheimpactsofthecloud-affectedradiancesonforecastedspatialandphasedistributionofwatermassandforecastverificationstatisticsthroughoutbothexperiments.Finally,wewillassesswhethertheseobservationscanimprovethepredictabilityofthisNorthAmericancontinentalseverestorm.4.07 4Dvariationalandensemble/variationalassimilationofevery10-minAHIclear-skyandall-skyradiancesatconvective-scalePresenter:ZhiquanLiuAuthors:ZhiquanLiu,YaliWu,andDongmeiXuHimawari-8AHIradiancedataassimilation(DA)isimplementedinNCAR’scommunityWRFDataAssimilation(WRFDA)system,whichallowstheassimilationofhightemporal-andspatial-resolutionAHIdatausingvariousschemessuchas3DVAR,multi-resolutionincremental4DVAR(MRI-4DVAR),andhybrid-3D/4DEnVaratconvective-scale.3DVARandMRI-4DVARexperimentswithandwithoutAHI’sthreewatervapor(WV)channels’clear-skyradianceswereconductedtoevaluatetheimpactonthepredictionofarecord-breaking(500mmwithin24hours)warm-sectortorrentialrainfall(WSTR)eventoccurredinGuangzhoucityon7May,2017.WRFandWRFDAisconfiguredwitha3-kmgridspacingtobetterresolvethisverylocalstormevent,forwhichalloperationalcentersfailedtopredictitstiming,location,andintensity.While3DVARexperimentwithorwithoutAHIislessskillfultopredictthisevent,4DVARwithoutAHIprominentlyimprovedconvectioninitiation(CI)forecastandextraevery10-minAHIradiancedatausedin4DVARimprovedthesecond-stageconvectionevolutionandprecipitationforecasts.Forthefractionalskillscores(FSS)of20-haccumulatedprecipitationforecasts,4DVARwith10-minAHIradiancesimproved2%–4.5%,1%–3%,6%–20%,and8%–24%for5mm,20mm,50mm,and80mmthresholds,respectively,comparingto4DVARwithoutAHI.Morerecentdevelopmentallowstheassimilationofall-skyAHIradiancesbyintroducingtheso-called“symmetricerrormodel”followingOkamotoetal.(2014)andHarnischetal.(2016),scatteringCRTM,andcloudanalysisvariables.All-skyAHIradianceassimilationimpactisevaluatedusingWRFDA’shybrid-3D/4DEnVartechniquesat3-kmgridspacingforseverestormeventsoverChina.Comparisonresultsofhybrid-3DEnVarvs.hybrid-4DEnVar(with10-minAHIdata)andclear-skyvs.all-skyAHIdatawillbepresented.

Session5:All-skyAssimilationforMicrowaveSensors(oralpresentations)5.01 Towardsall-skyMHS:ObservationPreprocessingandNWPSuiteDesignPresenter:BrettCandy,UKMetOfficeAuthors:BrettCandyandStefanoMiglioriniCurrentlyattheMetOfficeweareworkingonextendingtheassimilationofmicrowavehumiditychannelsintoregionswherethereissignificantscattering.Forthe183GHzchannelstheprimarysourcesofscatteringarecirruscloudsandraindroplets.Toaccountforscatteringinourschemerequiresbothimprovementstotheradiativetransfermodelandalsothepreprocessingofthedata,especiallythe1D-Varcomponent.Inthispresentationwewilllookatbothaspects.Weshowthat1D-Varcontinuestoplayanimportantpartinthequalitycontrolprocessandwilldiscusshow1D-Varhasbeenaugmentedtoincludeinformationonicecloudsandrain.Anotherimportantaspectofthedataassimilationschemethathasbeentestedinpreparationfortheoperationaluseofall-skyMHSistheuseofouterloopcyclingwithin4D-Varminimisation.Weshallgiveanoverviewofoursuitedesignandpresentsomeinitialstudies,includingexaminingtheroleofqualitycontrolastheouterloopcycleproceeds.5.02 AssimilatingcloudyandrainymicrowaveobservationswithintheARPEGEglobalmodelPresenter:PhilippeChambon,Météo-FranceAuthors:PhilippeChambon,MarylisBarreyat,Jean-FrançoisMahfoufWithintheARPEGE4D-VarglobaldataassimilationsysteminoperationsatMétéo-France,onlyclear-skymicrowaveobservationsarepresentlyused.Anewframeworkiscurrentlyunderinvestigationtoassimilateaswellmicrowaveobservationsincloudyandrainyareas.Thismethodiscalled1D-Bay+4D-Varandcorrespondstoatwo-stepprocess:(i)aBayesianinversionalgorithmtoretrieveprofilesoftemperatureandhumidityfromthemicrowaveradiances,(ii)the4D-Varassimilationoftheseretrievedprofiles.The1D-Bay+4D-Varmethodisanalternativetoboth1D-Var+4D-Varanddirectall-skyassimilation.Withinthisframework,anewerrormodelhasbeendevelopedaimingatmodelingradiativetransfererrorsinscatteringconditions,basedonanensembleofforwardsimulationswithmultiplemicrophysicalassumptions.ResultsfromtheassimilationofSAPHIRandMHSradiancesincloudyskyoverathree-monthperiodwillbepresented.Inparticular,theimpactsofthesemicrowavesoundersdataontropicalwindsandhurricaneforecastswillbediscussed.5.03 All-skyassimilationoverlandforsurfacesensitivemicrowavechannelsPresenter:KatrinLonitz,ECMWFAuthors:KatrinLonitz,AlanJ.GeerandNielsBormannTheall-skyassimilationofmicrowaveradiancesoveroceanhasasignificantpositiveimpactonforecastscoresatECMWF.Overlandonlychannelswhichhavesmallsensitivitiestothesurfaceareassimilated,thatishigher-peaking183GHzhumiditysensitivechannels.Themaindifficultyinassimilationofsurfacesensitivechannelsistheaccuracyinthesimulatedbrightnesstemperaturesduetouncertaintiesintheemissivityandskintemperatureoverland.All-skyassimilationaddsthedifficultyofseparatingerrorsincloudandprecipitationfromthoseinthedescriptionofthesurface.Inthisstudy,wetestifthecurrentdynamicemissivityretrievalcouldbeusedtoassimilateadditionalsurfacesensitivemicrowavesoundingandimagerchannels(e.g.150/166GHzor89/92GHz)withtheaimofassessingtheirimpactonforecastscoresintheIntegratedForecastSystem.Furthermore,weassessthespectralvariabilityofemissivitywithintheall-skyframeworkdependingontimeofday,oncloudinessandonlandcovertypeforconicalmicrowavescanners.Atthemomenttheemissivityretrievalsperformedforthe183GHzchannelsareonlyusedatlightcloudsituations;inverycloudysituationstheatlasvaluesareused.Wetestifaregressionofoneretrievalatacertainfrequencycanbeusedformostsurfacesensitivemicrowavechannelsinallcloudconditions.

5.04 All-skyradianceassimilationoverlandatNCEP:ApproachesandStatusPresenter:YanqiuZhuAuthors:YanqiuZhuBothclear-skyandcloudyradiancesfromAMSU-AoveroceanFOVshavebeenassimilatedoperationallyatNCEPsince2016.Astheall-skyapproachisextendedtotheATMSradiancesoveroceanFOVswiththeimplementationoftheFV3GFS,itisnaturaltoextendtheefforttotheradiancesoverland.Sofar,onlyclear-skyradiancesareassimilatedoverlandandfarfewerradiancesareusedthanoveroceanatNCEP.Thechallengeswearefacingintheassimilationofsurface-sensitivechannelsoverlandmainlycomefromtheuncertaintyinsimulatingmicrowavelandemissivity,theuncertaintiesoflandsurfaceproperties,andtheproblematicclouddetectionintheclear-skyradianceassimilationqualitycontrol.Toaddressthefirsttwoissues,ourintermediategoalistoproducereal-timeanalyticalemissivityretrievalcombinedwithTELSEMatlas(withorwithoutfilterupdate),andthelong-termgoalistoperformsoilmoistureandlandsurfaceskintemperatureanalyseswiththeimprovedcommunitysurfaceemissivitymodel(CSEM)usingradiancesfromlow-frequency(e.g.L-band)microwavesatellitesensors,suchasAMSR2,SMOS,GMI.Majorstepstowardsall-skyradianceassimilationoverlandwillbediscussedandthepreliminaryresultsofanalyticalemissivityretrievalfromGSIwillbepresentedatthemeeting.Session5:All-skyAssimilationforMicrowaveSensors(posterintroductions)5p.01 All-skyassimilationofmoisture-sensitiveradiancesattheMetOfficePresenter:StefanoMigliorini,MetOfficeAuthors:StefanoMiglioriniandBrettCandyCloud-affectedradiancesfromAMSU-Achannel4and5havebeenassimilatedintheMetOfficeoperationalweatherpredictionsystemsinceSeptember2018.ThisfollowedfromtheresultsoftrialexperimentswhichshowedRMSEreductionsin500hPageopotentialheightforecastsofabout1%uptoday2,whenincludingnon-precipitatingscenesobservedinthesechannels.Thenextstepistoincreasetheamountofassimilatedobservationsbyincludingcloud-affectedradiancesfromMHSthataresensitivetohumidityinthe183GHzband,bothintheglobalandtheregionalversioncentredintheUKoftheUnifiedModel.Tothisend,theall-skyradianceerrormodelusedforAMSU-Aradiances,dependentontheliquidwaterpath(LWP)estimatedfromitschannels1and2,hasbeenmodifiedtomakeitdependentontheLWPandicewaterpath(IWP)estimatedfromthe1D-Varanalysesroutinelyperformedforqualitycontrolpurposes,beforeassimilation.Resultsfromsingle-observationaswellasfromtrialexperimentsarediscussedinthisthistalk.Resultsarealsoshownfromatrialexperimentwherethebenefits(aswellasitsadditionalcosts)fromiteratingthe4D-Varprocedurewithupdatedlinearizationstates(knownasouter-loopminimization)arediscussed.Finally,astrategytoextendtheMetOfficeall-skyradianceassimilationproceduretoprecipitation-affectedradiancesisoutlined.5p.02 AssimilationofAMSU-AinthepresenceofcloudandprecipitationPresenter:NielsBormann,ECMWFAuthors:PeterWeston,AlanGeerandNielsBormannAtECMWF,AMSU-Aobservationsarecurrentlyonlyassimilatedinclear-skyconditions,i.e.withouttakingintoaccounttheeffectofcloudandprecipitationonthemeasurements.Inrecentyearsithasbeenshownthatassimilatinghumidity-sensitivemicrowaveradiancesinthepresenceofcloudandprecipitationcanleadtosignificantincreasesinforecastskill.Inthispostertheimpactofextendingtheexistingclear-skyassimilationoftemperature-soundingAMSU-Aradiancestocloudyareasisinvestigated.Anumberofdevelopmentstotheall-skyAMSU-Aconfigurationhavebeenresearchedandtestedleadingtoasignificantimprovementonpreviousresults.Thesedevelopmentshaveincludedchangingtheinterpolationofmodelfieldstoobservationlocations,thinningrefinements,additionalqualitycontrolandimprovedbiascorrection.Eachofthesedevelopmentsleadstoslightlyimprovedresultsbutwhencombinedtheyleadtoasignificantimprovement,addressingmanyoftheareasofdegradationinpreviousresults.Theperformanceoftheall-skyAMSU-Aconfigurationisnowveryclosetothecurrentlyoperationalclear-skyAMSU-Aconfigurationwithimprovementstoextra-tropicalshort-rangehumidityandlow-levelwindforecastsalthoughtherearestillsomesmallareasofdegradationtotropicaltemperatureandwindforecasts.Anumberofpossiblefutureenhancementswillalsobesummarised.

5p.03 Theuseofprecipitation-affectedMWradiancesinFV3-GFSHybridDataAssimilationSystemPresenter:BenjaminJohnson,UCAR/JCSDA(forEmilyLiu)Authors:EmilyHuichunLiu,AndrewCollard,DarylKleistTheoperationalFV3-GFShybriddataassimilationsystemcurrentlyassimilatescloud-affectedmicrowave(MW)radianceswiththeassumptionthatthecloudyscenesareovercastandtheprecipitation-affectedobservatonareexcluded.Inpreparationforassimilatingprecipitation-affectedMWradiances,theperformanceoftheobservationoperatorunderscatteringconditionbyhydrometeorshasbeenvalidatedandimproved.Atwo-columnradiancecalculationwasdevelopedintheobservationoperatortohandlefractionalcloudcoverage.Thevalidationandenhancementmadefortheoperationaloperatorwillbedescribedindetailsinthepresentationfollowedupbythediscussionontheimpactoftheprecipitation-affectedMWradiancestotheanalysisandtheforecast.5p.04 Updatesfromtheall-skyassimilationofmicrowaveradiancesattheECMWFPresenter:KatrinLonitz,ECMWFAuthors:AlanJ.GeerandKatrinLonitzWepresentsomehighlightsfromrecentupdatestotheall-skyassimilationofmicrowaveradiancesattheECMWF.

• Changestoconsiderinterchannelcorrelationintheerrormodelofmicrowaveimagers.Fortheall-skyassimilationithasbeenfoundthatafully-specifiedcovariancematrixthatadaptswiththecloudamountwasneededforthispurpose.Wefindthatthetuningoftheeigenvectorsandtheinterplaywithvariationalqualitycontroliskeytoasuccessfulassimilationconsideringcorrelatedobservationerrors.

• InthelatestECMWFIFSmodelcycleSSMIS-F17150hGHzandGMI166v/hGHzchannelshavebeenadded.Thisimprovesmedium-rangehumidityforecastsanddecreasestheadditionaldryingthroughtheassimilationofmicrowaveimagersby50%.

• RTTOV-SCATThasbeenupdatedtoversion12.2inthelatestECMWFIFSmodelcyclealongwiththerestofRTTOV.ThisupdateaddstheARTSscatteringdatabasewith16shapesincludingaggregates,hailandgraupelandcoveringfrequenciesfrom1to886GHztosupportallmicrowave/sub-mmbands:SMOStoICI.Furthermore,anewliquidwaterpermittivitymodel(Rosenkranz,2015)isnowaspartofRTTOV-SCATT.

5p.05 All-skyAssimilationoftheMWHS-2ObservationsandEvaluationtheImpactsontheForecastsofTyphoonsPresenter:ZhipengXian,InstituteofAtmosphericPhysics,ChineseAcademyofSciencesAuthors:ZhipengXian,KeyiChen,JiangZhuSatellitedataassimilationistransitioningfromclear-skytoall-skyapproachatsomeoperationalforecastingcenters;theall-skyapproachdirectlyassimilatesobservationsunderclear,cloudyandprecipitatingconditionsandshowsapositiveimpactonmedium-rangeforecasts.Toexaminetheimpactofall-skyassimilationoftheMicrowaveHumiditySounder-2(MWHS-2)datafromFengYun(FY)-3Conthehigh-impactweatherprocess,suchastropicalcyclone,threeexperiments(withoutMWHS-2data,withMWHS-2datainclear-skyconditionsandwithMWHS-2datainall-skyconditions)arecarriedout,andseveraltyphoons(i.e.,TyphoonsHaitang,Nesat,Nida,HatoandMangkut)havingbadinfluencesonSouthernChinaareselected.RTTOV-SCATT,afastRadiativeTransferModelforsimulatingcloud-andprecipitation-affectedmicrowaveradiances,andasymmetricobservationerrormodelforall-skyradianceassimilationareimplementedwithintheWeatherResearchandForecastingmodeldataassimilationsystem(WRFDA)anditsthree-dimensionvariationaldataassimilationschemeisusedforallexperiments.Theresultsshowthattheall-skyassimilationmakestheaverageerrorintrackoftyphoonsreduced17.9%and9.6%,andmakesabetterperformanceontheforecastofintensityoftyphoon.Inaddition,theforecastofheavyrainfall(100mm)causedbythesetyphoonsareimprovedgreatlywithmorecloud-andprecipitation-affectedobservationsbeingassimilated.Theseencouragingresultssuggestthatall-skyassimilationisabletoimprovetheforecastsoftyphoons.5p.06 All-skymicrowaveradianceassimilationintheJMAglobalNWPsystemPresenter:MasahiroKazumori,JapanMeteorologicalAgencyAuthors:MasahiroKazumori,TakashiKadowakiandHiroyukiShimizuAll-skymicrowaveradianceassimilationhasbecomeincreasinglyimportanttoproduceaccurateinitialfieldsfornumericalweatherprediction.All-skymicrowaveradianceassimilationcanprovidenewobservational

informationundercloudyconditions.Wedevelopedanall-skymicrowaveradianceassimilationschemefortheJMAglobal4D-Vardataassimilationsystem.Adatascreeningmethodappliedinforecastmodel’sbiasedareas,observationerrorassignmentmethodbasedoncloudamountandouter-loopiterationsfortrajectoryupdatesinthe4D-Varminimizationprocesswereintroducedinthesystem.Microwaveradiancedata(e.g.microwaveimager,AMSR2,GMIandSSMIS,andmicrowavehumiditysounder,MHS)wereassimilatedunderall-skyconditions.Toextendthedatacoverageintheanalysis,dataqualityofunusedinstruments(e.g.WindSatonCoriolis,MWRIonFY-3Band3C)wereevaluated.WefoundMWRIhadrelativelylargerorbitdependentbiasesintheFGdeparture(observedminussimulatedradiance)statistics.ForMWRIradiancedata,atypeofsatelliteorbitnode(ascendingordescending)asapredictorinavariationalbiascorrectionwascrucialtoobtainsimilardataqualityasothermicrowaveinstruments.Additionofthenewmicrowaveradiancedataintheall-skyassimilationbroughtfurtherimprovementsintheanalysisandforecasts.Theall-skymicrowaveradianceassimilationisplannedtobeincorporatedintheJMAoperationalglobaldataassimilationsysteminNovember2019.Thedetailswerepresentedintheconference.5p.07 AssessingtheimpactofdifferentliquidwaterpermittivitymodelsontheassimilationofmicrowaveradiancesPresenter:KatrinLonitz,ECMWFAuthors:KatrinLonitzandAlanJ.GeerPermittivitymodelsformicrowavefrequenciesofliquidwaterbelow0°C(supercooledliquidwater)arepoorlyconstrainedduetolimitedlaboratoryexperimentsandobservations,especiallyforhighmicrowavefrequencies.Thisuncertaintydirectlytranslatesintoerrorsinretrievedliquidwaterpathsofupto80?%.UsingtheECMWFall-skyassimilationframeworkwehavetestedhowdifferentliquidwaterpermittivitymodelsimprintthemselvesonsimulatedbrightnesstemperatures.Thecurrentpermittivitymodeliscomparedwithfivealternatives.ThelargestdifferencesoccurprominentlyduringaustralwinterinthestormtracksoftheSouthernHemisphereandintheintertropicalconvergencezonewithvaluesofaround0.5to1.5?K.Comparedtothedefaultapproach(Liebe,1989),improvedfitsbetweenobservedandsimulatedbrightnesstemperaturesarevisibleforthepermittivitymodelsofStogrynetal.(1995),Rosenkranz(2015)andTurneretal.(2016).Forthesemodels,asmallmeanreductioninsimulatedbrightnesstemperaturesofatmost0.15?Kat92?GHzcanbeseenonaglobalmonthlyscale.Incyclingdataassimilationthesenewermodelsalsogivesmallimprovementsinshort-rangehumidityforecastswhenmeasuredagainstindependentobservations.Overall,Rosenkranz(2015)isfavouredduetoitsvalidityupto1?THz,whichwillsupportfuturesubmillimetremissions.5p.08 MicrophysicalpropertiesoficeparticlesasrevealedbysatellitemicrowavepolarimetricmeasurementsandradiativetransfermodelingPresenter:VictoriaGalligani,CIMA-CONICETAuthors:V.S.Galligani,D.WangandC.PrigentTheunderstandingofcloudmicrophysicalprocessesandtheirrepresentationinclimatemodelsneedstobeurgentlyimproved.Frozenandmixedphasecloudprocesses,inparticular,arethemostpoorlyunderstood.Themeasurementofthesecloudsaredifficulttoobtainowingtothechallengesinvolvedinremotelysensingicewatercontent(IWC)anditsverticalprofile,includingcomplicationsassociatedwithmulti-levelclouds,mixedphasesandmultiplehydrometeortypes,theuncertaintyinclassifyingiceparticlesizeandshapeforremoteretrievals,therelativelysmalltimeandspacescalesassociatedwithdeepconvection.Microwaveradiometryhasshownapromisingabilityinthecharacterizationoffrozenparticles,asitisabletopenetrateandprovideinsightintotheverticalprofilesofmostclouds,incontrasttoinfraredandvisibleobservations,whichessentiallysensecloudtops.Microwaveobservations,speciallyatthehigherfrequencychannels(37GHz),aresensitivetocloudscatteringsignals.Thehigherthemicrowavefrequency,thelargerthescatteringsignalproducedbytheinteractionoffrozenhabitswithEMradiation.Theknowledgeregardingthemicrophysicalpropertiesoffrozenhabitsresponsibleforsuchscattering(size,density,shape,orientation,composition)iskeyinradiativetransferandclimatemodellingandneedstobefurtherdiscussed.Thedifferenceintheverticallyandhorizontallypolarizedmicrowavemeasurements(TBV-TBH)atcloudscatteringsensitivefrequencieshavebeenshowntocontaininformationoniceparticleshape(aspectratio)

andorientation.ThelaunchoftheGlobalPrecipitationMeasurement(GPM)satellitein2014,whichhoststheGPMMicrowaveImager(GMI),hasextendedtheavailabilityofmicrowavepolarizedobservationsathigherfrequencies(166GHz),previouslyonlyavailableupto89GHzinplatformssuchasAMSR-EorTMI.Scatteringbyfrozenhabitsishighly(poorly)polarizedinstratiformandanvilclouds(deepconvection)resultingfromthehorizontalorientationofnon-sphericalfrozenhabits(randomorientationduetoturbulenceandstrongupdrafts),asshowninapre-GPMerabye.g.,Prigentetal.,2005,Galliganietal.,2013,Deferetal.2014,andconfirmedbyGongandWu2017whoexploredGPM’snovel166GHzpolarizedchannels.Inthisstudy,weanalyzedoneyearofGMIobservationsattwowindowchannels(i.e.,89and166GHz).Stratiformcloudsshowlargerpolarization(upto10Kinaverage)duetothedepositionandaggregationgrowthofsnowflakes,whiletheconvectiveregionsshowsmaller(evennegative)polarization,asthegraupeland/orhailbecomerandomly(evenvertically)orientedduetothestrongupwardairmotion.Arobustrelationshiphasbeenfoundbetweenthepolarizationdifferenceandverticalpolarizedbrightnesstemperatureforbothlandandoceansurfaces,andisparameterizedusingHermitecubicsplineinterpolationwhichcanbeeasilyincorporatedintoradiativetransfermodels.Theregionalandseasonalvariabilityhasalsoinvestigatedbetweendifferentcloudregimes.Inordertosupportthesestatistics,sensitivitytestsareperformedusingaradiativetransfer(RT)robustmodelingframeworkforadeepconvectioncasestudyinthehighlysevereweatherproducerSoutheasternSouthAmericanregion.TheAtmosphericRadiativeTransfer(ARTS)modeliscoupledwiththeWeatherandResearchForecasting(WRF)modeltoexplorethesensitivityofpolarizedsignalstofrozenhabitmicrophysicsparameterssuchasaspectratio,orientationordensity.ThedeepconvectionmidlatitudecasestudyexploitscoincidentGMI-DPRobservations,aswellasgroundradarpolarimetricdata,andsupportsphysicallytherelationshipsparametrizedfromGMIglobalobservations.Communicationfromsiblingworkinggroups(oralpresentations)a.01 ResearchHighlightsfromtheInternationalPrecipitationWorkingGroup(IPWG)Presenter:PhilippeChambon,Météo-FranceAuthors:PhilippeChambon,VivianaMaggioni,ZiadHaddad,Dong-BinShin,VincenzoLevizzani,RalphFerraroTheInternationalPrecipitationWorkingGroup(IPWG)isapermanentInternationalScienceWorkingGroup(ISWG)oftheCoordinationGroupforMeteorologicalSatellites(CGMS),co-sponsoredbyCGMSandtheWorldMeteorologicalOrganization(WMO).IPWGprovidesaforumfortheinternationalscientificcommunitytoaddressissuesandchallengesrelatedtosatellite-basedprecipitationretrievals.Throughpartnershipsandbiennialmeetings,thegrouppromotestheexchangeofscientificandoperationalinformationbetweentheproducersofprecipitationmeasurements,theresearchcommunity,andtheusercommunity.Specifically,IPWGfurtherstherefinementofcurrentestimationtechniquesandthedevelopmentofnewmethodologiesforimprovedglobalprecipitationmeasurements,togetherwiththevalidationofthederivedprecipitationproductswithground-basedprecipitationmeasurements.IPWGpromotesinternationalpartnerships,providesrecommendationstotheCGMS,andsupportsupcomingprecipitation-orientedmissions.ThispresentationwillhighlightsomeofthelatestresearchfindingsfromtheIPWGworkinggroups.a.02 FeedbackfromlasttwoISWGmeetingsPresenter:BenjaminRuston,NavalResearchLaboratoryAuthors:BenjaminRustonSession6:Climate(oralpresentations)6.01 Establishingtimesseriesofessentialclimatevariablesfrom3successiveMetop/IASIPresenter:CyrilCrevoisier,LMD/CNRSAuthors:C.Crevoisier,R.Armante,V.Capelle,A.Chédin,N.A.Scott,C.Stubenrauch,L.Crépeau,J.PerninSinceitsfirstlaunchonboardMetop-AinOctober2006,andthenonboardMetop-BinSeptember2012andMetop-CinNovember2018,IASIcontributestotheestablishmentofrobustlong-termdatarecordsofseveralessentialclimatevariables.Owingtoitslaunchesonboard3successiveMetopplatforms,IASIhasthepotential

tomonitortheevolutionofthesevariablesovertwodecades,toassesspotentialtrends,andtodetectsignaturesofspecificclimateevents,suchasENSOorothersourcesofclimatevariability.Tofulfilltheseobjectives,itismandatorythateachsuccessiveIASIinstrumentsarespectrallyandradiometricallywellcharacterizedindividuallyandamongthemselves.WewillshowthattheIASIinstrumentsdisplayexceptionalspectralandradiometricstabilities,andthatthe3instrumentsagreeatthelevelrequiredforclimatemonitoring.Resultswillbepresentedatbothlevel1andlevel2.Forlevel1,wewillrelyonIASIradiancemonitoringandintercomparisonwithcompanioninstrumentsdoneintheframeworkoftheGlobalSpace-basedInter-CalibrationSystem(GSICS)ofWMO.Forlevel2,wewillfocusonfouressentialclimatevariablesthatareretrievedatLMD:(i)clouds:physicalandmicrophysicalproperties;(ii)greenhousegases:mid-troposphericintegratedcontentofCO2,CH4andCO;(iii)dustaerosols:AOD,altitude,andradius;(iv)continentalsurfacecharacteristics:skintemperatureandspectralemissivity.UsewillbemadeonaprocessingchainofsatelliteobservationsthathasbeendevelopedformanyyearsatLMDandthatincludes:permanentvalidationandimprovementoftheGEISAspectroscopicdatabaseandoftheradiativetransfercode4A(whicharerespectivelytheofficialdatabaseandcodeforIASICal/ValactivitiesatCNES),developmentofdedicatedcloudandaerosoldetectionschemes,retrievalprocesses,andvalidationactivities.AsthesuiteoflongtimeseriesofclimatevariablesretrievedfromIASIcontinuestoexpand,wewillarguethatIASIhasalreadydemonstratedthatitcanandwillplayamajorroleinthemonitoringandunderstandingofclimateevolutionandvariability.6.02 TowardImprovedClimateDataRecordUsingStableSNPP/ATMSObservationsasReferencesPresenter:Cheng-ZhiZou,NOAACollegeParkAuthors:Cheng-ZhiZou,XianjunHao,HuiXu,andMitchGoldbergObservationsfromthesatellitemicrowavesoundersplayavitalroleinmeasuringthelong-termtemperaturetrendsforclimatechangemonitoring.Changesindiurnalsamplingovertimeandcalibrationdrifthavebeenthemainsourcesofuncertaintiesinthesatellitemeasuredtemperaturetrends.WehaverecentlyexaminedobservationsfromtheAdvancedTechnologyMicrowaveSounder(ATMS)thathasbeenflyingonboardtheNOAA/NASASuomiNationalPolar-orbitingPartnership(SNPP)environmentalsatellitesincelate2011.TheSNPPsatellitehasastableafternoonorbitthathasclosetothesamelocalobservationtimeasNASA’sAquasatellitethathasbeencarryingtheheritagemicrowavesounder,theAdvancedMicrowaveSoundingUnit-A(AMSU-A),from2002untilthepresent.Thesimilaroverpasstimingnaturallyremovesmostoftheirdiurnaldifferences.Inaddition,directcomparisonoftemperatureanomaliesbetweenthetwoinstrumentsshowslittleornorelativecalibrationdriftformostchannels.OurresultssuggestthatbothATMSandAMSU-Ainstrumentshaveachievedabsolutestabilityinthemeasuredatmospherictemperatureswithin0.04Kelvinperdecade.WehavealsoanalyzedAMSU-AobservationsonboardtheEuropeanMetOp-Asatellitethathasastablemorningorbit8hoursapartfromtheSNPPoverpasstime.Theircomparisonrevealslargeasymmetrictrendsbetweendayandnightinthelower-andmid-tropospherictemperaturesoverland.ThehighradiometricstabilityintheSNPP/ATMSandMetOp-A/AMSU-Aobservationshasbroadimpactontheclimatetrendobservationsfromthemicrowavesoundersaswellasotherinstrumentsandcouldhelpresolvedebatesonobserveddifferencesintheclimatetrends.Inthispresentation,weusestableobservationsfromtheSNPP/ATMSandMetOp-A/AMSU-AasreferencestorecalibratemicrowavesoundersonboardothersatellitessuchasMetOp-BandNOAAPOESseries.Wedemonstratethatdataqualityandconsistencyfromthesesatelliteobservationscanbelargelyimprovedthroughrecalibration.Suchrecalibrateddatawillbeusedtogeneratemicrowaveatmospherictemperatureclimatedatarecord(CDR).ImprovedCDRandtrendaccuracyareexpectedfromsuchanapproach.6.03 Validationofthe183GHzC3S/EUMETSATFCDRusingERA5simulations,SNOsandoperationaldatasetsPresenter:ChristoforosTsamalis,MetOfficeHadleyCentreAuthors:C.Tsamalis,E.Good,R.King,F.Aldred,T.Hanschmann,V.O.JohnandR.RoebelingThe183GHzmeasurementsfrommicrowave(MW)soundersconstitutenecessaryhumidityinformationforbothassimilationtoreanalysesandcreationofThematicClimateDataRecords(TCDRs).Forthisreason,anewFundamentalClimateDataRecord(FCDR)hasbeendevelopedbyEUMETSATfortheCopernicusClimate

ChangeService(C3S).ThisFCDRhasbeengeneratedbasedonthemethodologydevelopedbytheFiduceoprojectprovidingtheobservationsinNetCDFfilesfromequatortoequator,thusinaneasyfileformattohandlewhileavoidingthepossibleduplicationsofoperationaldatasets.Also,itprovidesuncertaintiesperpixelinthreecategories:independent(orrandom),structured,andcommon(orsystematic).TheFCDRincludesmeasurementsfromtheMicrowaveHumiditySounder(MHS)onboardtheMetOp-AandBsatellites,theMicrowaveHumiditySounder(MWHS-1/2)onboardtheFY-3A,-3B,and-3CsatellitesandtheAdvancedTechnologyMicrowaveSounder(ATMS)onboardtheS-NPPsatellite.ItcoverstheperiodOctober2006toDecember2018,thusextendingovertimetheFCDRprovidedbyFiduceoandmoreimportantlyincludingnewsounderslikeMWHS-1,MWHS-2andATMSwhichpermitanenhancedsub-dailycoverageoftheEarth.TheFCDRisvalidatedusingthreeapproaches:i)ERA5simulations,ii)SimultaneousNadirOverpasses(SNOs)andiii)comparisonswiththeoperationaldatasets.TheERA5reanalysisbrightnesstemperature(BT)simulationshavebeenperformedwiththeNWPSAFRadSimsoftware,whichconstitutesaninterfacetotheRTTOVfastradiativetransfermodel.ThevalidationwithERA5hasbeenrestrictedoveroceanonlywithin60°Sto60°Ntoavoidthecomplicationswithlandandicesurfaceemissivities.It’sworthnotingthatERA5assimilatesthe183GHzobservationsfromMHS,MWHS-1/2andATMS.CriteriausedtogenerateSNOsweretimedifferencewithin5min,FieldofView(FOV)distancewithin5kmandsatellitezenithangledifferenceswithin5°.SNOshavebeencalculatedforallthepotentialsatellitepairsoftheFCDR,butalsowithSondeurAtmosphériqueduProfild’HumiditéIntertropicaleparRadiométrie(SAPHIR)MWsounderonboardtheMegha-Tropiquessatellite,whichpermitsanindependentvalidation.ThethirdapproachincludestheintercomparisonwithoperationaldatasetsprovidedbyNOAACLASSforMHSandATMSandbyCMAfortheMWHS-1and2sounders.FirstresultsindicatethatMHSandATMShaveastableperformanceovertimeandagreeratherwellwithERA5simulationsandbetweenthemasshownfromSNOsanalyses.TheMWHS-1/2instrumentspresentsomediscontinuitiesinthetimeseriesandlargerdiscrepanciesagainstERA5simulations,andwithSNOstoMHS,ATMSandSAPHIR.ThefirstindicationsarethatthenewFCDRismoreconsistentthantheavailableoperationaldatasets.Session6:Climate(posterintroductions)6p.01 Standalonenight-timeseasurfacetemperatureretrievedbytheIASI/Metopsuite:TowardlongtimeseriesPresenter:VirginieCapelle,LMD/EcolePolytechniqueAuthors:V.Capelle,J.-M.Hartmann,A.Chédin,R.Armante,H.Tran,N.Scott,C.CrevoisierSSTisoneoftheessentialclimatevariablesforwhichaccurateandglobalmeasurementsarecrucialforimprovingourunderstandingofclimateevolution,aswellasofnumericalweatherprediction.Withinthisframework,satelliteremotesensing,byprovidingdailyandglobalobservationsoverlongtimeseries,offersgoodopportunities.Inparticular,theexcellentcalibrationandstabilityoftheIASIinstrumentandtheplannedlongtimeseriesofobservationprovidedbythesuiteofthreesatellitesMetopA,BandC,launchedrespectivelyinOctober2006,September2012andNovember2018,isfullyconsistentwiththequalityrequirement.HereafullphysicalalgorithmispresentedtoretrieveSSTfromIASI.Themainadvantageofsuchanapproachistoprovideadatasettotallyindependentofin-situmeasurementsormodels.SSTisretrievedattheIASIspotresolution(clearsky)forthethreeMetopplatforms,offeringsofaracontinuousrecordovermorethan12years,whichisplannedtobeextendedforatleastanotherdecade.Restitutionsarecomparedwithin-situdriftingbuoysnetworkmeasurementsaswellaswithothersatellitedatasets.ThedifferencebetweentheskinsurfacetemperatureobtainedbyIRsatellitesandthein-situsurfacetemperatureprovidedbythebuoysisinvestigatedandthesensitivityofthisdifferencetotheconditionsofobservation(wind,watervaporcontent,etc…)isanalyzed.Finally,thestabilityoftherestitutionsamongthe3satellitesisalsoinvestigated.6p.02 AlongtimeseriesofMetop/IASIobservationsofSaharanaerosolsdistributionusingAOD-Altitude-SurfacetemperaturetripletsPresenter:VirginieCapelle,LMD/EcolePolytechniqueAuthors:V.Capelle,A.Chédin,R.Armante,N.Scott,C.CrevoisierAerosolsrepresentoneofthedominantuncertaintiesinradiativeforcing,partlybecauseoftheirveryhighspatiotemporalvariability,astillinsufficientknowledgeoftheirmicrophysicalandopticalproperties,oroftheirverticaldistribution.Observationsfromspaceofferagoodopportunitytofollow,daybydayandathigh

spatialresolution,dustevolutionatglobalscaleandoverlongtimeseries.Infraredobservationsallowretrievingdustaerosolopticaldepth(AOD)aswellasthemeandustlayeraltitude,daytimeandnighttime,overoceansandovercontinents,inparticularoverdesert.Therefore,theyappearcomplementarytoobservationsinthevisible.Byitsexcellentcalibrationandstabilityandtheexpectedlongtimeseriesofobservation,theInfraredAtmosphericSounderInterferometer(IASI),onboardthesuiteofEuropeanSatelliteMetopA,BandC,launchedrespectivelyinOctober2006,September2012andNovember2018isparticularlysuitedforaccuratemonitoringofdustevolution.Here,observationsfromthethreesatelliteshavebeenprocessedpixelbypixel,usinga“Look-Up-Table”(LUT)physicalapproach,providingatimeseriesofmorethan12yearsof10µmdustAODandmeanaltitude.Rigorousstatisticalanalysisofthewholetimeserieshasbeenperformedinorderto1)detectandestimateday-timeandnight-timedustAODtrendsoverSahara,aregionwheredustaerosolemissionsarefrequentandoftenintenseand2)demonstratethehighstabilitybetweenthedifferentsatellites.TakingbenefitoftheequalqualityofmorningandeveningIASImeasurements,wehavestartedcomparingthesetwosourcesofAOD-altitude-surfacetemperatureretrievaltripletsinordertobetterunderstandtheirdifferencesinrelationwiththeoftencomplexmeteorologicalsituationsencounteredintheseregions.Firstconclusionswillbediscussed.6p.03 Surfaceskintemperatureanditstrendfromrecent12-yearIASIobservationsPresenter:DanielZhou,NASAAuthors:DanielK.Zhou,AllenM.Larar,andXuLiuSurfaceskintemperaturehasbeenretrievedfromIASImeasurements.Monthlyandspatially-griddedsurfaceskintemperatureisproducedtoshowsomephenomenaofitsnaturalvariability,whichisalsoreflectedinthesurfaceemissivityand/orsoilmoisturederivedfromthesametimeseriesofmeasurements.Theanomaliesofsurfaceskintemperatureareusedtoestimateitstrend.Errorestimationand/orevaluationhasbeenperformedanddiscussedtounderstandtheuncertaintyinthetrends.ThetrendofIASIglobalsurfaceskintemperatureiscomparedwiththatofNASAGISSglobalsurfaceairtemperature.Despitethephysicaldifferencesbetweensurfaceskinandairtemperatures,agreementisshownbetweenthesetwodatasetsindicatingconsistencyandglobalsurfacewarmingduringthepast12years.ThetrendofIASIglobalsurfaceskintemperaturereportsanapproximate0.034ºK/yr.increasehasevolvedduring2007–2019.Thiswarmingtrendismorepronouncedinthenorthernhemisphere.Retrieving,analyzing,andmonitoringsurfaceparametersfromsuchadvancedhyperspectralinfraredsounderswillcontinue.6p.04 RTTOVforaC3SprojectonearlysatellitedatarescuePresenter:PascaleRoquet,Meteo-France(forJeromeVidot)Authors:JeromeVidot,EmmaTurner,BrunaSilveira,PascaleRoquet,RogerSaundersandPascalBrunelIntheframeoftheCopernicusClimateChangeService(C3S),theproject311cLot1onrescuingearlysatellitedatahasbeenstartedinordertoevaluatethepossibilitytousetheobservationsfromthesesatellitesinthenextECMWFERA-6reanalysis.Inthisproject,weareinvolvedinstudyingthecapabilityofRTTOVtosimulatesomeofearlyinfraredandmicrowaveinstrumentsflyinginspaceinthe70sto90s.Afirstpartoftheprojectistousetheup-to-dateinformationontheinstrumentspectralresponsefunction(ISRF)orchannel’spass-bandtocalculatetheso-calledRTTOVcoefficients.FormostIRinstruments(HIRS-1to-4,MVIRI,IRIS-D,VTPR,MRIR,THIR,SSUandPMR)andMWinstruments(SMMR,SSM/T2,MSU,SSM/IandSSMI/S),RTTOVcoefficientsalreadyexistbutsomeofthemstillneedtobeconsolidated.Forexample,theISRFofIRIS-Dmaybeimprovedwhencomparingwithobservations.Additionally,coefficientsforinstrumentssuchasSIRS-Dwillbeprovided.Thesecondpartoftheprojectaimsatsimulatingalargesetofatmosphericprofiles(25000)inordertoindependentlyestimatetheaccuracyofRTTOVcoefficientsversusLBLmodels.ThethirdpartoftheprojectistoestimatetheRTTOVerrorsduetodifferentversionsofLBLmodel.6p.05 ClimateDataRecordsanduserserviceoftheEUMETSATSatelliteApplicationFacilityonClimateMonitoringPresenter:NathalieSelbach,DeutscherWetterdienstAuthors:NathalieSelbachonbehalfoftheCMSAFteamTheEUMETSATSatelliteApplicationFacilityonClimateMonitoring(CMSAF)generates,archivesanddistributeswidelyrecognizedhigh-qualitysatellite-derivedproductsandprovidesservicesrelevantforclimatemonitoring.TheCMSAFproductportfoliocoversClimateDataRecords(CDRs)forEssentialClimateVariables

(ECV),asrequiredbytheGlobalClimateObservingSystem(GCOS)implementationplaninsupportoftheUnitedNationsFrameworkConventiononClimateChange(UNFCCC).DuringthecurrentContinuousDevelopmentandOperationsPhase3(CDOP-3,2017-2022),severalnewFundamental-,Thematic-andInterim-CDRsarebeingreleased.Theyprimarilydescribepropertiesoftheatmosphericradiation,clouds,watervapourandprecipitationoverupto40years.Thus,usershaveaccesstomanyparametersoftheEarth’swaterandenergycyclebasedonoperationalsatelliteinstruments.Thetimeseriesofthecurrentlyavailableclimatedatarecordsrangefrom8tomorethan35yearswithaglobalcoveragefordatabasedonpolarorbitingsatellites,whilethosebasedongeostationarysatellitedatahavearegionalcoverage(currentlyMeteosatdisk).CMSAFisofferingCDRsgeneratedfromATOVS,AVHRR,SMMR,SSM/IandSSMISondifferentpolarorbitingsatellitesaswellasfromtheMVIRI,SEVIRIandGERBinstrumentsonboardtheMETEOSATseriesandsimilarinstrumentsonfurthergeoringsatellites.Furthermore,CMSAFwillfocusonprecipitationasanadditionalnewparameterinthecurrentprojectphase.TheseCDRsaremadeavailableviaawebuserinterfacewhichalsoallowsapplyingpost-processingprocedures,suchastheextractionofsub-areasorre-projection.ThiscontributionwillpresentcurrentlyavailableaswellasnewreleasesofCMSAFclimatedatarecords,plannedforCDOP-3.Itwillgiveageneraloverviewofthecurrentandplannedre-processingactivitiesattheCMSAF.TheconceptofprovidingTCDRsaslongtermdatarecordsinconnectionwithprovidingrelatedInterimClimateDataRecords(ICDRs)withashorttimelatencysuitableforclimatemonitoringapplicationswillbeshown.Inaddition,theoffereduserservicesofCMSAFwillbepresented.6p.06 TheEUMETSATCMSAFFundamentalClimateDataRecordofMicrowaveImagerRadiancesPresenter:NathalieSelbach,DeutscherWetterdienstAuthors:KarstenFennig,MarcSchröderThesatellitebasedHOAPS(HamburgOceanAtmosphereParametersandFluxesfromSatelliteData)climatologyprovidesclimatedatarecordsofprecipitation,evaporationandtheresultingfreshwaterfluxovertheglobalice-freeoceanbetween1987and2014.ThelatestversionofHOAPShasbeenreleasedbyCMSAFandisavailablefromtheCMSAFswebuserinterface(https://wui.cmsaf.eu/;DOI:10.5676/EUM_SAF_CM/HOAPS/V002).TheHOAPSclimatedatarecordsareprimarilybasedonpassivemicrowavemeasurementsfromtheSSM/I(SpecialSensorMicrowave/Imager)sensorfamily.InordertoderivereliablelongtermtrendestimatesoftheglobalwaterandenergycycleparametersitisstrictlynecessarytocarefullycorrectforallknownproblemsanddeficienciesoftheSSM/Iradiometersaswellastointer-calibrateandhomogenisethedifferentinstruments.Moreover,allappliedcorrectionsneedtobeclearlydocumentedtoprovideacompletecalibrationtraceabilityforaFundamentalClimateDataRecord(FCDR).Followingtheserecommendations,CMSAFreleasedaFCDRofSSM/Ibrightnesstemperatures(DOI:10.5676/EUM_SAF_CM/FCDR_SSMI/V001)in2013,freelyavailablefromthewebuserinterface(https://wui.cmsaf.eu/).ThisFCDRhasalreadybeenusedintheESACCISeaiceprojectandalsointhereanalysisERA5.InordertofurtherextendtheHOAPSdatasetintime,theSSM/IsuccessorinstrumentSSMIS(SpecialSensorMicrowaveImagerSounder)hastobeusedfrom2009onwards.CMSAFalsoreprocessedtheSSMISsensorsonboardF16,F17,andF18tothesamestandardsastheSSM/Idatarecordforthetimeperiod2006-2013andthecombinedFCDRwasreleasedin2015(DOI:10.5676/EUM_SAF_CM/FCDR_MWI/V002).Amongstothers,knowninstrumentissueslikesunlightintrusions,moonlightintrusions,andreflectoremissivityhavebeenaccountedforandthebrightnesstemperatureshavebeeninter-calibratedtotheSSM/IinstrumentseriestoallowaseamlesscontinuationofexistingTCDRs.InordertoextendtheavailableFCDRtothetimeperiodbeforetheSSM/Iepoch,CMSAFhasnowreprocessedavailablebrightnesstemperaturesfromtheSMMR(ScanningMultichannelMicrowaveRadiometer)onboardNimbus-7withthemainfocusontheinter-calibrationofthebrightnesstemperaturestotheSSM/Iseries,usingERA20casatransfertarget.There-processeddatarecord(DOI:10.5676/EUM_SAF_CM/FCDR_MWI/V003)isavailableinthesameuserfriendlydataformatasthe

existingFCDReditions.ThenewFCDRreleasealsoextendstheSSMISdatarecordwithtwoadditionalyears.Altogether,theFCDRnowspansthetimeperiodfrom1978to2015combiningobservationsfromthreedifferentplatformsSMMR,SSM/IandSSMIS.Thispresentationwillfocusontheinter-calibrationoftheSMMRandtheevaluationofthecombinedFCDRoverthecompletetimeperiod.Avalidationofthebrightnesstemperaturesisachallengingtaskastherearenoground-truthreferencemeasurementsavailableforthemicrowaveband.Hence,thehomogeneityoftheFCDRisevaluatedbyananalysisoftherelativebiasesbetweenthedifferentinstrumentsbeforeandaftertheinter-calibrationoffsetsareapplied.ItisplannedtoreleaseaneweditionoftheFCDRin2020,extendingtheSSMIScoveredtimeperiodtotheend2019.ItisalsoenvisagedtoimprovetheuncertaintycharacterizationandtolookatnewsensorslikeMWIandAMSR-3forfutureusage.6p.07 TowardsaclimatedatarecordofprecipitationmergingsatelliteobservationsbypassivemicrowavesoundersandimagersPresenter:NathalieSelbach,DeutscherWetterdienstAuthors:HannesKonrad,GiuliaPanegrossi,AnjaNiedorf,PaoloSanò,MarcSchröder,ElsaCattani,AnnaChristinaMikalsen,NathalieSelbach,RainerHollmannWithintheCopernicusClimateChangeService(C3S),theClimateDataStore(CDS)builtbyECMWFwillprovideopenandfreeaccesstoglobalandregionalproductsofEssentialClimateVariables(ECV)basedonsatelliteobservationsspanningseveraldecades,amongstotherthings.GivenitssignificanceintheEarthsystemandparticularlyforhumanlife,theECVprecipitationwillbeofmajorinterestforusersoftheCDS.C3Sstrivestoincludeasmanyestablished,high-qualitydatasetsaspossibleintheCDS.However,italsointendstooffernewproductsdedicatedforfirst-handpublicationintheCDS.Oneoftheseproductsisaclimatedatarecordbasedonmergingsatelliteobservationsofdailyandmonthlyprecipitationbybothpassivemicrowave(MW)soundersandimagers(SSMI/SSMIS)ona1°x1°spatialgridinordertoimprovespatiotemporalsatellitecoverageoftheglobe.TheMWsounderobservationswillbeobtainedusing,asinputdata,theFIDUCEOFCDRforAMSU/MHSinanewglobalalgorithmbasedonthePassivemicrowaveNeuralnetworkPrecipitationRetrievalapproach(PNPR;Sanòetal.,2015)developedspecificallyfortheproject.TheMWimagerobservationsbySSM/IandSSMISwillbeadoptedfromtheHamburgOceanAtmosphereFluxesandParametersfromSatellitedata(HOAPS;Anderssonetal.,2017),itselfbasedontheCMSAFSSM/IandSSMISFCDR(Fennigetal.,2017).Here,wepresentthestatusofthisproduct’sdevelopment.WecarryoutaLevel-2comparisonandobtainfirstresultsofthemergedLevel-3precipitationfields.Basedonthis,weassesstheproduct’sexpectedplausibility,coverage,andtheaddedvalueofmergingtheMWsounderandimagerobservations.Andersson,Axel;Graw,Kathrin;Schröder,Marc;Fennig,Karsten;Liman,Julian;Bakan,Stephan;Hollmann,Rainer;Klepp,Christian(2017):HamburgOceanAtmosphereParametersandFluxesfromSatelliteData-HOAPS4.0,SatelliteApplicationFacilityonClimateMonitoring,DOI:10.5676/EUM_SAF_CM/HOAPS/V002,https://doi.org/10.5676/EUM_SAF_CM/HOAPS/V002.Fennig,Karsten;Schröder,Marc;Hollmann,Rainer(2017):FundamentalClimateDataRecordofMicrowaveImagerRadiances,Edition3,SatelliteApplicationFacilityonClimateMonitoring,DOI:10.5676/EUM_SAF_CM/FCDR_MWI/V003,https://doi.org/10.5676/EUM_SAF_CM/FCDR_MWI/V003.Sanò,P.,Panegrossi,G.,Casella,D.,DiPaola,F.,Milani,L.,Mugnai,A.,Petracca,M.,andDietrich,S.:ThePassivemicrowaveNeuralnetworkPrecipitationRetrieval(PNPR)algorithmforAMSU/MHSobservations:descriptionandapplicationtoEuropeancasestudies,Atmos.Meas.Tech.,8,837-857,https://doi.org/10.5194/amt-8-837-2015,2015.

6p.08 TheGEWEXwatervaporassessment(G-VAP):finalresultsfromfirstphaseandthefutureofG-VAPPresenter:NathalieSelbachAuthors:MarcSchröder,HeleneBrogniez,Shu-pengHo,LeiShi,NathalieSelbachAlargevarietyofsatelliteandreanalysesbasedwatervapordatarecordsisavailabletodate.Withoutproperbackgroundinformationandunderstandingofthelimitationsofavailabledatarecords,thesedatamaybeincorrectlyutilisedormisinterpreted.Thus,assessmentsareconductedtoobjectivelyandindependentlyevaluateandinter-compareavailableclimatedatarecords(CDRs)inordertopointoutstrengths,differencesandlimitationsand,ifpossible,toprovidereasonsforthem.TheGEWEXDataandAssessmentsPanel(GDAP)hasinitiatedtheGEWEXwatervaporassessment(G-VAP)whichhasthemajorpurposetoquantifythecurrentstateoftheartinwatervaporproducts(uppertropospherichumidity,specifichumidityandtemperatureprofilesaswellastotalcolumnwatervapor)beingconstructedforclimateapplications.InordertosupportGDAPandthegeneralclimateanalysiscommunityG-VAPintendstoanswer,amongothers,thefollowingquestions:

a) Howlargearethedifferencesinobservedtemporalchangesinlong-termdatarecordsofwatervaporonglobalandregionalscales?

b) Arethedifferencesinobservedtemporalchangeswithinuncertaintylimits?c) Whatisthedegreeofhomogeneity(presenceofbreakpoints)ofeachlong-termdatarecord?

AgeneraloverviewofG-VAPwillbegivenwhichincludesaninventoryofavailablewatervapordatarecords(seealsohttp://gewex-vap.org/?page_id=13)andanintroductiontotheG-VAPdataarchive(digitalobjectidentifier:10.5676/EUM_SAF_CM/GVAP/V001).Thefocusofthepresentationwillbeonobservedinconsistenciesamonglong-termdatarecords(elevenTCWVdatarecordsandsevenspecifichumidityandtemperatureprofiledatarecords,sixofwhicharebasedonreanalysis).Theinconsistenciesareobservedbyinter-comparisons,comparisonstoin-situobservationsandthestabilityanalysis.Onbasisofconsistentlyappliedtoolsmajordifferencesinstate-of-artCDRshavebeenidentified,documentedandtoalargeextendexplained.TheresultsandtheanswersforTCWVaresummarizedasfollows:Onglobalice-freeoceanscalethetrendestimatesamonglong-termdatarecordsweregenerallyfoundtobesignificantlydifferent.Maximainstandarddeviationamongthedatarecordsarefoundover,e.g.,tropicalrainforests.Theseandothernoticeableregionscoincidewithmaximainmeanabsolutedifferencesamongtrendestimates.Thesedistinctfeaturescanbeexplainedwithbreakpointswhichmanifestonregionalscaleandwhichtypicallydonotappearinstabilityanalysisrelativetoground-basedobservations.Resultsfromprofileinter-comparisonswillalsobeenshownandexhibit,amongothers,thattheobservedbreakpointsarenotonlyafunctionofregionbutalsoofparameter,i.e.,breakpointsobservedinspecifichumiditydataaretypicallynotpresentincorrespondingtemperaturedataandviceversa.WiththepublicationoftheWCRPreportonG-VAP(16/2017,availableathttps://www.wcrp-climate.org/resources/wcrp-publications)G-VAP’sfirstphaseends.Asagreeduponatthe7thG-VAPworkshopG-VAPwillcontinuewithunchangedscope,objectives,andgovernanceundertheumbrellaofGDAP.Also,coreactivitiessuchasPDF,consistencyandstabilityanalysiswillbecontinuedtoprovideasustainedservicetothecommunity.Finally,newscienceactivitieshavebeendiscussedanddefined.Amongothers,theseincludetheanalysisofthequalityofprofiledatarecordsoverstratusregionsandtheassessmentandcharacterizationofthedifferencesbetweenobservationsinallsky,cloudyskyandclearskyconditions.Answerstothesciencequestionswillbeprovidedandfirstresultsfromnewanalysiswillbeshown.6p.09 TheValueofTwoSatellitesintheSameOrbitforNowcastingandClimateMonitoringPresenter:MitchGoldberg,NOAAAuthors:MitchGoldberg,LihangZhouThispaperwillshowthevalueofSNPPandNOAA-20beingseparatedbyahalforbitinthe13:30orbit.Webelievethisconfigurationshouldbecomeastandardbestpracticeforallsatelliteagenciesflyingpolarorbitingsatellites.Fornowcasting,twosatelliteshalforbitapartenablesbettermonitoringofthechangingenvironmentinthepolarregions–suchasicemonitoringandevenwildfires.Forsevereweather,(tropicalstorms,thunderstorms-convectiveenvironment),relyingononesatellitewillnotprovidethenecessarycoveragebecauseoflimbeffectsandorbitalgaps.Forclimatemonitoring,thedifferencebetweenthetwosatellites(instruments,products)canbemonitoredona16dayor32dayrepeatcycle,becausetheglobalaverageshouldbeidenticalsincebothsatellitesareobservingat13:30and1:30.WewillshowstabilityforATMS,CrIS,andVIIRSradiancesaswellasanumberofderivedproductssuchaslandsurfacetemperature,inadditiontonowcastingexamples.

6p.10 Theincreaseintheimpactoftheobservationsina40multi-yearReanalysisonthetropicalregionwith41emphasisontheAmazonbasinPresenter:DirceuHerdies,CPTEC/INPEAuthors:FabioL.R.Diniz,DirceuL.HerdiesandRicardoTodlingConventionalandnon-conventionalatmosphericobservationsareoffundamentalimportancetoallowreliableweatherforecastsandallowresearcherstoimprovethemodelingoftheatmospheretocreateplausiblescenariosforclimatestudies.However,conventionalobservationsmakeupaverysmallnumberofobservationsavailableforuseinweatherforecastsandclimatestudies.Satellites,thenon-conventionalobservations,observetheEarthSystemalmostcontinuouslyintimeandgeneratemassiveamountsofdatathatbyfardominatetheobservationblend.Unlikeconventionalobservingnetworks,satellitesobservetheEarthindirectlybymeasuringeitheremittedorabsorbedradiationbytheEarthandtheinstrumentstheycarry,thismakestheirusesomewhatharderthanusingconventionalobservations.Assessinghowthesevariousobservingsystemscontributetoimprovingweatherforecastshasbecomeanessentialtooltohelpscientistsunderstandhowtobuildfutureandbetterobservingsystems.Thepresentstudyprovidesacomprehensiveassessmentofnearly40yearsofobservationsusedintheReanalysisprocedure,whichessentiallyprovidesamixtureofmodelpredictionsandobservations.ThisparticularworkexaminestheregionalimpactofobservationsontheAmazonbasinduringtheperiod1980to2017.Onthisrelativelydenseanddifficult-to-accesstropicalforestregion,certainobservationsystemshaveaparticularlygreaterimpactthantheynormallyhavewhenobservedonaglobalscale,suchasAMVs.Theimpactoftheobservationsonshort-rangereanalysisforecastshasincreasedslightlyoverthecourseofthereanalysis.Thisincreaseislargelyassociatedwithanincreaseinavailableobservationsonthisregion.6p.11 Satellite-DerivedUpperTroposphericHumidityDatasetsandComparisonwithTotalColumnWaterVaporPresenter:LeiShi,NationalCentersforEnvironmentalInformation(NCEI),NOAAAuthors:LeiShi,CarlJ.SchreckIII,MarcSchröderAspartoftheactivitiesfortheGlobalEnergyandWaterExchanges(GEWEX)watervaporassessment(G-VAP,http://gewex-vap.org),theuppertropospherichumidity(UTH)datasetsareinter-compared.Withtherecentavailabilityofnewversionsandextendedtimeseriesofdatasets,theanalysesareupdated.TheUTHdatasetsincludebothinfraredandmicrowavesatellitesoundermeasurements.TheHIRSUTHdatasethasalsobeencomparedtothetotalcolumnwatervapor(TCWV)timeseriesfocusingontheirrespectivepatternsduringmajorElNiñoevents.TheexaminationshowsthatthedifferenceinUTHandTCWVpatternsresultsinanoppositephaseinthetimeseriesduringamajorElNiñoeventwhenatropicalaverageistaken.ThoughbothUTHandTCWVarecloselycorrelatedwithmajorclimateindices,theyhavesignificantlydifferentlagcorrelationswiththeNiño3.4indexinboththesign(positiveornegative)andlagtimeovertropicaloceans.6p.12 EUMETSAT'sContributionofFundamentalClimateDataRecordstoCopernicusClimateChangeServicePresenter:VijuJohn,EUMETSATAuthors:VijuO.John,T.Hanschmann,J.Onderwaater,F.Ruethrich,R.Roebeling,M.Grant,andJ.SchulzThedetectionofclimatechangeandanalysisofclimatevariabilityatinter-annualanddecadalscalesrequirewell-calibratedmeasurementsthatcanbeusedtocreatelong-term,homogeneoustimeseriesofclimatedatarecords.ThesedatarecordscanbeutilisedindirectanalysisoftheclimatesystembutalsowithinNumericalWeatherPrediction(NWP)modelstocreateaphysicallyconsistentreanalysissuchastheERA-5generatedbytheC3S.EUMETSAThasbeengeneratingawidevarietyoflevel1satelliteclimatedatarecords,alsoknownasfundamentalclimatedatarecords(FCDR)andtheyincludedatafromthefollowinginstruments:microwavehumiditysoundersSSM/T-2,AMSU-B,MHS,MWHS,MWHS-2,andATMS,infraredsoundersIASIandHIRS,infraredimagersMVIRI(alsothevisibleband)andSEVIRI,radiooccultationinstrumentsGRAS,COSMIC,andCHAMP,scatterometerASCAT,andtheopticalspectrometerGOME-2.AlltheseFCDRs,exceptoftheGOME-2willbeassimilatedinthenextgenerationERAreanalysis.ThepresentationwillfocusonthegenerationofmicrowaveandinfraredFCDRs.ThemicrowavehumiditysoundingFCDRsweregeneratedusingacommonalgorithmforallthesensors,whichisbasedonprinciples,developedintheEUHorizon-2020FIDelityandUncertaintyinClimatedatarecordsfromEarthObservations(FIDUCEO)project.Athoroughanalysisofthephysicaleffectsinthemeasurementequation,whichcouldintroduceuncertaintiestothemeasurements,isperformed,andthusweareprovidingnotonlytheradiances

(brightnesstemperatures),butalsotheindependent,structured,andcommonuncertaintieswhichareassociatedwithindividualmeasurements.ThelatestversionoftheHIRSalgorithmisadaptedforthethreegenerationsoftheHIRSinstrumentsandtheFCDRsareaccompaniedbytheindependentradiometricnoiseofthemeasurements.TheMVIRIandSEVIRIinfraredchannelmeasurementshavebeenrecalibratedusingIASI,AIRS,andHIRSmeasurements.TheGRASradiooccultationdatawerealsoreprocessedbyusingthelatestwave-opticsalgorithmtogeneratetheFCDR.ThemethodsaswellastheimprovementsofthetheseFCDRsovertheoperationalradianceswillbedemonstratedbytimeseriesanalysesandcomparisonsagainstreferencemeasurements.Session6:Reanalysis(oralpresentations)6.05 TheAssimilationofRadianceDataintheERA5GlobalReanalysisPresenter:WilliamBell,ECMWFAuthors:BillBell,HansHersbach,PaulBerrisford,AndrasHoranyi,JulienNicholas,RalucaRadu,JoaquinMunozSabater,DinandSchepers,AdrianSimmons&CornelSociThemostrecentECMWFglobalatmosphericreanalysis,ERA5,coveringtheperiod1979-2019(soontobeextendedto1950),isnowavailable.ItusesarecentversionoftheECMWFNumericalWeatherPrediction(NWP)forecastmodelanddataassimilation(DA)systemtoassimilateobservations(87billionfortheperiod1979-2018)toanalysetheatmosphericstate.Informationonuncertaintiesinthestateestimatesareprovidedbya10-memberensembleofdataassimilations(EDA).SatelliteradiancedataisakeyinputtoERA5.Thecompletionofthe1979-2019periodallowsustoinspecttheevolutionofbiasesevidentinthesatellitedataoverthemodernsatelliteera,andinsomecases(forexamplethehyperspectralIR)tocomparewithindependentradiometricuncertaintyestimates.ERA5alsousessatellitedatafromtheearlysatellite-era.ObservationsfromtheVerticalTemperatureProfilingRadiometer(VTPR)havebeenassimilatedintheproductionstreamspanning1972-1978andhavehadabeneficialimpactonthereanalysis,mostnotablyinthesouthernhemisphere.Assimilationofthisdatahasalsopresentedsomechallenges,includingtheresponseofthereanalysisintheupperstratosphere(above5hPa)andintheresponseoftheanalysedozonefields.InadvanceofthenextC3Sglobalreanalysis(ERA6,duetostart2023)ECMWF,throughtheCopernicusClimateChangeService(C3S),issupportingsatellitedatareprocessinganddatarescueefforts.Theseeffortsincludedatasetsfromoperationalsatellitesfromthemodernsatelliteera(1979-)aswellassomedatasetsdatingfromthelate1960sand1970s.Aspartofthese,methodsfortherobustdefinitionofobservationaluncertainties,developedaspartoftheEU'sHorizon-2020FIDUCEOproject,arebeingadoptedwhereviable.Severaloptionsbeingconsideredfortheapplicationofthisuncertaintyinformationwillbedescribed.6.06 SatelliteeraretrospectiveanalysisovertheIndianregionPresenter:SIndiraRani,NCMRWFAuthors:S.IndiraRani,RichardRenshaw,JohnP.GeorgeandE.N.RajagopalSatelliteeraretrospectiveanalysisoverIndiaandsurroundingregionsiscarryingoutundertheNationalMonsoonMission(NMM)projectofMinistryofEarthSciences(MoES),India,withthecollaborationamongUKMetOffice,NCMRWFandIndiaMeteorologicalDepartment(IMD).TheIndianMonsoonDataAssimilationandAnalysis(IMDAA)spans40yearsofreanalysisfrom1979to2018,whichusesthehistoricallyarchivedconventionalobservationsfromIMDaswellasthestateoftheartsatelliteobservationsviz.,geostationaryandpolarradiances,AMVsandscatterometerwinds.Thereisaconsiderableprogressintheobservingsystemduringthecourseofthisre-analysis,particularlythespace-borneassimilablemeteorologicalobservations,supplementedbyconventionalobservations.Thispaperdescribesthesatellitedatausageinthe4D-VARassimilationsystemofIMDAA,withanemphasistothequantityandquality.Timelineofdifferentsatelliteradiancesassimilated,coverageofsatelliteobservationsduringthereanalysisperiod,variationalbiascorrection,backgroundandanalysisfitstothesatelliteobservation,etc.arediscussedinthispaper.

6.07 Assessingtheimpactofobservationsinamulti-yearReanalysisPresenter:RicardoTodling,NASA/GMAOAuthors:FabioL.R.DinizandRicardoTodlingOperationalandquasi-operationalweatherpredictioncentershavebeenroutinelyassessingthecontributionfromvariousobservingsystemstoreducingerrorsinshort-rangeforecastsforanumberofyearsnow.Theoriginaltechnique,ForecastSensitivity-basedObservationImpact(FSOI),involvesdefinitionofaforecasterrormeasureandevaluationofsensitivitieswithrespecttochangesintheobservingsystemthatrequireadjointoperatorsofboththeunderlyingtangentlinearmodelandcorrespondinganalysistechnique.ThepresentworkappliesFSOItoReanalysisandaimsatprovidinganexpandedviewofthecontributionofvariousobservingsystemsovernearly40yearsofassimilation.Specifically,thisstudyusesMERRA-2giventhatitssupportingsoftwareincludesallingredientsnecessarytocalculateFSOI.Partofthisworkshowshowthequalityofforecastsimprovesoverthecourseofthereanalysis,andexaminesforecastsensitivitiesrelevanttoFSOI.Theassessmentherefinds,forexample,that:conventionalobservationsareamajorplayerinreducingforecasterrorthroughoutthe40yearsofreanalysis,evenwhentheirvolumereducesfrom45\%intheearlierperiodstoabout5\%inthemodernera;satelliteradiances,especiallymicrowaveinstrumentsaremajorcontributorstoerrorreductionfromtheearlysingleplatformTIROS-Ndaystothecurrentmulti-platformscenario;infraredinstrumentsplayasecondaryroletomicrowavebutaresignificantstill,withthepeculiarresultoffractionalimpactscontributionfrommodernhyperspectralinstrumentsbeingroughlysimilartothosefromearlyinfraredinstruments.Thedependenceofresultsonthechosenerrormeasureisillustratedwithfurtherthoughtontheoverallimpactofsatelliteobservations.Session7:AssimilationofNewHyperspectralInfaredInstruments(oralpresentations)7.01 AssimilationofhightemporalGIIRSradianceinGRAPESPresenter:RuoyingYin,InstituteofAtmosphericPhysics(forWeiHan)Authors:WeiHan,RuoyingYin,HaoWang,JinchengWang,XueshunShenHighspectralresolutioninfrared(IR)soundersfrompolarorbitsatelliteshavebeenwidelyusedinglobalandregionalnumericalweatherprediction(NWP)modelsandhaveprovidedpositiveimpactonweatherforecasts.Forthefirsttime,ahyper-spectralIRsoundercallGIIRS(GeostationaryInterferometricInfraredSounder)isstationedonthegeostationaryorbit,itisonboardthefirstsatelliteofthesecondgenerationofChinesegeostationaryweathersatellites-FengYun-4series.FY-4AhasthecapabilityonprovidingGIIRSobservationsevery15minutesforselectedregionswhereactiveweathereventsoccur.TheGIIRSmeasurementshavebeencalibrated,geo-locatedandprocessedforreal-timeapplications.TheRTTOV-GIIRScoefficientshavebeendevelopedbasedonlocaltrainingprofilesforassimilation,GIIRSdatanowhavebeenassimilatedintheoperationalGRAPES(Global/RegionalAssimilationPredictionSystem)globalforecastmodelwith4D-Varassimilationsystem.Twomonths’cycleexperimentsshowapositiveimpactonanalysesandforecastsoverEastAsia.GIIRStemperaturesoundingchannelshavebeenoperationallyassimilatedinGRAPESglobal4D-VarsinceDecember252018.AnanalysisonthreeTyphooncasesin2018indicatespositiveimpactonTyphoonforecasts,especiallyonwarmcoreandtrackforecasts,whichdemonstratesthepotentialaddedvalueforhighimpactweatherforecastthroughassimilatingthehightemporalresolutionsoundinginformationintoNWPmodels.7.02 InformationcontentoftheCross-traceInfraredSounder(CrIS)instrumentandrecentdatadenialexperimentsrelevanttooperationaluseofsounderdata.Presenter:ChristopherBarnet,STCAuthors:ChrisBarnet(STC),SidBoukabara(STAR),KevinGarrett(STAR),KayoIde(UMD),ErinJones(UMD-CICS),YingtaoMa(UMD-CICS)AdvancedhyperspectralsounderssuchastheAdvancedInfraredSounder(AIRS),InterferometricAtmosphericInfraredSounder(IASI)andtheCross-trackInfraredSounder(CrIS)allinvestedheavilyinprovidinghighsignal-to-noisemeasurementsintheshort-waveinfrared(SWIR)spectralregion(definedhereasfrom3.8to5microns)aswellasthelong-wave(LWIR,15.5-9um)andmid-wave(MWIR,9-5um)spectralregions.TheuseoftheSWIRiscomplicatedbytheneedtohandlesolarradiationthatisbothreflectedfromthesurfaceandalsoexcitesmoleculesintheupperstratosphereandlowermesosphereintonon-equilibriumemission.TheAIRSScienceTeamdemonstratedhowtoproperlyusetheSWIRtoderivehigh-qualitytemperature,moisture,andtracegaseswiththelaunchofAqua/AIRSin2002.TheNOAA-UniqueCombined

AtmosphericProcessingSystem(NUCAPS)operationallydeployedtheAIRSalgorithmfortheMetop-A/IASI,S-NPP/CrIS,Metop-B/IASI,andtheNOAA-20/CrISinstrumentssince2008,2011,2012,and2018,respectively.Wewilldemonstratetheinformationcontent(IC)oftheCrISinstrumentusingsingularvaluedecompositionofCrISobservationsandillustratetheexpectedICofvarioussubsetsofCrISchannels.ThisisrelevanttoboththeuseoftheSWIRbandindataassimilation(seepresentationsatthismeetingbyco-authors)butalsointhecontextoftherecentlossoftheMWIRbandontheS-NPP/CrISinstrument.WewilldemonstrateresultsfromanexperimentinwhichvariousCrISand/orAdvancedTechnologyMicrowaveSounder(ATMS)datawereexcludedfromtheoperationalNUCAPSsystem.Theseresultsarealsoinformativefornewsmall-satelliteinstrumentconceptsthatmaynotbeabletoaffordhavingall3bandspresent.7.03 ImplementationandassessmentofFY-3DHyperspectralInfraredAtmosphericSounder(HIRAS)intheMetOfficesystemPresenter:FabienCarminati,MetOfficeAuthors:FabienCarminati(MetOffice),XianjunXiao(CMA),FionaSmith(BOM),NigelAtkinson(MetOffice)Launchedlate2017onboardFY-3D,thefourthChinesepolarorbiterofsecondgenerationintheFengYunseries,HIRASisaMichelsoninterferometerof1370channelscoveringthreespectralbands(650-1136,1210-1750,and2155-2550cm-1)with0.625to2.5cm-1spectralresolutionand16kmnominalresolutionatnadir.Theinstrumenthas58pixelsperscanlinesarrangedin2x2arrayswhichcoveratotalswathof2250km.HIRASprocessingcapabilityhasbeenimplementedattheMetOffice.RawdirectbroadcastdatalocallyreceivedattheMetOfficethroughtheantennabasedinExeterareconvertedtolevel1geolocatedradiancesatnormalspectralresolutionwiththeChinameteorologicaladministration(CMA)FY3softwarepackageandpre-processedwiththeATOVSandAVHRRPre-processingPackage(AAPP)beforebeingstoredinthedatabaseforlateruseintheNWPsystem.TheAAPPprocessingincludesconversionfromHDF5toBUFRandapplicationofthechannelselectionthatisalsousedforCrIS.TwomonthsofglobalHIRASobservationsprovidedbyCMAarebeinganalysedatthetimeofwritingandwillserveasabaselinetodevelopbiascorrectionsforlaterassimilationexperiments.Pendingtimelynearrealtimedatadistributionavailably,HIRASpre-operationalassimilationtestingphaseisexpectedtobecompletedbyOctober2019.ThissubmissionwillpresentthelatestprogresstowardsassimilationofthisdataintheMetOfficeNWPsystem.7.04 NewIRsoundersintheECMWFNWPsystemPresenter:ReimaEresmaa,ECMWFAuthors:ReimaEresmaaECMWFhasassimilatedNOAA-20CrISradiancessince11September2018.Twoaspectsarecurrentlylimitingtheoperationaluse:Firstly,duetothespectralpropertiesdifferingfromS-NPPCrISinthemid-waveIRband,theactiveuseisrestrictedtothelong-waveIRbandonly.Secondly,observationerrorcovarianceisspecifiedidenticallytotheS-NPPCrIS,althoughthenoisecovarianceisknowntodifferbetweenthetwoCrIS's.Preparationsareunderwaytowardsactivating37water-vapour-soundingchannelslaterin2019:thiswillmakeuseofNOAA-20-specificobservationerrors.Metop-CIASIradianceshavebeenavailableformonitoringandassimilationexperimentssinceApril2019.PreliminarydataqualityassessmentsuggestssimilarnoiseperformanceascomparedwithMetop-BIASI.Weexpecttostarttheoperationaluseof220channels,applyingsimilarsettingsasthoseforMetop-AandMetop-BIASI's,duringSummer2019.InadditiontoNOAA-20andMetop-C,werecognizethegap-fillingpotentialofRussianandChinesesatelliteprogramsandhavestartedexploringdataacquiredfromMeteor-MN2andFY-3Dsatellites.WefindboththeIKFS-2andHIRASsoundersslightlynoisierthanthewell-establishedAmericanandEuropeanIRsounders.Nevertheless,wearelookingforwardtothelaunchofFY-3Eintotheearly-morningorbitandexpectthenewIRobservationstomakeausefulcontributiontotheoperationalsysteminthecomingyears.7.05 AssessmentandassimilationofobservationsofthehyperspectralIRsounderIKFS-2onboardtheRussianMeteor-MN2satellitePresenter:DmitryGayfulin,HydrometeorologicalCentreofRussiaAuthors:DmitryGayfulin,MichaelTsyrulnikov,AlexanderUspenskyThehyperspectralIRsounderIKFS-2ispartofthepayloadofMeteor-Mseriessatellites.ItisaFourier-transformspectrometerwithspectralrange5-15micronsandspectralresolution0.4-0.7cm-1.Currently,data

fromMeteor-MN2satellite(flyinginamorningorbit)areavailable.Thenextsatelliteintheseries,Meteor-MN2-2,isduetobelaunchedintoanafternoonorbitinJuly2019.ObservedspectrafromIKFS-2onboardMeteor-MN2werecomparedwithabackground,theNCEP6hforecastconvertedtotheradiancespacewiththeRTTOVradiativetransfermodel.Thecomparisonyieldedencouragingresults:theaccuracyoftheIKFSdatawasfoundcomparablewiththeaccuracyofIASIobservations.Thinnedandcloud-clearedIKFSobservationsinatmospherictemperaturechannels(inthe660–750cm-1spectralrange)wereassimilatedintothedataassimilationsystemoftheHydrometcentreofRussia.Asignificantpositiveimpactonthree-dayweatherforecasts(intheabsenceofIASIdata)wasfound.Therolesofchannelselection,qualitycontrol,biascorrection,andcloudclearingarediscussed.7.06 TheevaluationofGIIRSlongwavetemperaturesoundingchannelsusing4D-VarPresenter:RuoyingYin,IAPAuthors:RuoyingYin,WeiHan,ZhiqiuGaoandDiDiThetheoryofclassicalvariationalassimilationassumesthatbiasesareunbiasedGaussian.ThispaperinvestigatesthebiascharacteristicestimateandthebiascorrectionofGeostationaryInterferometricInfraredSounder(GIIRS)onboardtheFY-4A.QualitycontrolproceduresforGIIRSlongwavetemperaturechannelsbrightnesstemperatureincludeclouddetectionbasedontheAdvancedGeosynchronousRadiationImager(AGRI)andoutlierremoval.Themeanbiasesformostchannelsarewithin±2Kafterqualitycontrolexceptforthecontaminatedchannels.StatisticalevaluationofthedifferencesbetweenGIIRSobservationsandmodelsimulationsrevealthatbiasesforthelongwavetemperaturechannelsdependonfieldsofview(FOVs).Thestandarddeviationissmallerintheprincipalopticalaxisoftheobservationarrayandbecomeslargertothenorth/southends,reachingmaximumvaluesinthe32ndand96thFOVs.Thelatitudinaldependenceofthemeanbiasesandstandarddeviationsareapparent(13°N,20°N,27.5°N,35°N,45°Nand60°N)duetotheFOVsarrayobservationmode.Additionally,thediurnalvariationofbiasesisobvious,especiallyfortheuppertroposphericchannels,andthebiasesforhightroposphericchannelsaresmallerthanthebiasesforlowtroposphericchannels.Finally,off-linebiascorrectionthatwasusedinthisstudyaccountsforthefieldofregard(FOR)dependenceandthediurnalvariationbiascharacteristicsofGIIRS.Afterbiascorrection,theresultsshowthatbiasesoflongwavetemperaturesoundingchannelsarereducedto±0.02K,andthestandarddeviationsarelessthan1Kexceptforthecontaminatedchannels.TheprobabilitydensityfunctionofthedifferencesbetweenobservationsandsimulationsforsomecommonassimilationchannelsisclosertotheunbiasedGaussiandistribution.Session7:AssimilationofNewHyperspectralInfaredInstruments(posterintroductions)7p.01 ImpactassessmentofIASItemperatureandhumidityretrievalsintheECMWFsystemPresenter:KirstiSalonen,ECMWFAuthors:KirstiSalonen,ThomasAugust,TimHultbergandAnthonyMcNallyEUMETSATisproducingforecastindependentstatisticalretrievalsofatmospherictemperatureandhumidityfromIASIradiancesinpreparationforfutureproductgenerationfromMTG-IRS.ThispostersummarisesthekeyfindingsoftheretrievalqualityandimpactassessmentintheECMWFsystem.Theoverallqualityoftheretrievalsisgoodaslongasstrictqualityindicatorsareappliedtoexcludecloudyscenes.Generallytheclear-skyobservationminusmodelbackground(OmB)statisticsindicatesmallerthan-0.3Kbiasfortemperatureandsmallvaryinginsignbiasforhumidity.TheseretrievalOmBstandarddeviationsarecomparableinmagnitudetosimilarstatisticscomputedforradiosondes.However,ithasbeenfoundthattheretrievedprofilesarerathersmoothandtypicallylackimportantfineverticaltemperaturestructureduringinversionsandaroundthetropopause.Investigationsrevealthattheobservationerrorsarealsohighlycorrelatedbetweenverticallevels.Itisessentialtotakethesecorrelationsintoaccountindataassimilationexperiments.Theerrorcorrelationsaresituationandlocationdependentandbecomeincreasinglystrongerforcloudaffectedprofiles.

Assimilationexperimentsindicatethatinclearskyconditionsthehumidityretrievalshaveapositiveimpactonanalysesandforecastquality,comparableinmagnitudetothatobtainedwhenIASIradiancesareassimilated.However,theresultsareverysensitivetothediagnosedobservationerrorcorrelationthatisused.Theassimilationoftemperatureretrievalscurrentlydegradesanalysesandforecasts,mostlikelyduetosmoothingofinversionsandtropopausestructures.7p.02 EvaluationoftheperformanceofCrISinstrumentundervariousassimilationscenariosPresenter:SylvainHeilliette,EnvironmentCanadaAuthors:SylvainHeilliette,StéphaneLarocheTheCross-trackInfraredSounder(CrIS)infraredhyperspectralsoundinginstrumentisnowflyingonboardNPPandNOAA20platforms.Thisinstrumenthasverygoodradiometriccharacteristicsinthe15micronCO2band.AttheCanadianMeteorologicalCentre(CMC),NPPCrISobservationshavebeenassimilatedoperationallysinceDecember2015,whereasNOAA20CrISobservationswillsoonbeassimilated.ThegoalofthisstudyistoevaluatedifferentconfigurationsofCrISassimilationandtheirimpactinforecasts.Afrequentlyencounteredproblemforthistypeofevaluationisthatitisoftendifficulttoseeaclearsignalgiventhenecessaryredundancyintheassimilatedobservationsfromvarioussensors.Therefore,inthisstudywechoosetocomparefirstthedifferentconfigurationsstudiedwithrespecttoareducedbaselineconfigurationinwhichonlyconventionalobservationsareassimilated.Morespecifically,weaddressthefollowingquestions:WhatisthebenefitofthetwoCrISinstrumentsontopofthebaselineconfiguration?Whatistheimpactofthelostwatervapourchannelsfromband2onNPPCrIS?Isthereabenefitofassimilatingmoretemperaturesoundingchannelsinthe15micronsCO2band?CanthequalitycontrolbeimprovedusingtheVIIRScloudmaskforclouddetection?Finally,theimpactofthetwoCrISsensorsontopofallotherassimilateddatacanalsobereevaluated.7p.03 Operationaluseofinter-channelcorrelationsforIASIintheDWDEnVarandinvestigationintotheuseofReconstructedRadiancesPresenter:SilkeMay,DWDAuthors:SilkeMay,OlafStiller,RobinFaulwetter,ChristinaKöpken-Watts,RolandPotthastHyperspectralinfraredsounderslikeIASIorCrISprovideaverylargenumberofchannelswhicharespectrallyverydense.Thisposterfocusesontwochallengesassociatedwiththis:First,observationerrorsdisplaynon-negligibleerrorcorrelationsbetweenthechannelsthatneedtobetakenintoaccountintheassimilationsystem.TheimpactofusingafullerrorcovariancematrixRintheoperationalglobalensemblevariationaldataassimilationsystem(EnVAR)fortheglobalICONmodelsystemofDWDhasbeentestedforIASIradiances.Secondly,inviewofthefutureMTG-IRSforwhichdatatransmissionisbasedonprincipalcomponentcompresseddata,theuseofreconstructedradiancesforIASIassimilationisbeingtested.Uptonow,IASIradianceshavebeenassimilatedatDWDusingadiagonalRmatrix,diagnosedwithDesroziersetal.(2005)method,butinflatingdiagonalelementstoaccountfortheneglectedinter-channelcorrelations.Anon-diagonalRmatrixhasbeenestimatedusingtheDesroziersmethodandusedinassimilationexperimentsensuringitisinvertiblethroughsettingalowerthresholdonthesizeofeigenvalues.Usingthenon-diagonalRmatrixleadstopositiveimpactonNWPforecasts,particularlyonthehumidityfieldsvisibleinamuchimprovedfittoothersatelliteobservationssensitivetohumidity.Furtherexperimentsonsensitivitiesofresultstodifferentparameters,e.g.applyinganadditionalscalingfactortotheRelementsorachangedcut-offlimitfortheeigenvaluesofR,areinvestigated.Inparallel,assimilationtestsusingreconstructedradiances,inaninitialsetuptreatingthemsimilarlytorawradiances,havebeenrun.Aswithrawradiances,afullRmatrixhasbeendiagnosedandintroducedinexperiments.Asexpectedmuchlargerinter-channelcorrelationsarevisible.Resultsoftheassimilationofreconstructedradiancesincomparisontorawradianceswillbeshown.AnextensiontotheassimilationofCrISFSRdatausingfullRisongoing.7p.04 Detectionofaerosol-andtrace-gas-affectedIRradiancesatECMWFPresenter:ReimaEresmaa,ECMWFAuthors:ReimaEresmaa,JulieLetertre-Danczak,TonyMcNallyNWPsystemdiagnosticsindicatetowardstheneedforscreeningroutinesspecificallytopreventaerosol-andtrace-gas-affectedinfraredradiancesfromenteringtheassimilation.IntheNWPsystematECMWF,aerosolisdetectedfromobservedbrightnesstemperature(BT)differencesatwindowchannelsbothsidesofthe9.6

micronozoneabsorptionband.Positivedetectionofaerosolcurrentlyleadstorejectingallchannels,althoughitisknownthattheassociatedradiativeeffectistypicallylimitedtolower-tropospheric-andsurface-sensitivechannels.Wehaverecentlydevelopedamethodtorestricttheaerosol-relatedrejectionstoaffectedchannelsonly.ThemethodisbasedonconvertingtheobservedBTdifferenceintoaproxyofAerosolOpticalDepthandfurtherintoanestimateofaerosolverticalextentateachobservationlocation.Theoperationalimplementation,foreseenin2020,willrecoveralargenumberofstratospheric-andupper-troposphericdata.Thenextstepwillbetodistinguishbetweenvolcanicashanddesertdustaerosoland,subsequently,tomakethechannel-specificaerosolrejectionsdependingontheaerosoltype.Inadditiontopresentingtherecentandongoingworkontheaerosoldetection,wewillreviewthestatusofthetrace-gasdetectionatECMWF.Thetrace-gasdetectionschemewasimplementedinresponsetothe2015Indonesianforestfireepisode,duringwhichextra-ordinaryamountsofHydrogenCyanide(HCN)werereleasedintotheatmosphere.7p.05 QuantifyingtheeffectsoftheCrIS-FSRRadiancePolarizationCorrectionsusingtheNCEPGlobalDataAssimilationSystem.Presenter:JamesJung,CIMSSAuthors:JamesJungandco-authorsTheCross-trackInfraredSounders(CrIS)havepolarizationeffectsduetothedesignoftheinstrument.ThesepolarizationeffectsmaybesignificanttoNumericalWeatherPrediction(NWP),especiallyfortheshortwaveband.ScientistsattheSpaceScienceandEngineeringCenter(SSEC)havedevelopedatheoreticalmodelandcorrectionforthepolarizationinducedcalibrationerrors.ThepolarizationparametersrequiredforthecorrectionweredeterminedusingdataacquiredbytheCrISinstrumentduringtheFebruary2012SNPPpitchmaneuver.Duringthepitchmaneuver,alloftheCrIScross-trackfieldsofregardthatnormallyviewtheEarth,werelookingtodeepspace.Inthisconfiguration,fieldofregardanddetectordependentdifferencesaredominatedbytheinstrumentpolarization,makingthisanidealdatasetforderivationofthepolarizationparameters.Forband3(shortwave),theuncorrectedpolarizationinducedcalibrationerrorscanbeasmuchasseveralbrightnesstemperature(BT)degreesforcoldEarthscenes.TheCrISFullSpectralResolution(CrIS-FSR)radiances,frombothSNPPandNOAA-20,withandwithoutthepolarizationcorrectionareassimilatedintheNCEPGlobalDataAssimilationSystem(GDAS)toquantifydifferencesinassimilationstatistics.WewillspecificallyreviewbiasandstandarddeviationstatisticsoftheninedetectorsforallCrIS-FSR2211channels.GDASAnalysisdifferencesarealsoinvestigatedandpresentedinthisposter.Session8:SpaceAgencyReports(posterintroductions)8p.01 Statusreportofspaceagency:JMAandJAXAPresenter:KozoOkamoto,JMA/MRIAuthors:KozoOkamoto,MisakoKachi,KotaroBesshoJMAstarteddiscussingthefollow-onsatellitesofHimawari-8/9,scheduledtolauncharound2029.Oneofpossibleinstrumentsonthefollow-onsatellitesisahyperspectralinfraredsounderanditsimpactisevaluatedusingOSSE.JAXAoperatesGCOM-W/AMSR2,GCOM-C/SGLI,GPM-core/GPMandGOSAT2,andprepareforEarthCARE/CPR.Theirstatuswillbepresented.8p.02 TheCurrentEUMETSATPolarSystemPresenter:K.DieterKlaes,EUMETSATAuthors:K.DieterKlaesThisPaperprovidesanoverviewontheproductsandservicesoftheEUMETSATPolarSystem(EPS),highlightingtheaspectsrelatedtoobservingandforecastingtheweather.EPSistheEuropeancontributiontothePolarMeteorologicalSatelliteObservingSystem.ItformsapartoftheInitialJointPolarSystem(IJPS),formedwithNOAA(NationalOceanicandAtmosphericAdministration).EUMETSATisassuringthemid-morningservice(9:30AMLST,descendingnode),whereastheUSpartnersareassuringtheafternoonservice(13:30PMLST,ascendingnode).Eightmeteorologicalinstruments(among11)areembarkedontheMetopsatellites(sevenonMetop-C).TheMetopsatellitesdevelopedinco-operationwiththeEuropeanSpace

Agency(ESA)formthespacesegmentofEPS.Metopinstrumentdata–inparticularthesoundinginstruments-provideanessentialcontributiontoglobaloperationalNumericalWeatherPrediction(NWP).ClimatemonitoringandatmosphericcompositionmonitoringandoceanandcryosphereobservationsarefurtherapplicationareassupportedbyMetopinstrumentdata.TherearethreeMetopsatellitesintheprogrammeandflyinasun-synchronousmidmorningpolarorbitwithequatorcrossingtimeof9:30LocalSolarTime(LST)forthedescendingnode.Atleast15yearsofoperationsareforeseentoprovidemeasurementsfromspace.AllthreeMetopsatellitesareinorbit(Metop-Alaunched2006andMetop-Bin2012),withthethird,Metop-Csuccessfullylaunchedonthe7November2018(UTCtime)fromKourou.Afteritssuccessfulcommissioning,therewillbethreeMetop-satellitesinorbitforaboutthreeyears.ThepaperwillalsohighlightcommissioningresultsofMetop-Candimpactalreadyvisiblefromthedataonthequalityofweatherforecasts.8p.03 NOAAPresenter:MitchGoldberg,NOAAAuthors:MitchGoldberg8p.04 CMAPresenter:PengZhang,CMAAuthors:PengZhang8p.05 RussianMeteorologicalSatelliteProgramsPresenter:TBCSession9:AdvancesintheAssimilationofInfaredSensors(oralpresentations)9.01 AssimilationofHyperspectralInfraredShortwaveCrISObservationsintheNOAAGlobalDataAssimilationSystemPresenter:ErinJones,UMDCISESSAuthors:ErinJones,C.Barnet,K.Garrett,K.Ide,Y.Ma,S.BoukabaraThoughshortwavechannelsonhyperspectralinfrared(IR)instrumentsarecapableofprovidinggoodtemperaturesoundinginformationtousers,thenumericalweatherprediction(NWP)anddataassimilation(DA)communitieshavehistoricallyfavoredtheuseoflongwavehyperspectralIRobservations,citingissueswithsolarimpactsonshortwavefrequencies.Asaconsequence,shortwavehyperspectralIRobservationsfrominstrumentsliketheCross-trackInfraredSounder(CrIS)arenotusedoperationallyintheNOAAGlobalDataAssimilationSystem(GDAS)/GlobalForecastSystem(GFS),regardlessofthefactthatthecapabilitytosimulateandassimilatethemexists.Owingtotheease,comparedtolongwaveinstruments,withwhichshortwaveinstrumentscanbeproduced,theirsmallersize,andthelowercostsassociatedwiththeirmanufacture,NOAA’sCenterforSatelliteApplicationsandResearch(STAR)isactivelyinvestigatingwhatbenefitsmaybegarneredfromassimilatingCrISshortwaveobservationsintheNOSSGDAS/GFS,andwhethertheseobservationscanbeassimilatedintheGDAS/GFSinplaceofhyperspectralIRlongwaveobservationswithoutdiscernablelossinanalysisorforecastskill.TobediscussedaremeasurestakentoimproveGDASqualitycontrolproceduresforhyperspectralIRshortwaveobservations,thespecificationoferrorsforshortwaveCrISchannels,theperformanceofshortwaveCrISobservationsintheGDASanalysis,andtheimpactofassimilatingshortwaveCrISobservationsontheGFSforecastinexperimentsdenyinglongwaveobservations.9.02 EnhancingthehyperspectralinfraredradianceassimilationintheECMWFsystemPresenter:KirstiSalonen,ECMWFAuthors:KirstiSalonenandAnthonyMcNallyThenumberofassimilatedWVchannelsforInfraredAtmosphericSoundingInterferometer(IASI)hasbeenrecentlyincreasedfrom10to39channelsintheECMWFsystem.TheadditionalWVchannelshaveapositiveimpactonshortrangetemperatureandhumidityforecastsandviathewindtracingmechanismalsoonthewindforecasts,especiallyinthetropics.WiththeadditionofthethirdIASI(Metop-C)theimprovementsarefurtherenhanced.

OnlyIASIradiancesclassifiedascloudfreeareusedintheECMWFsystem.Currentlytheusedobservationerrorsanderrorcorrelationsarestatic.AteachIASIsoundinglocationalsoinformationonstatisticalradiancepropertieswithinclustersoftheAdvancedVeryHighResolutionRadiometer(AVHRR)pixelsoccupyingtheIASIfieldofview(FOV)isavailable.TheAVHRRradiancestandarddeviationprovidesanindicationofthesceneheterogeneityandincreasedvaluessuggestthepresenceofcloudintheIASIFOV.Observationminusbackground(OmB)statisticsindicatethatespeciallyforhumiditysensitivechannelsofIASIthemagnitudeoftheOmBstandarddeviationincreaseswhenthecollocatedAVHRRradiancestatisticsindicateincreasedheterogeneityintheFOV.Thepotentialofusingthisinformationforscenedependentobservationserrorsisinvestigated.Thepresentationwillsummarisethelatestresultsofthework.9.03 4D-VarassimilationofIASIozone-sensitiveradiancesinoperationalglobalmodelARPEGEPresenter:OlivierCoopmann,CNRM,UniversitédeToulouse,Météo-France,CNRSAuthors:OlivierCoopmannTheinfraredhyperspectralsoundersonboardpolar-orbitingsatellitesprovidearound70%ofthedataassimilatedintheglobalNWPmodelofMétéo-France(ARPEGE),includingbothIASIs.Infraredmeasurementissensitivetosurfaceparametersandnumerousatmosphericspecies.Informationontemperaturecanberetrievedfrommeasurementsatvariouswavelengths(orchannels)correspondingtospeciesforwhichconcentrationisknown.Mostofthetemperatureretrievalsfromtheinfraredsensorsusechannelssensitivetocarbondioxide.PartsoftheinfraredspectrumaresensitivetoozonebuttheyarenotcurrentlyusedatMétéo-France.Wenotethat,inthecurrentversionsoftheradiativetransfermodel(RTM)usedintheassimilation,concentrationsofthosegasesareconstantinspaceandtime.ApreviousstudiesatMétéo-FranceshowedthatusingconcentrationsofozonecomingfromChemistry-transportModels(CTM)ledtoanimprovementofthesimulationsforIASI.Moreover,assimilationofIASIozone-sensitivechannelsallowtoimprovetemperatureandhumidityanalyses(Coopmannetal.,2018).ThegoalofthisstudyistocarryoutanewchannelselectionofIASIozone-sensitivechannelstobeusedinthefour-dimensionaldataassimilationsystem(4D-Var)byacouplingbetweentheARPEGEandMOCAGEmodelsforozonefields.TheuseofozonefromMOCAGEallowsabetteruseofinfraredsatelliteobservationsandhasapositiveimpactonthequalityofthermodynamicandozoneanalysesbutalsoonweatherforecasts.Resultsofassimilationincludingozoneinthecontrolvariablewillbepresented.9.04 Assimilatingsolarsatellitechannelsinaconvective-scaleLETKFPresenter:ChristinaKöpken-Watts,DeutscherWetterdienst(forLiselotteBach)Authors:LiselotteBach,LeonhardScheck,ChristophSchraff,ChristinaStumpf,StefanGeiss,RolandPotthastConvectiveprecipitationandcloudcoverareamongthemostchallenginguser-relevantvariablesinconvective-scaleNWP.WeseegreatpotentialinimprovingtherepresentationoftheseprocessesassimilatingvisiblechannelsoftheSEVIRI-InstrumentonMSGmakinguseofthenewfastandaccurateforwardoperatorMFASIS(Schecket.al,2016),whichhasrecentlybeenimplementedintoRTTOV.ToallowforaccuratewarningsaheadofconvectiveprecipitationandtoapproachthegoalofaseamlesstransitionfromnowcastingtoNWP,ourobjectiveistoimprovetherepresentationofconvectionalreadyatthestageofitsinitiation.Atthisstage,cloudsusuallyformatlowlevelsandatsmallscales,suchthattheyarelikelynotdetectableinthermalIR(orMW)observations.Therefore,visiblechannelsprovideveryusefuladditionalinformation.Further,weexplorepossibilitiesforimprovingtherepresentationofwintertimelowstratusininitialconditionsandforecastsforwhich,again,reflectanceobservationsduringdaytimeareimportantcomplementaryobservationstoIRsounderdata.Weshowresultsfromdataassimilationexperimentswiththenewconvective-scaleweatherpredictionmodelICON-LAM,currentlyindevelopmentatDWD,usinganensemblebaseddataassimilationapproach,theLocalEnsembleTransformKalmanFilter(KENDA-LETKF,Schraffet.al,2016).Thefocusisontheimprovementobtainedforcloudcover,precipitationandsurfacevariables.Further,wediscusspotentialproblemsrelatedtonon-gaussianfirstguessdepartures,nonlinearityoftheforwardoperatorandambiguitiesoftheobservations.

9.05 ImprovementstoOzoneAnalysesusingHyperspectralSoundersinthe9.6umBandPresenter:BryanKarpowicz,GESTAR/USRA/NASAGMAOAuthors:BryanM.Karpowicz,WillMcCarty,andKrzysztofWarganPreviously,hyperspectralsounderbrightnesstemperaturesassimilatedintheGoddardEarthObservingSystemAtmosphericDataAssimilationSystem(GEOS-ADAS)werelimitedtoassimilatingtemperatureandmoisture.Theozonesensitive9.6umregionissensedbyseveralhyperspectralsoundersincludingAIRS(AtmosphericInfraRedSounder),IASI(InfraredAtmosphericSoundingInterferometer),andCrIS(Cross-trackInfraredSounder).Directassimilationofbrightnesstemperaturesinthe9.6umregionhavebeenoperationalatECMWFforseveralyears(DraganiandMcNally,2013;Eresmaaetal.,2017).Withthisstudy,similarimprovementsusingtheGEOS-ADASarepresented.Channelswereselectedfromavailableoperationalsubsetsevaluatinginformationcontentandminimizinginter-channelcorrelation.Additionally,informationsuchaschannelselectionsmadebyotherstudies,andverticalsensitivitiesofozoneandtemperaturewereconsideredindevelopingthestudy.TheanalysesproducedshowimprovementsverifiedagainstozonesondestakenfromSHADOZ(SouthernHemisphereAdditionalOzonesondes),andWOUDC(WorldOzoneandUltravioletDataCenter).Inclusionofinter-channelcorrelationerrortotheselectedozonechannelsareexpectedtoimproveozoneanalysesfurtheralongwithimproveanalysesoverall.ItisanticipatedthatinclusionoftheseozonesensitivechannelswillbeusedtoimproveNASAGMAOproductsinthenearfuture.Session10:Retrievals(oralpresentations)10.01 StatusofregionalIASIL2productsatEUMETSATandstudiesinviewofMTG-IRSPresenter:ThomasAugust,EUMETSATAuthors:ThomasAugust,TimHultberg,CédricGoukenleuque,MarcCrapeau,DorothéeCoppens,JochenGrandellInanswertoUserrequest,EUMETSAThasbeenoperatingsinceNovember2017aregionalserviceprovidingatmospherictemperatureandhumiditysoundingwithin30minutesfromsensingfromthePolarSystemEPS.ThealgorithmexploitsinsynergymeasurementsfromIASIandfromitsmicrowavecompanionson-boardMetop,AMSUandMHS.ThesatellitedataaredirectlybroadcastedatlocalreceivingstationsfromtheEUMETSATAdvancedRetransmissionServices(EARS)systemandprocessedwithanon-linearstatisticalmethodreferredtoasPWLR3.Theretrievalsareperformedinclearaswellasincloudypixels.Theproductsaredistributedinnear-realtimethroughEUMETCastinHDF5format.Theyincludetemperatureandhumidityprofilestogetherwithaseriesofqualitycontrolparametersanduncertaintyestimates,whichUserscanusetoperformdataselectioncorrespondingtotheirapplications.BuildingontheEARS-IASIL2productsandtheirintegrationinagrowingnumberofweathermonitoringsystems,anumberofstudieshavebeeninitiatedtoexplorefurtherthesupporttoveryshortrangeforecastsandfortheearlydetectionandmonitoringofatmosphericinstabilitiesinparticular.WeprovidehereastatussummaryandfirstconclusionsofinteractionswithEuropeanforecastersinthatperspective,whichwillguidethefutureevolutionoftheregionalservice.Wediscussforinstancethecoarserverticalresolutionofthesatellitesoundingascomparedtoe.g.radiosondesprofilesandthelowersensitivitynearsurfaceinherenttohyperspectralsounders.TheoperationalalgorithmsforIASIformthebasisfortheDay-1baselineofthefuturegeostationaryhyperspectralsounderIRSon-boardMeteosatThirdGeneration(MTG)sounderplatforms(MTG-S).Withamuchhigherspatio-temporalsamplingthanEPS/IASI(observationsevery30minutesincontiguouspixelssampledat~7kmoverEuropewithMTG-IRSvstwice-a-dayinsparse12-40kmpixels)MTG-IRShasbeendesignedtothemonitoringofregionalweatherdevelopments.ThelessonslearntwiththeEARS-IASIL2productscontributetotheconsolidationoftheMTG-IRSL2requirementsandthespecificationoftheoperationalproductsandtheirprocessor.Inaddition,EUMETSAThasinitiatedanumberofcomplementarystudiestoevaluateIRS-specificaspectsliketheapplicabilityofall-skysoundinginIR-onlymode(asIRShasnomicro-wavecompanions)andthechallengesandpotentialofatmosphericsoundingfromageostationaryviewinggeometry.Inparticular,thevariationoffNadirofthetheoreticalverticalsensitivity,resolutionandsoundingprecisionisquantitativelystudiede.g.atsurfaceandinthelowertroposphereinviewofinstabilitymonitoring.Asacomparison,alargepartofEuropewillbeobservedinaviewingregimecorrespondingtothe

limitofswathforIASI.WepresenthereanoverviewandinitialresultsoftheIRS-specificstudies,limitationsandpotentialathighviewingangles.10.02 TheNewNASAMulti-missionMicrowaveSounderRetrievalSystemPresenter:BjornLambrigtsen,JetPropulsionLaboratoryAuthors:BjornLambrigtsen,MathiasSchreier,EvanFishbeinNASAhasaninterestinextendingthesounderdatatimeseriesstartedwiththeAquaAIRS-AMSU-HSBsoundersuitebeyondAqua,tosupportatmosphericandclimateresearch.NASAthereforeinvestedsignificantlyintheSuomiNPPmission,firstwiththedevelopmentofATMSandlaterwiththeformationofabroaderinstrumentscienceteam.ThatteamwasaskedtoconsiderwhethertheS-NPPsensors,includingtheCrIMSSsoundersuite,canbeusedtosupportresearchbeyondAquainlieuofacontinuingseriesofdedicatedNASAmissions.Thescienceteamconcludedthatitislikelythatoperationalmissions,suchasS-NPPandtheNOAAJPSSseries,canbeusedinsuchaway,butitalsoconcludedthattheoperationaldataproducedbyNOAAareinadequate.NASAthereforesolicitedproposalstodevelopnewdataalgorithmsthatwillbeusedtoproduceNASA’sowndatasetsfromtheNOAAsounders.Thisisparticularlynecessarytosupportclimateresearch,sincetheNOAAdataareintendedprimarilyforweatherprediction,wherecontinuityandconsistencyoverlongtimeperiodsisnotimportant,anddataarenotreprocessedasalgorithmsareupdated.TheNOAAdatasetsarethereforenotoptimalforresearchuse.UnderthisNASAprogramanewmicrowaveretrievalsystemhasbeendevelopedatJPL,calledthe“RetrievalAlgorithmforMicrowaveSoundersinEarthScience”(RAMSES).Analgorithmtestbedwasconstructedthatallowsplugandplaytestingofvariouselementsoftheretrievalalgorithm.Theinitialversion,whichisbasedonoptimalestimation,usesaveryfastradiativetransfercodethatisbasedontheAqua-AMSUretrievalsystem.ThenewretrievalsystemhasbeenoperationalizedattheSounderScienceInvestigator-ledProcessingSystem(Sounder-SIPS)atJPL,whichactsasaclearinghousefornewNASAsounderalgorithms,andwillsoonbedeliveredtotheGoddardEarthScienceDataandInformationServicesCenter(GESDISC)forproduction,archivinganddistribution.RAMSESwillbeusedtoprocessallATMSdatafromS-NPPandtheJPSSseriesandwillalsobeconfiguredforAMSUandusedtoreprocessdatafrombothAquaandNOAAplatformsgoingbackto1998.WewillgiveanoverviewofRAMSESandshowsampledataandwillalsodiscussplannedupgradestotheretrievalsystem,primarilyaccountingforprecipitationbytheuseofadifferentradiativetransfercode,suchastheCommunityRadiativeTransferModel(CRTM).Copyright2019CaliforniaInstituteofTechnology.Governmentsponsorshipacknowledged.10.03 ContinuityinSoundingProductsfromMultiplePlatforms–examplesfromCLIMCAPSandNUCAPSPresenter:NadiaSmith,ScienceandTechnologyCorporationAuthors:NadiaSmith,Chris.D.Barnet,RebekahEsmailiHyperspectralinfraredsoundersmeasureemittedradianceatthetopofatmosphere.Retrievalalgorithmsinvertthesemeasurementsintoprofilesoftemperature,moistureandtracegasspeciestocharacterizetheatmosphere’sthermodynamicstructureandchemicalcomposition.HereweintroducetheNASAoperationalalgorithm,CLIMCAPS(CommunityLong-termInfraredMicrowaveCombinedAtmosphericProductSystem),designedtoachievecontinuityinglobalsoundingproductsacrossmultipledecades(from2002onwards,startingwiththelaunchofEOS/Aqua)tocharacterizeclimateprocesses.WecontrastCLIMCAPSwiththeNOAAoperationalalgorithm,NUCAPS(NOAA-UniqueCombinedAtmosphericProcessingSystem),designedtoretrievesnapshotsoftheatmosphericstatefrompolar-orbitingplatformstocharacterizeinstantaneousweatherandchangewithinthediurnalcycle.Differencesininstrumentdesign,spatialsampling,spectralcoverageandresolutionchallengeproductcontinuityinmulti-platformoperationalsystems.Wepresentresultstocharacterizetheseeffectsanddemonstratehowretrievalsystemdesigncanaffectcontinuityintheinformationcontentandsystematicuncertaintyofretrievalproducts.

10.04 RetrievaloftheradiativefluxandatmosphericverticalheatingrateprofilesinthethermalinfraredwiththeIASIinstrumentsonboardtheMetopplatformsPresenter:YoannTellier,CentreNationaldeRechercheScientifique(CNRS),LaboratoiredeMétéorologieDynamiqueAuthors:YoannTellier,CyrilCrevoisier,RaymondArmante,Jean-LouisDufresneandVirginieCapelleOneofthefundamentaldriversofourclimatesystemisthebalancebetweenthenetincomingsolarradiationandtheoutgoingradiationfromtheEarthanditsatmosphere.OutgoingLongwaveRadiation(OLR)isthegloballongwaveradiationoutgoingfromtheEarth-Atmospheresystem,integratedoverallangles.AnaccurateestimateofOLRisthusparticularlyimportanttostudythevariabilityofEarthclimate.Forthepastdecades,severalspacebornemissionsaimedatmeasuringtheoutgoingradiationoftheEarthsystem.Sincetheyears2000s,hyperspectralsoundershaveallowedtheretrievalofverticallyresolvedatmosphericparameters.Inparticular,thehyperspectralsounderIASI,launchedonboardtheplatformsMetop-A(2006),Metop-B(2012)andMetop-C(2018)alreadyoffersmorethan12yearsofmeasurementofseveralessentialclimatevariables,withthepotentialforofferingatleasttwodecadesofobservation.Owingtoitscoverageofalargeportionofthelongwavespectrum,itoffersthepossibilitytoderivedEarth’soutgoinglongwaveradiation(OLR)andverticallongwavecoolingrate,andtostudytheirvariabilityonclimatictimescales.Inthisposter,wewillpresenttheretrievalofoutgoinglongwaveradiationandverticallongwavecoolingratefromIASI.Forthisobjective,theradiativetransfercode4AhasbeenmodifiedtocomputetheOLRandtheverticalsourcesandsinksofradiativeenergy.ValidationofthecodeisperformedinthecontextoftheRadiativeForcingModelIntercomparisonProject(RFMIP).Usinganonlinearinferenceschemebasedonneuralnetworksand4A,OLRandcoolingrateareretrievedfromIASIobservations.WewillpresentthefirstresultsiftheseretrievalsandanalyzethembycharacterizingthelinksexistingbetweenthethermodynamicstateoftheatmosphereandtheverticalrepartitionofthesourcesandsinksofEarthradiantenergy.Session10:Retrievals(posterintroductions)10p.01 AtmosphericprofileretrievalusingrapidscanobservationofGeo-KOMPSAT-2ASatellitePresenter:Hee-JungKang,NMSC/KMAAuthors:Hee-JungKang,Tae-MyungKim,Myung-HwanAhn,Sung-RaeChung,andSeonghoonCheongTheAdvancedMeteorologicalImager(AMI)onboardtheGeo-KOMPSAT-2A(GK-2A)launchedintothegeosynchronousorbitaround128.2Eon5December2018,andwillstartoperationinJuly2019aftersomefunctionalandintegratedperformancetestscompleted.OverFullDisk(FD)andExtendedLocalArea(ELA)centeredontheKoreanPeninsula,AMIprovides16channelimageriescoveringfromthevisibletothethermalinfraredbandsevery10minutesand2minutes,respectively.GK-2Ameteorologicalproductshavealotofpotentialinnowcastingconvectiveactivity,trackingmovementofaerosols,andimprovementoftheNumericalWeatherPrediction(NWP)modelforecasts.ItisexpectedthatAMIverticaltemperatureandhumidityprofileswillespeciallycontributetomonitoringsevereweatherbecauseitisusedtocalculateatmosphericinstabilityindices.ThealgorithmforAMIatmosphericprofile(AAP)wasdevelopedtoretrieveitoverclearsky,whichisidentifiedbyGK-2Aclouddetectionproduct.Thealgorithmisbasedontheone-dimensionalvariational(1D-VAR)methodusingbiascorrectedbrightnesstemperatures(BTs)fromAMI9channels(6.2um,6.9um,7.3um,8.6um,9.6um,10.4um,11.2um,12.4um,and13.3um)andsimulatedBTsasafirstguess.ThesimulatedBTsaregeneratedfromtheKMAoperationalglobalNWPsystem(GlobalDataAssimilationandPredictionSystem;GDAPS)forecastsusingtheRadiativeTransferforTOVS(RTTOV)v12.1withAMIcoefficients.ItisdemonstratedthatthealgorithmincreasesthequalityofhumidityinformationthroughanapplicationofthealgorithmtoAdvancedHimawariImager(AHI)data,asaproxydata,overtheFDarea.Consideringoperationaluseafterlaunching,optimizationfortheELAisessential.BecausetheradiancesovertheELAismorebiasedthanradiancesovertheFDarea,thebiascorrectioncoefficientsstatisticallycalculatedfromAMIobservationandNWPanalysisovertheELAshouldbeappliedtothealgorithm.ThispresentationwillgivepreliminaryresultsofinitialoptimizationofAAPalgorithmforlocalareausingrapidscanmeasurements.

10p.02 FirstresultsfromtheMetop-CIASILevel2cal/valPresenter:ThomasAugust,EUMETSATAuthors:MarcCrapeau,ThomasAugust,TimHultberg,StefanStapelberg,AnneO’Carroll,GaryCorlettGeophysicalparametersfromtheIASIinstrumentsonMetop-AandMetop-BareprovidedfromEUMETSAT’sCentralFacilityinnearrealtimesince2007forMetop-Aandsince2013forMetop-B.TheEUMETSATIASILevel2(L2)suiteincludesverticalprofilesoftemperatureandhumidity,cloudinformation(coverageandtopheight),surfaceskintemperatureandemissivity,andatmosphericcompositionparameters(e.g.CO,O3,SO2,CH4).Metop-C,thethirdofaseriesofthreeplatformsintheEUMETSATPolarSystem(EPS)programmewassuccessfullylaunchedNovember2018,7th,bringingthenumberofflyingIASIinstrumentstothree.WepresentherevalidationresultsfromtheMetop-CIASILevel2commissioning.InparticularweprovideanoverviewofthestatusoftheIASILevel2productscomingfromMetop-Candcomparetheiraccuracytotheproductsfromtheothertwoplatforms.IASIinstrumentson-boardMetop-Aand-BhaveshownverysimilarlevelsofperformanceleadingtoverycloseIASILevel2productsandwecanexpectacomparablebehaviourforMetop-C.Wepresentperformanceassessmentsofthetemperatureandhumidityprofilesretrievalsfromtheroutinemonitoringagainstsondesmeasurementsandagainstmodelanalyses.Surfacetemperatureretrievalsareevaluatedwithinsitumeasurementfortheseasurfacetemperature(buoys),withmodelanalysesaswellaswithothersatelliteproducts(e.g.SEVERILST).Thecloudparameters(mask,toppressureandfractionalcoverage),whichconstituteacrucialinformationfortheuseofIASIL2products,areevaluatedbyvisualinspectionofthescenes(e.g.withAVHRR)andquantitativecomparisontoexternalreferencedatasets.10p.03 AnadaptativeOEMretrievalforIASIPresenter:ThomasAugust,EUMETSATAuthors:ThomasAugust,TimHultbergOptimalestimation,e.g.asformulatedinRodgers2000,ispopularinversemethodforatmosphericsounding.ItbuildsonBaye’stheoremandrequiresaccurateobservationandapriorierrors,assumedofGaussianstatistics.Theseerrorsareusuallyevaluatedoff-lineoftheretrievalandappliedgloballyforagivenobservingsystem.InthecontextoftheoperationalIASIL2processoratEUMETSAT,theobservationerrorhasbeendefinedstaticallyasthecovarianceofobservedandcalculatedradiances,usingthefirstguessstatevectorandRTTOVasaforwardmodel.TwodistinctobservationerrormatricesaredefinedforlandandmaritimescenessincethereleaseofIASIL2v6.3,withtheaimtoaccountforusuallymoreaccurateknowledgeofsurfaceemissivityandtemperatureoveroceansthanovercontinentalsurfaces.Thisleadalsotomoreaccuratehumidityretrievalinthelowtroposphere.Inthiswork,westudythefeasibilityandadvantagesofdynamicallyapplyingscene-dependentobservationerrorstoeachindividualretrievalinstancefromIASI.First,distinctobservationclassesareestablishedoff-lineforIASIbyapplicationofunsupervisedK-meanclusteringtoarepresentativesetofobservations,basedontheirleadingprincipalcomponents.AdifferentobservationerrormatrixiscomputedasthecovarianceofOBS-CALCineachoftheseclasses.Weanalysethegeographicaldistributionoftheobservationclasses,theirseasonalvariationsandtheamplitudeofthecorrespondingobservationerrors.Second,tocompensateforsystematicdifferencesbetweenobservationsandforwardmodelling,ascene-dependentbiascorrectionisregressedasafunctionofIASIobservationsandviewinggeometry(e.g.satellitezenithangle,elevation).Finally,duringtheonlineretrievalprocess,eachindividualobservationisassociatedtoitsnearestclassintheaboveclusteringandtheOEMretrievalisconfiguredwiththecorrespondingbiascorrectionandobservationerror.WewillcomparetheretrievalsperformedwiththestaticandthenadaptativeOEMconfigurationtoradiosondesandnumericalmodelanalyses.Theirrespectiveperformanceswillbediscussed,takingalsointoaccounttheaddedcomplexitywithgrowingobservationclasses.

10p.04 NearRealTimeActiveFiresandGAASPLevel-2ProductsViaDirectBroadcastUsingtheCommunitySatelliteProcessingPackagePresenter:GeoffCureton,CooperativeInstituteforMeteorologicalSatelliteStudies,UW-MadisonAuthors:GeoffCuretonTheCooperativeInstituteforMeteorologicalSatelliteStudies(CIMSS)hasalonghistoryofsupportingtheDirectBroadcast(DB)communityforvariouslow-Earth-orbit(LEO)sensors,previouslywiththeInternationalMODIS/AIRSProcessingPackage(IMAPP)fortheNASAEOSpolarorbitersTerraandAqua,andcurrentlywiththeCommunitySatelliteProcessingPackage(CSPP)fortheNOAApolarorbitersSuomi-NPPandNOAA-20.CSPPhasbeensignificantinencouragingtheearlyusageofSuomi-NPPdatabyUSandinternationalweatheragencies,andthissituationshouldcontinuewithNOAA-20andbeyond.CSPPsupportforNOAApolarorbiterstodatehasrestedupontheAlgorithmDevelopmentLibrary(ADL)developedbyRaytheon,arefactoringofthesciencecodeintheInterfaceDataProcessingSegment(IDPS),theNOAAoperationalprocessingsystem.MorerecentlyvarioussciencealgorithmsarebeingprovidedforDBintheDeliveryAlgorithmPackage(DAP)format.ExamplesofbothADLandDAPpackageswillbepresented:theActiveFirespackageforS/NPPandNOAA-20,andtheGCOM-W1AMSR2AlgorithmSoftwarePackage(GAASP),fortheretrievaloftotalprecipitablewater(TPW)andcloudliquidwater(CLW).10p.05 ComparisonofPCRTM-derivedCrISretrievalsoftemperature,watervapor,andtracegases(O3,CO,CH4,andN2O)within-situmeasurementsPresenter:Hyun-SungJang,NASALaRC/NASAPostdocProgramAuthors:Hyun-SungJang,XuLiu,andWanWuAphysicalretrievalalgorithm,whichusesaPrincipalComponent-basedRadiativeTransferModel(PCRTM),wasappliedonsix-year(2013-2018)CrISmeasurementsaboardSuomiNationalPolar-orbitingPartnership(S-NPP)satellitetoderivethermodynamicpropertiesandtracegasesprofiles.PCRTMisanultra-fastforwardmodelthatallowstheretrievalalgorithmtousetheprincipalcomponentsofspectralinformationfromallCrISchannelstofindanoptimalsolution.Retrievalresultsoftemperature,watervapor,andozonearecomparedwiththosefromGCOSReferenceUpper-AirNetwork(GRUAN)dataandSouthernHemisphereADditionalOZonesondes(SHADOZ)data.WealsousedaircraftmeasurementsfromAtmosphericTomographyMission(ATom)tovalidateretrievalfortracesgasesincludingCO,CH4,andN2O.Inthisstudy,wechoosein-situmeasurementsthataretemporallyandspatiallycollocatedtobewithin15kmand30minutesofCrISFieldofViews(FOVs).Statisticsofbiaserror,rootmeansquareerror,andstandarddeviationaredemonstratedtoassessbaselineperformanceofthealgorithm.OurresultsshowthatthePCRTM-basedphysicalretrievalalgorithmcanbeusedforsingleFOVretrievalunderall-skycondition.10p.06 SatelliteInter-comparisonofGeostationaryGIIRSandPolar-orbitingIRSoundersCrISandIASI:Radiances,ThermodynamicRetrievals,andStabilityIndicesPresenter:RobertKnuteson,UniversityofWisconsin-MadisonSpaceScienceandEngineeringCenterAuthors:RobertKnuteson,JessicaMaier,PaulMenzel,HenryRevercomb,W.L.Smith,Sr.,DavidTobin,andElisabethWeiszThelonghistoryofsatelliteremotesensingofEarth’satmospherictemperatureandwatervaporverticalprofilesledtothedevelopmentofhighspectralresolutionthermalinfraredemissionspectrometerswithon-boardabsolutecalibration.TheHigh-resolutionInterferometerSounder(HIS)programatUW-MadisonSSEC/CIMSSwasstartedinthe1980’sbyNOAAandNASAasanairbornedemonstrationofthevalueofhyperspectralinfraredradianceobservationsandthermodynamicretrievals.SubsequentlytheNASAAtmosphericInfraredSounder(AIRS)waslaunchedontheAquasatellitein2002,followedbytheMETOPseriesofsatelliteswiththeInfraredAtmosphericSoundingInterferometer(IASI)in2006.TheCross-trackInfraredSounder(CrIS)joinedthesatelliteconstellationwithonNASA’sSoumi-NPPsatellitein2011andmostrecentlyontheNOAA-20operationalsatellitein2018.Allthesesatellitesarecontributingtotheglobaldataassimilationusedbynumericalweatherpredictioncentersformediumrangeforecasting.ThefirsthyperspectralIRsounderistheGeostationaryInterferometricInfraredSounder(GIIRS),whichisoperationalaboardtheChineseFengyun-4Asatellitelaunchedin2016.Thegeostationaryplatformhastheadvantageofsamplingtheatmosphereoveraregionatmultiplepointsinthediurnalcycle.TheFY4AGIIRSsatelliteradianceshavebeenroutinelydistributedsincein24January2019bytheChinaMeteorologicalAdministration(CMA).FY4AGIIRScontainsafocalplanearrayof128detectorswhichusesaFouriertransformspectrometer(FTS)tosimultaneouslycollect128interferogramsoftheEarthsceneemission.Periodicviewsofaninternalblackbodyandadeepspaceviewareusedforcalibration.TheoperationalmodeofFY4AGIIRScoversthe

Chinaregionwitharepeatofabout2hours.Theinter-comparisonofthegeostationaryGIIRSradianceswithpolar-orbitingsatellites(CrISandIASI)hasbeenmadeusingamatchuptechnique.Constraintsareappliedintime,space,andviewingangle.Resultsarepresentedasbrightnesstemperaturebiasspectraandasspectrallineshifts.Theseradiance(orobservedbrightnesstemperature)comparisonsprovideadirectassessmentofthecalibrationaccuracyofGIIRSrelativetotheothersatellitesensorswhicharealreadyroutinelyinter-compared.Asecondobjectiveistheinter-comparisonofretrievedprofilesoftemperatureandmoisturefromthevarioussensors.Thisiscomplicatedbythemultiplicityoforganizationsanddisparatealgorithmsusedtocreatetheverticalprofiles.Finallythederivedquantitiesofatmosphericstability(CAPE,CIN,LI,etc.)arecompared.Inthisstudy,comparisonsarealsomadebetweenGIIRS-derivedverticalprofilesoftemperatureandhumidityandlocalobservationaldatafromcoincidentradiosondelaunchsites,e.g.atShanghai.Thispaperdescribesaninter-comparisonofobservedradiances,thermodynamicretrievals,andderivedstabilityindicesamongthesevarioussatellitesensorsandderivedproducts.10p.07 SoundingDataProductsgeneratedatNOAA/NESDISUsingHighSpectralResolutionInfraredandAdvancedMicrowaveSounders(CrIS/ATMS)Presenter:AwdheshSharma,NOAAAuthors:Dr.AwdheshK.SharmaTheOfficeofSatelliteandProductOperations(OSPO)ofNOAA/NESDIShasimplementedinnovativetoolstomonitorperformanceanddataqualityoftheoperationalsounderandimagerproductsthatarebeinggeneratedusingtheCross-trackInfraredSounder(CrIS),inconjunctionwiththeAdvancedTechnologyMicrowaveSounder(ATMS).Higher(spatial,temporalandspectral)resolutionandaccuratesoundingdatafromCrISandATMSsupportcontinuingadvancesindataassimilationsystemsandNWPmodelstoimproveshort-tomedium-rangeweatherforecastsandclimateapplications.Currently,theNOAAUniqueCombinedAtmosphericProcessingSystem(NUCAPS)level2productsfromMetop-A/B/C,S-NPP,andNOAA-20satellitesincludetemperatureandhumidityprofiles;tracegasessuchasozone,nitrousoxide,carbondioxide,andmethane;andthecloudclearedradiances(CCR)onaglobalscaleandtheseproductsareavailabletotheoperationalusercommunity.TheOSPOtoolshavebeenextendedtoincludetheCrIS/ATMSSKEW-T(LogarithmicPressurevsTemperatureandDewPointTemperature)soundingplotsovertheglobe.Theseplotsareupdatedeveryhourtoshowthelatestsoundingateachgridpoints(0.5X0.5degrees)overtheglobe.Thelasttensoundingsareretainedtotrackthechangesintheatmosphericconditions.TheincorporationofthesetoolsintheOSPOoperationhasfacilitatedthediagnosisandresolutionofproblemswhendetectedintheoperationalenvironment.ThispresentationwillincludeseveralofthesetoolsdevelopedanddeployedforthesoundingproductsmonitoringanddataqualityassurancewhichledtoimprovingthemaintenanceandsustainmentoftheEnvironmentalSatellitesProcessingCenter(ESPC)processingsystems.ThepresentationwillincludethediscussionontheESPCsystemarchitectureinvolvingsoundingdataprocessinganddistributionforCrISandIASIsoundingproducts.Discussionwillalsoincludetheimprovementsmadefordataqualitymeasurements,granuleprocessinganddistribution,andusertimelinessrequirementsenvisionedfromthenextgenerationofJPSSandEUMETSATsatellites.Therehavebeensignificantchangesintheoperationalsystemduetosystemupgrades,algorithmupdates,andvalueaddeddataproductsandservices.10p.08 Expandingthecapabilityofreal-timetemperature,humidity,andtracegasretrievalproductsinfieldcampaignsPresenter:NadiaSmith,ScienceandTechnologyCorp.(forRebekahEsmaili)Authors:RebekahEsmaili,NadiaSmith,ChrisBarnet,ColbyFrancoeurIntensewildfirescansignificantlyimpactregionalairqualityintheUnitedStatesandCanada.Soundingproductsareusefulforvisualizingthelong-rangetransportoftracegases(CO,O3,CH4,etc.),temperature,andmoisturefromactivefires.Thisinformationisvaluableforenvironmentalsituationalawarenessduringfieldcampaignexperiments.However,satellitesoundingparticipationinfieldcampaignsusuallyoccursaftertheconclusionoftheexperimentduetodatalatency;operationaldatafrompolarorbitingsatellitescanarriveupto3hoursafteranoverpass,outsideofNOWCASTINGrequirements.However,withNUCAPSavailableintheCSPPsuite,datalatencyisbelow30minutes,whichallowssoundingalgorithmstobeevaluatedalongsidein-situobservations.Duetoreducedlatency,fieldcampaignscannowactasareal-timesoundingalgorithmproducttestbed.

Usingthedirectbroadcastnetwork,weshowhowNUCAPSproductsfrombothexistingandnewsatellites(NOAA-20,SNPP,andtheMetOpseries)wereutilizedtoprovidediurnalmeasurementsoftracegasesduringtheFIREX-AQcampaignoverNorthAmerica.Fortheexperiment,weprovideddirectreadoutviaweb-basedtoolstocampaigncoordinators.Parametervisualizationsweretailoredtothecoordinatorsneedsasthecampaignprogressed.Thisworklooksbeyondwhetheraproductcanmeet“requirements;”ratherweexaminehowitcanbeimprovedtofacilitatereal-timeplanningandforecasting.Throughthiswork,wediscussifsoundingproductsareaviabletoolduringfieldcampaignsandhowproductscanbeimprovedtorisetothechallenge.10p.09 TowardsaFurtherUseofSatelliteObservationsforaBetterDefinitionofSurfaceTemperaturePresenter:MohamedZiedSassi,CNRM/Météo-France&CNRSAuthors:ZiedSassi,NadiaFourrié,VincentGuidardandCamilleBirmanThelandsurfacetemperature(LST)isofmajorinterestinsurfaceprocessesunderstanding.However,itshighvariabilityanddependenceonsurfaceparametersmakeitsmodelizationdifficultoverland.Thesatelliteobservations,producedbyInfra-redandMicrowavesensors,helpdefiningagoodqualityLSTbyusingsurfacesensitivechannelsandemissivityatlastoretrievesurfacetemperature.ThefirstpartofthisworkprovidesacomparisonoftheMSG/SEVIRIretrievedLSTtolocalobservationsfromtwostationsincludedinAROME-Francedomain.DiurnalcyclesofthelocalLSTandtheSEVIRILSTareingoodagreementespeciallyinsummerperiod.ThesecondpartcomparesLSTvaluesthatareretrievedfromdifferentInfraredsensorsinAROME-Francemodel.WefocusedonIASIandSEVIRIsensors.FirstresultsshowencouragingagreementbetweentheinfraredsensorsIASIandSEVIRILSTs.AcomparisonduringOctoberandNovember2018underclearskyconditionsshowsanalmostnullbiasandastandarddeviationofabout1.6K.Abettersynergyisnoticedduringnight-timegivingabiasofabout0.7Kandastandarddeviationoflessthan1K.Finally,thesimulationofSEVIRIandIASIsensorsbrightnesstemperaturesbyusingthefastradiativetransfermodelRTTOVwithdifferentLSTisstudied.Thus,differentsimulationshavebeenruncoveringselectedclearskysituationsbyday-timeandnight-timeandusingSEVIRILST,IASILSTandAROMEmodelLST.Thesimulatedbrightnesstemperatureswerethenassessedagainstsatelliteobservations.10p.10 IASIL1andL2reprocessingstatusatEUMETSATPresenter:DorotheeCoppens,EUMETSAT(forMayteVasquez)Authors:MayteVasquez,StephanieGuedj,DorotheeCoppens,TimHultberg,ThomasAugust,MarcCrapeau,AlessioLattanzio,MarieDoutriaux-BoucherAspartofthepayloadoftheMetopseriesofpolar-orbitingsatellites,therearecurrentlythreeIASIinstrumentsinoperation:onMetop-A(launched19October2006),onMetop-B(launched17September2012)andonMetop-C(launched7November2018).IASIinstrumentisdesignedaroundaMichelsoninterferometerandprovidesmeasurementsin8461spectralchannelsbetween3.6and15.6microns(645-2760cm-1).120spectraareacquiredinthecross-trackdirectionwithahorizontalresolutionof12kmatnadirfromthenominal815kmhighorbit.In2018,EUMETSAThasreprocessedtheL1cproductsforIASIon-boardMetop-A(IASI-A)fortheperiod2007-2017.ThereprocessingtookintoaccountthepastevolutionsmadeintheoperationalIASIL1processingchain:

• A“Day-2evolution”algorithmslinkedtoaproductformatchangetoextendtheon-boardqualityflagsinformationandtoincludecloudandland/seamask;

• AchangeinthespectralharmonizationprocessinFebruary2011.ThereprocesseddatahavebeengeneratedwiththelatestversionoftheIASIL1processingchain(version8.0)anditsoptimizedinstrumentandprocessingtuning.Itprovidescontinuousdatarecords(gapshavebeenfulfilled).WepresentherethecompletereprocessingcycleofIASIlevel1products.ThankstothehomogeneousandhighqualityIASIlevel1cdata,EUMETSATisalsointendingtoreprocessthelevel2productsfortheentireperiodcoveredbyIASIonMetop-A.Preliminaryresultswillbepresentedusingfirstthestatistical‘all-sky’retrieval(PWLR3)fortheentireperiodaswellasthefuturesplans.

10p.11 Anexperimental2DVARretrievalusingAMSR2Presenter:DavidDuncan,ECMWFAuthors:DavidDuncan,PatrickEriksson,SimonPfreundschuhAtwo-dimensionalvariationalretrieval(2DVAR)hasbeendevelopedfortheAdvancedMicrowaveScanningRadiometer-2(AMSR2)sensor.OverlappingbeampatternsatallAMSR2frequenciesareexplicitlysimulatedintheforwardmodel.Thispermitsretrievalofnearsurfacewindspeed(WSP)andSSTatfinerspatialscalesthanindividualantennabeams,withtheeffectivespatialresolutionofretrievedparametersshownbyanalysisof2DVARaveragingkernels.SSTretrievalsachieveabout30?kmresolution,withWSPreachingabout10?kmresolution.Itisarguedthatmulti-dimensionaloptimalestimationpermitsgreateruseoftotalinformationcontentfrommicrowavesensorsthanothermethodslikeBackus-Gilbert.Nocompromisesontargetresolutionareneededandinsteadvarioustargetsareretrievedatthehighestpossiblespatialresolution,drivenbythechannels'sensitivities.AllAMSR2channelscanbesimulatedwithinneartheirpublishednoisecharacteristicsforobservedclear-skyscenes,thoughcalibrationandemissivitymodelerrorsarekeychallenges.Thisdemonstratesthefeasibilityof2DVARforcloud-freeretrievals,andopensthepossibilityofstand-alone3DVARretrievalsfromimagersandsoundersthatincluderetrievinghydrometeorfields.Theresultsindicatethatspatialoversamplingcansomewhatmitigatetheneedforlargerantennasinthepushforhigherspatialresolution.Therelevanceoffieldofviewmodellingforsoundingapplicationsandtheapplicabilityoftheseresultswillalsobediscussed.Session11:Validation(oralpresentations)11.01 Combiningsatellite-withground-basedmeasurementsfornear-real-timemonitoringofatmosphericstability,atmosphericwatervaporandliquidwater.Presenter:MariaToporov,UniversityofCologneAuthors:MariaToporov,UlrichLöhnert,ChristopherWilliamFrankShort-termforecastsofcurrenthigh-resolutionnumericalweatherpredictionmodelsstillhavelargedeficitsinforecastingtheexacttemporalandspatiallocationofsevere,locallyinfluencedweathersuchassummer-timeconvectivestormsorcoolseasonliftedstratusorgroundfog.Thethermodynamicinstability-especiallyintheboundarylayer-playsanessentialroleintheevolutionofweatherevents.Onewaytoassesstheatmosphericinstabilityofferso-calledforecastorstabilityindices(STI).Thetemporalandspatialresolutionofradiosondesoundings,whicharetraditionallyusedforcalculationofSTI,isnotsufficienttocapturetheinitiationandthedevelopmentofconvection.Ground-basedremotesensinginstrumentsandinstrumentsonboardofgeostationarysatellitesprovidehightemporallyresolvedinformationonverticalstructureoftheatmosphereandcanbeusedformonitoringofatmosphericstability.Previousstudiesshowedthatmicrowaveprofilers(MWR)arewellsuitedforcontinuouslymonitoringthetemporaldevelopmentofatmosphericstability(Ciminietal.2015).However,theverticalresolutionofmicrowavetemperatureandhumidityprofilesisbestinthelowestkilometerabovethesurface,decreasingrapidlywithincreasingheight.Typicalstabilityindices(STI)usedtoassessthepotentialofconvectionrelyontemperatureandhumidityvaluesnotonlyintheregionoftheboundarylayerbutalsointhelayersabove.Therefore,satelliteremotesensingisexpectedtocomplementground-basedMWRandDIALobservations.Inthiscontribution,wepresentaneuralnetworkretrievalofstabilityindices,integratedwatervapor(IWV)andliquidwaterpath(LWP)fromsimulatedsatellite-andground-basedmeasurementsbasedontheCOSMO-REA2reanalysisastruth.Inordertomaketheapproachfeasiblefordataassimilationapplications,wesimulatesatelliteobservationswiththestandardRTTOVmodelandusethenewlydevelopedRTTOV-gb(ground-based)fortheground-basedmicrowaveradiometers(DeAngelisetal.,2016).Focusingonthetemporalresolutionandspatialcoverage,thesatellite-basedinstrumentsconsideredinthestudyarethecurrentlyoperationalSEVIRIandthefutureIRS,bothingeostationaryorbit.Weshowthesingleinstrumentperformanceandthesynergybenefitintermsofcorrelation,uncertaintyreduction,probabilityofdetectionandotherforecastskillscores.ThehyperspectralgeostationaryIRSobservationscontainsignificantlymoreinformationonverticalhumidityandtemperatureneededforassessmentofatmosphericstabilitythanSEVIRImeasurements.ThustheerrorofSTIretrievedfromsimulatedIRSobservationswasshowntodecreasebyupto50%comparedtoSEVIRI.The

additionalground-basedMWR/DIALmeasurementsprovidevaluableimprovementsnotonlyinthepresenceofclouds,whichrepresentalimitingfactorforinfraredSEVIRI/IRS,butalsounderclearskyconditions.Toassessthespatialrepresentativenessofobservationsofasingleground-basedMWR/DIALandtoestimatetherequirednetworkdensitytheretrievalisappliedtoa150*150kmreanalysisdomain.TheaccuracyoffieldsofSTI/IWV/LWPcalculatedfromsatelliteonlyandfromcombinedsatelliteandground-basedobservationsisestimatedandtheaddedvalueofground-basedobservationsinanetworkconfigurationisdiscussed.11.02 InvestigatingtheComparisonsofHyperspectralIRSounders,RadioOccultation,andRadiosondesinRadianceSpacePresenter:MichelleFeltz,UniversityofWisconsin-Madison,SSECAuthors:MichelleFeltz,LoriBorg,RobertKnuteson,DaveTobin,HankRevercomb,JohannesNielsenInrecentdecadestheimportanceofensuringcontinuityandconsistencybetweenmeteorologicalsatellitedatasetshasbeenhighlighted.Asevidence,GSICSwascreatedin2005asaninternationalefforttoharmonizethequalityofoperationalweathersatellites,projectsunderNASA’sMEaSUREshavebeenfundedtocreatemergeddatarecordsusingpre-existingdatasets,andnetworkslikeGRUANhavebeencreatedtocoordinatemeasurementsofessentialclimatevariables.Lessfocus,however,hasbeenputonthecomparisonsofsatellitedatasetswhichhavedifferentmeasurementtechniques—forexamplebetweenpassiveandactiveremotesensingtechnologies.Thoughmuchworkhasbeenpreviouslydonetocomparetheactiveradiooccultation(RO),passivehyperspectralinfrared(IR)sounder,andradiosonderetrievalsfortemperatureandwatervaporprofilevalidationpurposes,lessworkhasbeendonecomparingtheminradianceorrefractivityunits—whereeachtheIRsounderandROhavemuchsmalleruncertaintiesontheirmeasurements.Thisworkprovidesafollow-uptopreviousstudieswhichprovedthathyperspectralIRradiances,withtheirprescribeduncertainties,canbeusedasavalidationreferenceforROtemperatureretrievalsviaradiativetransferforchannelsrepresentingtheuppertroposphereandlowerstratosphere.CasestudymatchupsofhyperspectralIRsounders,RO,andradiosondesareusedwithradiativetransfertocomparethesedatasetsinradianceunitsforthepurposesof1]furthercharacterizingandunderstandingtheirdifferencesoverthefullIRspectraldomain,2]investigatingthefeasibilityofusingtheIRsounderradiancesastruthforchannelswhichrepresentdifferentregionsofthetroposphere,giventheincreaseduncertaintiesoftheradiativetransferalgorithmforvariousspectralchannels,and3]prescribingmethodologicaluncertaintiestothecomparisons.11.03 Ground-basedremotesensingnetworkforthevalidationofmulti-scalesatelliteproductsandnumericalmodelsPresenter:EricPequignot,SentinAirAuthors:EricPequignot,JavierAndreySentinAirwascreatedinApril2018.Itisaspin-offofCNESthatbenefitsfromitssupportintheframeworkofits“swarmingprogram”whichaimstopromotespaceinnovationsandpatents.SentinAiriscurrentlyusingapatentintheframeofanexclusivelicenseprovidedbyCNESandACRI-STonatmospherictomographyfollowingWINTIstudies(R&T2008-2013andPhase02015-2016).WINTIisa0.5-1kmresolutioninfraredmulti-spectralimageroperatinginLEOorbit.Itperformsatomographicacquisitionwhichconsistinascanfrombackwardtoforwardlimb.RetrievalsofatmosphericfieldsaredonebyusingatomographicBayesianscheme.TheobjectiveofSentinAiristoadaptthisspacetechnologyongroundbydeployingandoperatingautonomousoutdoorground-basedcameranetworksinordertoprovideretrievalsofgeophysicalvariableswithintheboundarylayer(temperature,watervapor,aerosols,tracegases,cloudcoverage)at100m(horizontalandvertical)with15minresolution.Eachnetworkwouldcovertypicallya20x20kmarea.The2componentsofanetworkarethefollowing

• TheAirCamswhichareautonomousdevicesembarkingdifferenttypesofstate-of-the-artcameras(ultraviolet,visibleandinfrared).TheAirCamsaresetuponhighspotswithinandaroundtheareatobeobserved.TheAirCamsarecurrentlyunderdefinition.

• AcentralizedmissioncentrethatprocessestherawimagesacquirebytheAirCams.

Themainaddedvaluesofsuchanetworkwithrespecttosatelliteproductandnumericalmodelvalidationarethefollowing

• “HR”:acontinuousmeasurementoveralargearea(20x20km)athigh-resolution(100m/15min).Thisisauniquetoolformulti-scaleandmulti-orbitEOsatellitedatavalidationandinter-comparison(chemistry,meteorologyandairquality).

• “Scalableproduct”:Aproductwithascalethatcanbeadaptedtodifferentnumericalmodelandsatellitedataresolution.

• “ABL”:3DAtmosphericBoundaryLayersoundingathighverticalresolution(100m)• “2-in-1”:anopportunitytocoupleinthesameprojectsatellite/modelvalidationandaservicethat

couldbeusefulforcitiesconsideringairquality,greenhousegasesandurbanheat-islandissues.Session11:Validation(posterintroductions)11p.01 ExtendedcharacterisationofNWPmodelbiasesanduncertaintiesacrossthemicrowaveandinfrareddomainsPresenter:FabienCarminati,MetOfficeAuthors:FabienCarminati(MetOffice),StefanoMigliorini(MetOffice),BruceIngleby(ECMWF),HeatherLawrence(ECMWF)Withtheimprovementofnumericalweatherprediction(NWP)modelskillsthroughadvancesinmodelling,dataassimilation,anddatausage,therepresentationoftheatmosphereinanalysesandforecastsisincreasinginreliability.Thisisnotonlybenefitingthepublicandprivatesectorsthroughbetterweatherforecastsandalerts,butalsothescientificcommunitythatcanuseNWPfieldstoevaluateinstrumentssensingtheatmospheresuchassatellitemicrowaveandinfraredsounders.However,bothanalysesandforecastsremainsubjecttonon-zerobiasesanduncertaintieswhosecharacterisationandtraceabilityarefundamentalstepstoproperlyuseNWPmodelsasreferencecomparators.ThisisinvestigatedviacomparisonsbetweenradiosondesoftheGlobalClimateObservingSystem(GCOS)ReferenceUpper-AirNetwork(GRUAN)andNWPtemperatureandhumidityfieldsmappedtoradiancespace(thespaceusedforsatellitevalidation).Top-of-atmospherebrightnesstemperaturessimulatedfromover15000GRUANprofilesoftemperature,humidity,andpressure(andtheirassociateduncertainties)from11sitesin4geographicareas(northernlatitudes,northernmid-latitudes,northersub-tropicalAtlantic,andtropicalwestPacific)obtainedovertheperiod2011-2017andcollocatedNWPfieldsfromboththeMetOfficeandECMWFglobalmodelshavebeencomparedatkeyfrequenciesspanningboththemicrowaveandinfraredspectrumsforaselectionofAMSU-A,MHS,ATMS,HIRS,CrISandIASIchannels.Preliminaryresultssuggestbiaseswithin±0.5Katmicrowavefrequenciespredominantlysensitivetotemperatureforuncertaintylessthan0.12K,and0.5to-1.5Kbiasesatfrequenciessensitivetohumidityforuncertaintieslessthan2K.Infraredfrequenciesareunderinvestigationatthetimeofwriting.AlthoughprovidinganincompletepictureduetothelackofGRUANstationsinthesouthernhemisphere,thisanalysisoffersthemostdetailedandrobustestimationofmodelfieldsbiasanduncertaintyinradiancespacetodateandtakesNWPmodelsastepclosertoatraceablecharacterisationtoSIstandards.Ultimately,thiseffortcanservetooptimisetheuseofanchorobservationsindataassimilationandalsoenhancemodelevaluationefforts.11p.02 EnterpriseAssessmentandUncertaintyEstimatesforSatelliteRetrievalsUsingCollocationswithConventionalandGRUANRadiosondesPresenter:AnthonyReale,NOAANESDISSTARAuthors:TonyRealeBalloon-borneconventionalradiosondeobservationshaveplayedplayacriticalroleintheassessmentofderivedatmosphericsoundingsincetheoperationalonsetofsuchproductsin1979.Radiosondesprovideexcellentcharacterizationsofthelargescalesynopticweatherfeaturesandareoftenvaluabletoascertaintheoverallprofileshapeandfinerscalefeaturesinaregionalandlocalcontext.Problemswithconventionalradiosondesincludethenumerousinstrumenttypesandprocessingavailablegloballythatcanintroducesystematicerrorsintheperceivedperformanceofsatellitederivedtemperatureandmoistureprofiles.Addtothisthesystematictemporalmismatcharisingfromcomparingpredominantlysynopticradiosondesandsun-synchronoussatellites,thetimedurationofradiosondes(1.5hoursto20hPa),spatialdrift(over100km)andambiguityofcomparingpointversusvolumequantities.Together,thesecanmanifestassystematicor“TimeAlias”errors,however,resultsshowninthispapersuggestsucherrorsaresmall,beatendownbythevery

largesamplesizesofconventionalobservations(over1000perday)comparedtotheinherentsatelliteproducterrorsignal.Nonetheless,assessmentofregionaland/orperformanceinagivenair-mass(orsite)typicallyofinteresttousers(forecasters)remainsprohibitive.Asecondaryobjectiveofthispaperistoaddresstheaddedinformationonsatelliteproductperformanceobtainablefromsupplementaluseof“special”validationdatasetscontainingGlobalClimateObservingSystem(GCOS)ReferenceUpperAirNetwork(GRUAN)radiosondestargeted(courtesyofJPSS)with(NOAA)polarsatelliteoverpass.Thesedataareshowntobemuchmoreindicativeofregionalandlocal(air-mass)performanceandestimatesofthesatellitesoundinguncertaintybasedonthefullycharacterized(traceable)GRUANradiosondes.Thisreportsultimatelytracessoundingproductperformancesignalsforinternationallyavailableoperationalproductsuites(NOAA,NASAandEUMETSAT)fromtheglobaltoregionalscalesusingconventionalandspecialradiosondes,respectively.Theassessmentsareenterpriseinthatthedifferentsuitesareinter-comparedagainstthesame(common)setsofradiosondes.Verticalstatisticsofsatellite-minus-radiosondebias(andstandarddeviation)andassociatedestimatesofrespectiveproductsuiteuncertainty,includingfornumericalweatherpredictionprofilesarecompared.Impactsduetosystematicerrorsassociatedwith“timealias”effectsareshown.11p.03 ApplicationofthefastvisibleradiativetransfermodelRTTOV-MFASIS:comparisontoRTTOV-DOManduseformodelcloudvalidationofICONPresenter:ChristinaStumpf,GermanWeatherServiceAuthors:ChristinaStumpf,ChristinaKoepken-Watts,OlafStiller,LeonhardScheck,LiselotteBach,RolandPotthastMFASISisanovelfastradiativetransfermethodforthesimulationofvisiblesatelliteimagesthatisfastenoughtocopewiththecomputationalconstraintsofoperationaldataassimilationsystemsandhasthereforerecentlybeenimplementedintoRTTOVv12.2andv12.3.FirstevaluationanddataassimilationexperimentsusingMFASISincombinationwithregionalmodelshavedemonstrateditsvaluebyimprovingtherepresentationofcloudcoverandprecipitation.Asafurthersteptowardsusingvisiblesatelliteimagesinoperationaldataassimilation,weperformadetailedvalidationoftheaccuracyofMFASISandapplyitinevaluatingtherepresentationofcloudsinDWD'sglobalNWPsystemICON+EnVARincomparisontovisiblechannelobservationsfromtheSEVIRIinstrumentsonboardMSG.WeevaluateRTTOV-MFASISbycomparingforwardsimulationstoresultsfromthediscreteordinatemethodRTTOV-DOMbasedonglobalICONmodelfieldswhichofferalargevarietyofatmosphericsituations.Thisisdoneinasuitabletestsetupwithcontrolledviewingconditionsandbystudyingdependenciesonrelevantquantities,suchasopticaldepthsandcloudheights,toidentifyanysystematicerrorsresultingfromtheapproximationsmadeinMFASIS.TheseinvestigationspavethewayforfurtherimprovementstoMFASIS,e.g.foranimproveddescriptionofRayleighscatteringandmixed-phasecloudsituations.Additionally,wecomparereflectancessimulatedwithRTTOV-MFASISbasedonICONmodelfieldstorealvisiblechannelobservationsusinggeostationarysatellites.Thisaimsatvalidatingtheaccuracyofthemodelcloudfields,alsoinconjunctionwithall-skysimulationsofcorrespondingIRchannels.Here,thevisiblechannelinformationiscomplementaryespeciallyfortheanalysisoftherepresentationoflowclouds.11p.04 ValidationoftheEnvironmentalDataRecord(EDR)productsuitefromtheSNPP/NOAA-20NOAAUniqueCombinedAtmosphericSoundingSystem(NUCAPS)Presenter:NicholasNalli,IMSGInc.atNOAA/NESDIS/STARAuthors:NicholasR.Nalli,A.Gambacorta,C.Tan,L.Zhou,T.Reale,B.Sun,J.Warner,T.Wang,andT.ZhuThispresentationoverviewscurrentstatusofthevalidationoftheoperationalfull-spectralresolutionCross-trackInfraredSounder(CrIS)andAdvancedTechnologyMicrowaveSounder(ATMS)NOAAUniqueCombinedAtmosphericProcessingSystem(NUCAPS)onboardtheNOAA-20andSNPPsatellites.NUCAPSisaNOAAoperationalretrievalalgorithmdesignedtoprovideuserswithglobalatmosphericprofileenvironmentaldatarecords(EDRs),includingtemperature,moisture(H2O),ozone(O3)andcarbontracegases(CO,CH4,CO2).NUCAPSEDRproductassessmentsaremadewithreferencetotheJPSSLevel1requirements.Fortheprovisionalmaturityassessments,thebaselinedatasetsincludeglobalECMWFmodel,conventionalradiosondeobservations(RAOBs),limitedozonesondeensembles(e.g.,SHADOZandWOUDC),dedicatedRAOBs(e.g.,ARM,GRUANandAEROSE),anddatasets-of-opportunity(e.g.,TCCON,ATom,AirCore).OngoingstatisticalanalysesrelativetothesebaselineshaveshownthattheNOAA-20NUCAPStemperature,moisture,ozoneandcarbonmonoxideprofileEDRshavefavorableaccuracyandglobalyield,thusmeetingthegeneralqualificationsforJPSSprovisionalmaturity.

11p.05 FIREX-AQER-2:ASummaryofScanningHigh-resolutionInterferometerSounder(S-HIS)ObservationsPresenter:JoeTaylor,UW-SSECAuthors:JoeK.Taylor,DavidC.Tobin,HenryE.Revercomb,FredA.Best,RayK.Garcia,WilliamSmithSr.,ElisabethWeisz,R.BradleyPierce,GregFrost,MitchGoldberg,OlgaKalashnikova,JayAl-SaadiTheFIREX-AQ(FireInfluenceonRegionaltoGlobalEnvironmentsandAirQuality)isajointventureledbyNOAAandNASA,andprovidescomprehensiveobservationstoinvestigatetheimpactonairqualityandclimatefromwildfiresandagriculturalfiresacrossthecontinentalUnitedStates.FIREX-AQbringstogetherscientistsfromNOAA,NASAandmorethan40partnerstoexplorethechemistryandfateoftracegasesandaerosolsinsmokewithinstrumentedaircraft,satellites,UAVsandground-basedinstrumentationinthenorthwesternandsoutheasternU.S.duringthesummerof2019.TheoverarchingobjectiveofFIREX-AQistoprovidemeasurementsoftracegasandaerosolemissionsforwildfiresandprescribedfiresingreatdetail,relatethemtofuelandfireconditionsatthepointofemission,characterizetheconditionsrelatingtoplumerise,followplumesdownwindtounderstandchemicaltransformationandairqualityimpacts,andassesstheefficacyofsatellitedetectionsforestimatingtheemissionsfromsampledfires.TheairbornecomponentoftheFIREX-AQeffortiscenteredonthedeploymentoftheNASADC-8,withtwocomplementarilyoutfittedNOAATwinOtters,andwillsamplewildfireplumesfromnearthepointofemissiontodownwindonaregionalscale.TheNASAER-2isalsodeployedforFIREX-AQandplaysakeyroleintheexperiment.ThegoalfortheNASAER-2istoserveasabridgebetweenin-situandsatellitedatasetsbyusinganairborneremotesensinginstrumentsuitetohelpcharacterizefiredevelopment,emissionprocesses,plumeevolution,anddownwindimpactsonairquality,andevaluateandvalidaterecentlydevelopedremotesensingapproachesandalgorithms.TheER-2componentofthecampaignisconductedfromPalmdale,California.TheNASAER-2payloadconsistsoftheUW-SSECScanning-HighresolutionInterferometerSounder(S-HIS),theNASAJPLAirborneMultiangleSpectroPolarimetricImager(AirMSPI-1),theNASAJPLClassicAirborneVisibleandInfraRedImagingSpectrometer(AVIRIS-C),theNASAGSFCCloudPhysicsLidar(CPL),andtheNASAARC/GSFCEnhancedMODISAirborneSimulator(eMAS)forthedurationoftheexperiment.TheNASAGSFCGeostationaryCoastalandAirPollutionEventsAirborneSimulator(GCAS)andtheNASALaRCNPOESSAtmosphericSounderTestbed-Interferometer(NAST-I)arealsopartofthepayloadforfirsthalfoftheexperiment,andarereplacedwiththeNASAJPLHyperspectralThermalEmissionSpectrometer(HYTES)forthesecondhalfoftheexperiment.ThispresentationwillincludeanoverviewofFIREX-AQER-2campaignandpresenthighlightsofsomeofthemostnotableS-HISobservationsmadeduringthecampaign.11p.06 ReconcilingopposingPacificWalkercirculationtrendsinobservationsandclimatemodelprojectionsPresenter:Eui-SeokChung,IBSCenterforClimatePhysicsAuthors:Eui-SeokChung,AxelTimmermann,BrianSoden,Kyung-JaHa,LeiShi,andVijuJohnSurfacepressureobservationsandreanalysisdatasetssuggestthatthePacificWalkercirculationhasstrengthenedoverthesatelliteera(1979-present).Giventhelimitedtimeperiod,suchchangescouldarisefromeitherinternalvariabilityoraforcedresponsetoanthropogenicwarming.Here,weevaluatethesehypothesesbyanalyzingsatelliteobservationsalongwithalargeensembleofclimatemodelsimulationsforcedbyhistoricalradiativeforcings.Whilethesatellite-observedchangesdiffernoticeablyfromtheensemblemeanchangespredictedbythemodels,theyalsoindicatemuchlessstrengtheningofthePacificWalkercirculationthanimpliedbyreanalysisdatasets.Inparticular,thesatelliteobservationsrevealanomalouslyhighconvectiveactivityoverthePhilippineSeaandthenorthwesternpartoftheIndianOcean,ratherthanovertheMaritimeContinentandtheequatorialwesternPacificasinthereanalyses.Furthermore,somemembersoftheclimatemodelensemblearefoundtoreproducealargepartoftheobservedchanges.Thesefindingsoffercompellingevidencethatinternalvariability,ratherthananthropogenicforcing,hashadadominantinfluenceontherecentstrengtheningofthePacificWalkercirculation.

11p.07 GeneratingsyntheticvisiblesatelliteimageswithRTTOVPresenter:OlafStiller,DeutscherWetterdienstAuthors:LeonhardScheck,OlafStiller,ChristinaKöpken-Watts,BernhardMayer,MartinWeissmannSatelliteimagesinthevisiblespectralrangeprovideawealthofinformationabouttheclouddistribution,cloudmicrophysicalpropertiesandcloudstructureandareavailablefromgeostationaryandpolarorbitingplatformsathightemporalandspatialresolution.Therefore,theseimagesareseenasapromisingtypeofobservationfordataassimilationandmodelevaluationandareinmanyrespectscomplementarytoinfraredandmicrowavesounderdata.Whilethermalinfraredandmicrowavechannelshavebeenusedforthesepurposesforsometimeandarewell-supportedbypackageslikeRTTOV,sufficientlyfastandaccurateforwardoperatorsforvisiblechannelshaveonlyrecentlybecomeavailable.Thisisrelatedtothefactthatmultiplescatteringmakesradiativetransferatsolarwavelengthsmorecomplicatedandcomputationallymuchmoreexpensive,ifstandardradiativetransfermethodsareused.HerewedescribeMFASIS,afastmethodforthegenerationofvisiblesatelliteimages,whichisbasedonacompressedlook-uptableandhasrecentlybeenintegratedintoRTTOV.Wediscussthebasicdesignideasandseveralrecentextensionsaimedatimprovingtheaccuracyofthemethodandthecoveredfrequencies,includingmethodstoaccountfor3DRTeffectsandtheimpactofvariablewatervaporcontent.Moreover,firstresultsforanalternativeapproachareshown,inwhichthelook-uptableisreplacedbyaneuralnetwork.11p.08 Usingensemblebaseddiagnosticstoidentifysub-optimallyusedobservationsPresenter:OlafStiller,DeutscherWetterdienstAuthors:OlafStillerObservationimpactdiagnosticshavebeendevelopedtoassesstheimpactofsubgroupsofobservationsinthedataassimilation(DA)processwithouttheneedforperformingdatadenialexperiments.Thesediagnosticsarebasedonacostfunctionwhichillustratestheobservationimpactwithrespecttosomeverifying"truth".Here(asinKalnayetal.2012)suchdiagnosticsareusedforanensemblesystem,theENVAR/LETKFsystemoftheDWD'sglobalICONmodelusingobservationsasverifying"truth”(asinSommer&Weissmann,2016).Whilethismakesadirectquantitativecomparisonbetweentheimpactofdifferentobservationtypesproblematic(theresultstronglydependsonhowwelltheexaminedobservationsarecollocatedwiththoseusedfortheverification),thesediagnosticsallowtoidentifysubgroupsofobservationswhicharetreatedsub-optimallyintheassimilationchain.Suchsub-optimaltreatmentmayarisefromdifferentsources.Therefore,togetmorespecificinsights,thisworkfocusesondifferenttypesofconsistencyrelationshipswhichcanbeinferredfromdifferenttermsintowhichthecostfunctioncanbedecomposed.Furthermore,whilethetraditionaldiagnosticstrytoassesstheimpactrelatedtodenialexperiments(whatisthecontributionofselectedobservationsontheanalysisinthissystem?),wealsopresentanadditionaldiagnostictypeyieldingthecorrespondingimpactwhichtheselectedobservationswouldhaveiftheywhereassimilatedalone(i.e.,inabsenceoftheotherobservationswhichareassimilated).Thiscanbeusedasanindicatortowhichextentthemeasuredimpact(orlackofimpact)mayberelatedtotheredundancyofobservationalinformation.Theposterwillmainlypresentresultsfortheimpactofwellestablishedobservationsystemslikeradiosondes,radiooccultationsandAMSU-Aradiances.ItisalsoshownhowbiasesinAMSU-Aradiancesaffecttheseimpactdiagnostics.Thefocusisonveryshortforecastleadtimestwhichispossiblebecauseobservationsareusedforverification.Acomparisonbetweenresultsfort=0(impactontheanalysis)andt=3hoursgivessomeinsightoftheroleofimbalancesintheanalysisstates(spinup/downprocesses).Session12:SatelliteDataImpactinNWP(oralpresentations)12.01 GlobalobservingsystemexperimentsintheECMWFassimilationsystemPresenter:NielsBormann,ECMWFAuthors:NielsBormann,HeatherLawrence,DavidDuncan,JackyFarnanThepresentationsummarisesresultsfromobservingsystemexperimentswiththeECMWFsystemforsomeofthemainobservationtypes,covering8monthsovertwoseasons.TheexperimentsassessthepresentimpactofMWandIRpassivesoundingradiancesinthewidercontextofimpactfromconventionalin-situobservations,bendinganglesfromGPSradiooccultation(RO)andAtmosphericMotionVectors(AMVs).

Resultsshowthatconventionalin-situobservationsandmicrowaveradiancesarepresentlythemaindriversofheadlinescores,withinfraredsoundersaddingfurtherrobustnessforawiderangeofgeophysicalvariables.Thestrongimpactofthemicrowavesatelliteradiancesisaidedbytheavailabilityofanunprecedentednumberofinstruments,providinggoodspatio-temporalcoverage.ThiscontributestoverystrongimpactonwindanalysesandforecastsfromtheMWsounders,particularlyovertheSouthernHemisphere.ThegradualimpactfromaddingMWsounderdatafromseveralorbitswillalsobediscussed,furtherhighlightingthebenefitsfromincreasedspatio-temporalsampling.12.02 ImpactofHyperspectralRadiancesin4D-VARdataassimilationsystemPresenter:BuddhiPrakashJangid,NationalCentreforMediumRangeWeatherForecastingAuthors:BuddhiPrakashJangid,BushairMT,S.IndiraRani,andJohnP.GeorgeAccurateestimateofNumericalWeatherPrediction(NWP)requiresdetailedknowledgeofthestateoftheatmosphere.SatellitebasedobservationswithhightemporalandspatialresolutionplayacrucialroleinNWP.Unlikemultispectralinstrumentsonboardmanysatellites,thehyperspectralinstrumentsprovidewidecoverageoftheatmospherewithfinespatialandtemporalresolutions.DifferenthyperspectralinstrumentsareAtmosphericInfraredSounder(AIRS)on-boardNASA-AQUAsatellite,InfraredAtmosphericSoundingInterferometer(IASI)aboardMetOp-A,B,andC,CrosstrackInfraredSounder(CrIS)on-boardSuomi-NPPandNOAA-20satellites.AIRSandCrIShave2378and2211spectralchannelsrespectively,whereasIASIhas8461.Thoughtherearethousandsofchannelsinthehyperspectralinstruments,NWPcannotbenefitthefullestoftheseinstruments,onlyafewhundredchannelswhichareveryessentialforNWPcanbeassimilated.ThisstudypresentstheimpactofhyperspectralinstrumentsintheNCMRWF’sUnifiedModel(NCUM)assimilationandforecastsystem.ForthisstudytwoObservingSystemExperiments(OSEs)aredesignedinsuchawaythati)alongwithallotherconventionalandsatelliteobservations,hyperspectralradiancesareassimilated(EXP)andii)hyperspectralradiancesaredenied(CNTL)foramonthperiod,May2018.Impactofhyperspectralradiancesintheassimilationsystemisanalyzedintermsoftheimprovedassimilationofradiancesfromotherinfraredandmicrowaveinstruments.5-dayforecastsareproducedbasedon00UTCinitialconditionofeachdayandtheimpactoftheseradiancesintheforecastsystemareanalyzedintermsofvariousskillscores.12.03 AssessmentofassimilatingMetopcombinedretrievalL2productinAROME-FrancePresenter:BrunaBarbosaSilveira,CNRM/Météo-FranceAuthors:BrunaBarbosaSilveira,VincentGuidard,NadiaFourrié,PhilippeChambon,PierreBrousseau,PatrickMoll,ThomasAugust,TimHultbergIASIL2productsfromtheEUMETSATAdvancedRetransmissionService(EARS)arestatisticalretrievals(piece-wiselinearregression)whichcombineinformationfromIASIandmicrowavesensors,thesesensorsareonboardMetopsatellites.TheproductsaregeneratedbyEUMETSATfromdirectbroadcastatlocalacquisitioncentres,suchasLannion(SatelliteMeteorologicalCentreofMétéo-France)inBrittany,France.Theproductsareavailabletoregionaluserswithinamaximumof30minutesfromsensing.ApplicationsofResearchtoOperationsatMesoscale(AROME)-Francemodelistheoperationalconvective-scalemodelofMéteo-Francesincetheendof2008.ThismodeloperationallyassimilatestheLevel1(L1)radiancesfromIASI,AMSU-AandMHS,withapositiveimpact.However,themorerecentversionofAROMEhashadthetopmodelchangedfrom1to10hPa.Asaresult,thequalityofthesimulationofchannelshavingastrongcontributionfromtheatmosphereabove10hPadecreased.Inthisway,theamountofIASIchannelsassimilatedinAROMEchangedfrom123to44channels.ThemainobjectiveofthestudyistoevaluatethebenefitsofassimilatingMetopL2productsinreplacementofL1products(radiances)fromIASI,AMSU-AandMHSintotheAROME-Francedataassimilationsystem.Firstly,theL2productsobservationerrorsandthinningweredefinedbasedonapreviouslyL2productevaluation.Threeassimilationexperimentswereperformed,thebaseline(withoutL1productsfromIASI,AMSU-AandMHS,butwithothersatelliteradiances),thecontrolexperiment(withL1productremovedinthebaselineexperiment)andL2experiment,thisexperimentassimilatesthesameobservationsassimilatedinthebaselineandL2products(temperatureandspecifichumidityprofiles).TheL2productsareassimilatedaspseudo-soundes.ThecontrolandL2experimentswereevaluatedagainstthebaselineexperiment.Theobservationsstatistics(ObservationMinusForecast(OMF),standarddeviationofOMFandnumberofobservations)andtheforecast

verification(BiasandRMSE)wereassessedtomeasuretheimpactofassimilatingtheL2productsandtheradiancesfromIASI,AMSU-AandMHSintoAROME-Francedataassimilationsystem.Session12:SatelliteDataImpactinNWP(posterintroductions)12p.01 AssessmentoftheimpactofzonalcomponentofRadiosondewinds:ApreludetotheassimilationofAeoluswindsPresenter:SIndiraRani,NCMRWFAuthors:S.IndiraRani,PritiSharma,BushairM.T.,BuddhiPrakashJangid,GibiesGeorge,SumitKumar,JohnP.GeorgeandE.N.RajagopalWindobservationsareveryimportantforthebetteratmosphericanalysisparticularlyovertheTropicswherewindfieldsgovernthedynamics.ThereisalackofhomogeneousglobalcoverageofdirectwindprofilemeasurementsinthecurrentGlobalObservingSystem.Uniformlydistributeddirectwindobservationsareimportantforsmallerscalesanddeeperatmosphericstructures.AeoluswindLidardelivershomogeneouswindsuitableforNWPassimilation.ThemainproductfromAeolusistheHorizontallyprojectedLineOfSight(HLOS)windcomponent,asinglecomponentofwindinformation,approximatelyzonalinnature.APseudoObservationSystemSimulationExperiment(POSSE)isconductedwithonlythezonalcomponentofradiosonde(RS)windstoassesstheimpactofsinglecomponentofwindinformationintheNCMRWFassimilationandforecastsystemcomparedtothefullvectorwind.SameRSprofilesareassimilatedinboththeexperiments,butfullvectorincontrolandsinglevectorinPOSSE.FullvectorisdecomposedintozonalandmeridionalcomponentsandassimilatedonlythezonalcomponentinPOSSE.Singlevectorcomponentassimilationreproducesapproximately75-80%characteristicsoffullvectorassimilation.DifferencesinmeteorologicalfieldslikeTemperature,RelativeHumidity,Windcomponents,etc.,arenoticedathigherlevelsparticularlyoverthetropicalorographicregions.12p.02 AssessmentofimpactofsatelliteradiancesonanalysisinKIAPSPresenter:Hyoung-WookChun,KIAPSAuthors:Hyoung-WookChunandHyo-JongSongQuantifyingtheactualimpactofeachobservationonforecastoranalysisisimportanttoverifythesubsetoftheobservationmaketheforecastbetterorworse.Forecastsensitivitytoobservation(FSO)methodsbasedonadjointsensitivityhaveproventobeapowerfulmonitoringtool.KoreaInstituteofAtmosphericPredictionSystems(KIAPS)hasbeendevelopinganoperationalNWPmodelincludingowndataassimilationsystemwhichishybrid4DEnVar.Unfortunately,KIAPSdon’thavetheadjointfortheforecastmodelsocannotusetheFSOmethod.KIAPSkeepuptheefforttoassessmentofimpactofobservationwiththeKalmangaininobservationspace.TheaveragedKalmangainforthesubsetofobservationistheslopeoflinearregressionbetween‘analysisincrementinobservationspace’and‘innovation,i.e.backgrounddeparturefromobservation’forthegivensamples.Preliminaryresultsshowedthatthewatervaporsoundingchannelshavelargerimpactthanthetemperaturesoundingchannels.Andmid-altitude-sensitivechannelshavelargerimpactthanhigh-altitude-sensitivechannelsinKIAPSsystem.Theseresultsarerelatedwiththemagnitudeofbackgrounderrorcovarianceandinflationfactorofobservationerrorcovariance.12p.03 ExtendedUseofHumiditysensitiveRadiancesintheDWDSystemPresenter:RobinFaulwetter,DWD(GermanMeteorologicalService)Authors:RobinFaulwetterInthelastyearsmoreandmorehumiditysensitiveradianceswereintroducedintoDWD'sglobalICON+EnVaroperationalNWPsystem.Now,humiditychannelsfromIASI,MHS,ATMS,SSMI/S,GMI,SEVIRI,GOESsounder,ABIandAHIareassimilatedoperationallywithapositiveimpact.InthisposterongoingworkaboutfurtherextendingtheuseofhumiditysensitiveradiancesintheDWDoperationalsystemispresented.Theintroductionofmorehumiditysensitiveradiancesintothecurrentsystem,e.g.additionalinstrumentslikeMWHS-2,additionalchannels,adjustmentstotheoverlystrictclouddetectionorlessthinning,degradesforecastscoresatleastpartially.I.e.thesystemappearstobe"saturated"withrespecttohumiditysensitiveradiances.Thecurrentassumptionisthatthisisduetoamodelbiasintheuppertropsherictropicalhumidityandtheinteractionbetweenmodelbiasandradiancebiascorrection.Thisproblemisanalyzed,andpossiblesolutionsarepresented.

12p.04 AssessmentoftheFY-3DmicrowaveinstrumentsatECMWFPresenter:DavidDuncan,ECMWF(forHeatherLawrence)Authors:HeatherLawrence,DavidDuncan,NielsBormannTheChineseFeng-Yun(FY)-3DsatellitewaslaunchedinNovember2017,asthefourthsatelliteoftheFY-3series,andthesecondtobeaimedatoperationaluse.ItcarriesonboardthesamemicrowaveinstrumentsasFY-3C,includingtheMicroWaveTemperatureSounder-2(MWTS-2),theMicroWaveHumiditySounder-2(MWHS-2)andtheMicroWaveRadiationImager(MWRI).InthisposterwepresentafirstassessmentofthequalityofthedataatECMWF,anddiscussimplicationsforassimilation.Observationminusbackground(O-B)statisticsareassessedandresultscomparedtosimilarinstruments,includingthoseonFY-3CandATMS,MHSandAMSR-2.FirstresultsindicatethatbothMWHS-2andMWRIhavesimilarbiasesandstandarddeviationofO–Btootherinstruments,indicatingasimilarquality.ForMWRIstatisticsareimprovedcomparedtoFY-3C,withnovisibleorbital-dependentbiases.MWTS-2hassimilarglobalbiasestoothertemperaturesoundersbutahigherstandarddeviationofO-B,duetomoreprevalentstripingandscan-dependentbiases.12p.05 ImpactsofcloudscreeningalgorithmoftheATMSonnumericalweatherpredictionmodel:ScatteringindexPresenter:JisooKim,EwhaWomansUniversityAuthors:JisooKim,Myoung-HwanAhn,Han-ByeolJeong,Chu-YongChungTheclouddetectioninthepre-processingofsatellitedataforthenumericalweatherpredictionplaysasignificantroleinselectingqualitycontrolledobservationdata.Oneoftheclouddetectionmethodusedforsucheffortsistheuseofscatteringindexwhichutilizesthedifferentialscatteringeffectsoflargesolidhydrometeorstothedifferentchannelfrequencies.Herewepresentpreliminaryresultsofimprovedscatteringindex,estimatedbyusingreal-timeclearskybrightnesstemperature(Tb)insteadofglobalaveragedclimatologicalclearskyTb,ontheinnovationstatistics,analysisfield,andforecastfields.Here,thereal-timeclearskyTbisobtainedbyradiativesimulationwiththemodelbackgroundfields.Weapplythismethodtothepre-processingoftheAdvancedTechnologyMicrowaveSounder(ATMS)intheKoreanIntegratedModel(KIM)system.Thescatteringindexisestimatedwiththedifferenceinthemodelfirstguess(FG)departuresat89GHzand165GHz.Thisreducesthefalsepositiveofclouddetectionespeciallyoversnow/icecoveredregion.TherebyitcontributestobetterutilizationofATMSobservationstodataassimilationsystem.IthasneutralimpactonthestatisticsoftheFGdeparturesofobservations.Further,theanalysisfieldisevaluatedbycomparisonwiththeEuropeanCentreforMedium-RangeWeatherForecastsIntegratedForecastSystem(ECMWF-IFS).Itshowsneutralorimprovedperformancethanthecontrolsystem.Thescatteringindexhaslargerimpactonthehumidityfieldsthanonthetemperaturefields.Furtherresultsandanalysiswillbepresentedintheconference.12p.06 UseofmicrowaveradiancesintheMetCoOpoperationalHARMONIE-AROMElimited-areadataassimilationPresenter:MagnusLindskog,SwedishMeteorologicalandHydrologicalInstitute(SMHI)Authors:MagnusLindskog,RogerRandriamampianina,AdamDybbroe,UlfAndrae,andOleVignesMetCoOpisacommonoperationallimited-areakm-scalenumericalweatherpredictionensemblesystem.ThecooperationisbetweentheNordiccountriesFinland,NorwayandSweden.Thesystemconsistsoftenensemblemembers,includinganunperturbedcontrolmember.Theinitialstatesareproducedby3-dimensionalvariationaldataassimilationmakinguseofalargeamountofobservationsfromin-situmeasurements,weatherradars,globalnavigationsatellitesystem,advancedscatterometerdataandsatelliteradiancesfromvarioussatellitesandinstruments.Herewefocusontheuseofmicrowaveradiancesintheoperationalsystem.Thecurrentuse,restrictedtoMHSandAMSU-Ainstruments,isdescribedwithrespecttodatacover,currentuseandimpactonanalysisaswellasforecast.Inaddition,thebenefitfrom,inMetCoOpsystem,recentlyintroducedmicrowaveradiancesfrominstrumentson-boardthesatellitesMETOP-C,FY-3CandFY-3Darepresented.Futureplanswillbedescribed.TheseincludeontheshorttermintroductionofmicrowaveradiancesfromNOAA-20andinonalongertermfrominstrumentson-boardplannedmissionsofconstellationofsmallmicro-satellites.Furthermore,useofcloudaffectedradiancesisconsideredimportant,aswellasanimproveduseofnearsurfacesensitivechannels.Finallysomeplansforuseofmicrowaveradiancesaround,200GHz,tobeprovided

bytheIceCloudIceCloudImager(ICI),plannedon-boardthenextgenerationpolarsatellitestobelaunchedbyEUMETSAT,willbehighlighted.12p.07 ExtendinguseofmicrowavehumiditydataoverlandattheMetOfficePresenter:StuartNewman,MetOfficeAuthors:StuartNewman,StephanHavemann,FabienCarminatiandAmyDohertyMicrowavehumidityobservationshavetypicallybeenassimilatedovertheoceansonlyintheMetOfficeglobalmodel.Recentdevelopmentsmean183GHzhumiditychannelsonMHS,ATMSandMWHS-2cannowbeexploitedoverland.Keychangesinclude:

• Extendinga1D-VarretrievalofmicrowaveemissivityandsurfacetemperaturetoincludeMHSfrequencies.

• Revisingscene-dependentobservationerrorsasafunctionofsurface-to-spacetransmittance.• Updatingqualitycontrolsusedtoscreenoutobservationsstronglyaffectedbycloudsandtheland

surface.Relativetoafullobservingsystemcontrol,globaltrialsshowbenefitsintheextratropicsfromassimilatingtheextraobservationsoverland,includingforparameterssuchasgeopotentialheightandtemperaturefields.Improvedbackgroundfitstoindependenthumidity-sensitiveobservationsindicatebenefitstotheshort-rangeforecast.ThesedevelopmentshavebeenincludedinthepackageofchangestargetedatthenextMetOfficeoperationalsuiteupgrade.12p.08 ImpactofNOAALowLatencyLEODBNetConstellationInfraredSounderDataonNCEPGFSforecastsPresenter:YoungchanNoh,CIMSS/SSECAuthors:YoungchanNoh,AgnesLim,AllenHuang,andMitchGoldbergInoperationalnumericalweatherprediction(NWP)system,satelliteobservationsthatarrivelaterthanthecutofftimearenotassimilated.Thelengthofcutofftimedependsonthefrequencyofthedataassimilationcycle.Thus,thetimelyarrivalofsatelliteobservationsattheNWPcentersisoneofthemainfactorsformaximumusageofsatelliteobservations.ThetimingofdatadeliveryfromtheNOAADBNetdependsonthedatatransmittancespeedandtheabilityofthedataprocessingsystem.Inparticular,thelow-earthorbit(LEO)satellitearemoresensitivetothedeliveringtimeofDBNetbecausetheLEOsatellitestransmitreal-timedatatothegroundstationsspreadworldwideandthenitsdataaredeliveredtotheGFSsystemfromdistantgroundstations.CurrentsatelliteinstrumentsthatcantransitviatheDNetaretheAMSU-AonboardNOAA-15,18,19,Aqua,andMetOp-A/Bsatelliteplatforms,theMHSonboardNOAA-17,18,19andMetOp-A/B,theAIRSonboardAqua,theCrIS,theATMSonboardS-NPPandNOAA-20,andtheIASIonboardMetOp-A/B.AttheNationalCenterforEnvironmentPrediction(NCEP),theDBNetisoneofthesourceswhereobservationsarereceived.Inthisstudy,weassesstheimpactofreduceddatalatencyforLEOsatellitesontheforecastperformancebyrunning,threedataassimilationexperiments;(a)acontrolrun(ctrl_n20)withtheoperationalconfiguration,(b)ifthedatalatencyisat20minutesforallLEOsatellites(laten_20m),and(c)ifthedatalatencyisat5minutes(laten_5m).Thetrialrunswereconductedfrom30Juneto22August2018.Thisincludesaseven-daysspin-upperiod(30June–6July2018),usingtheNCEPGFSatT670.Reducingthelatencytimeto5minutesincreasesthenumberofassimilatedLEOsatelliteobservationsuptoamaximumofabout20%,comparedwiththeoperationalsetup.Forecastverificationshowspositiveimpactonanomalycorrelations(AC)sandrootmeansquareerrors(RMSEs)ofkeyatmosphericvariableswithshorterdatalatency.12p.09 TheImpactofFY3D-MWRIRadianceAssimilationontheTyphoonShanshanForecastswithGRAPES4D-VarPresenter:HongyiXiao,ChinaMeteorologicalAdministrationAuthors:HongyiXIAO,WeiHAN,HaoWANG,JinchengWANG,GuiqingLIU,ChangshanXuTheassimilationofFengYun-3DMWRI(MicroWaveRadiationImager)satellitemicrowaveimagingdatainHDF5formatisperformedinGRAPES_GFS(Global/RegionalAssimilationandPrEdictionSystem–GlobalForecastSystem)by4DVAR(4-dimentionalvariational)dataassimilation.Thequalitycontrol,clouddetectionandbiascorrectionproceduresareappliedtothedataduringtheperiodfrom0300UTC4Augustto0900UTC5August2018.Thequalityofsatellitedataisassessed,andisverifiedtobeimprovedeffectively.TheinfluenceofMWRIassimilationisindicatedbytheanalysisincrement.TyphoonShanshan(No.13in2018)isselectedto

evaluatetheimpactofMWRIassimilationontheforecastingoftropicalcyclones.ComparedtotheexperimentwithoutMWRIassimilation,MWRIassimilationobviouslyimprovedboththetrackforecastandtheintensityforecastoftyphoon.ThebehaviorofsubtropicalhighisappliedtointerpretthemechanismthatMWRIradiancedataassimilationimprovesthetyphoonforecast.12p.10 BackgroundFittoSatelliteObservationsPresenter:WilliamCampbell,NRLMontereyAuthors:WilliamF.CampbellMillionsofobservationsthatarealreadymonitoredandassimilatedarealsoavailableforverificationanddiagnosticsof6-hourforecasts.Theseobservationsspantheglobeandcoverthedepthoftheatmospherefarbetterthanthemuchmorelimitedsetofradiosondescurrentlyusedforverificationinobservationspace.Wehavedevelopedatooltocomputeaquantitativemeasureofthefitofthe6-hourforecast(background)totheseglobalobservations,alongwithuncertaintyestimates.Thismethodhasseveralconsiderableadvantagesovertraditionalforecaststatisticsandscorecards.ItrequiresanNWPsimulationofonlyonetotwoweekstoaccuratelyevaluatewhethertheexperimenthasimprovedordegradedanalysesandforecasts(AlanGeer,ECMWF,personalcommunication),amuchshortertimeframethanthethreetosixmonthsofsimulationneededwithtraditionalforecaststatistics.ThesavingsinsoftwaredevelopmenttimeforNWPandDAdevelopers,andinactualcomputetimeforexpensiveNWPmodels,isconsiderableWealsoproduceascorecardwithadecisionmatrixtodeterminewhetheranexperimentisawin,loss,orneutralwithrespecttoacontrolrun.Thetoolhasdiagnosticapplicationsaswell.Forexample,wemightfindthatanexperimentimprovesthebackgroundfittoradiancessensitivetomoistureintheboundarylayer,whiledegradingthebackgroundfittoradiancessensitivetostratospherictemperature.Thisveryspecificinformationcaninformtheexperimenter’sunderstandingofwhatwentrightandwrong,andprovideguidanceonhowtoresolveissues.Thesoftwareautomaticallygroupstheplotsandscorecardbyatmosphericvariabletypeandverticallocation,soitdoesnotrequireexpertiseinradianceassimilationtointerpretandusetheresults.12p.11 LetmorePolarOrbitingSatelliteDataavailableinRegionalNWPinCMA—DBNetData,itspotential,applicationandquestionsPresenter:ShuangXi,NationalSatelliteMeteorologicalCenterAuthors:ShuangXiItiswellprovedthatmicrowavethermometersoundingradiancefrompolarorbitingmeteorologicalsatellitehavebeentakenthemostimportantroleinNWP.AtpresentthepolarorbitingsatellitedataisseldomassimilatedinregionalNWPinChinaMeteorologicalAdministration(CMA),becauseoflittledataavailablelocatedinthedomainandintheassimilatingtimewindows,beforethecut-offtime.TherearefourtypesofsourcesofATOVSAMSU-AdataavailableinCMA,includingtwoDBNetdata(RARSandEUMETcast)andtwoglobaldata(NESDISandEUMETSAT).Thereissomedifferenceamongdifferenttypesofsources,suchasthesatellitekindsandregionalcoverages.It’seasytobeunderstoodthatbeforethecut-offtime,thedatacollectedbyregionalsatellitereceivingstationscouldfillsomegapsbetweentheorbitsofglobaldata,withlongerdelaytimeinreceivingcourses.Aquasi-real-timeNWPisrunninginNationalSatelliteMeteorologicalCenter(NSMC),basedonregionalWRFmodelandWRFDAassimilationwithallkindsofAMSU-Adataandconventionaldata.BothDBNetdataandglobaldataareputintothereal-timeregionalassimilationsystem,meanwhilethedataquantities,andcoverageswereanalyzedandcompared,withgivenassimilatingtimewindowsandNWPregion.AnalysisfromJunetoDecember2018showsthattheuseofDBNetdataincreasesthetotaldataquantitiesbymorethan100%andthecoveragesbymorethan50%.

Itisalsopointedoutthethattherearedifferencesinbrighttemperature/latitude/longitude,whichimpliesthedifferencesincalibrationandpositioninginsatellitedatapreprocessing,eitheramongdifferentRARSregionalstationsorbetweenRARSstationsandNESDISway.Thesestudiescouldprovideusefulexamplesandadvicesforapplicationsofmulti-sourcesatellitedatainNWPinCMA.12p.12 EvaluationandassimilationofMWsensorsonNOAA-20andMetop-CPresenter:NielsBormann,ECMWFAuthors:NielsBormann,PeteWestonATMSobservationsfromNOAA-20andAMSU-A/MHSdatafromMetop-CarethemostrecentadditionsofMWinstrumentsassimilatedintheoperationalECMWFsystem.Wereporthereonthedataqualityassessmentandassimilationexperimentsthatmotivatedtheoperationalassimilationofthedata.AnevaluationofNOAA-20ATMSdataagainsttheECMWFbackgroundshowsthatthedataareofoverallgoodqualitywithcharacteristicsthatarecomparabletotheS-NPPATMS,withtheadvantageoflowerinstrumentnoiseandlessstripingintheNOAA-20observations.Assimilationtrialsdemonstratethebenefitsofaddingtheseobservationstotheoperationallyusedobservingsystem.NOAA-20ATMSdatawereactivatedintheECMWFsystemon22May2018.AsimilarevaluationofMetop-CAMSU-AandMHSobservationsalsoshowsacceptabledataquality,withcharacteristicsthataremostlyinlinewithprevioussuchinstruments,withtheexceptionofMHSchannels3and4whichexhibitlargernoisewithsomestripingfeatures.Despitetheselimitations,assimilationtrialssuggestasmallbenefitfromaddingtheseobservations,whichisremarkablesincethisisthe3rdsetofAMSU-A/MHSobservationsinthe9:30orbitandthe9thand11thMWinstrumentwithtemperatureandhumiditysoundingcapabilities,respectively.OperationalassimilationofAMSU-AandMHSfromMetop-Cstartedon14March2019.12p.13 ContinuousDataAssimilationatECMWFandimplicationsforsatelliteobservationtimelinessPresenter:NielsBormann,ECMWFAuthors:PeterLean,MassimoBonavita,EliasHolm,NielsBormannSatellitesprovideanear-continuousstreamofdatathatcanbeassimilatedbyoperationalNWPcentres.However,theassimilationprocessisbothcomputationallyintenseandtimeconsumingand,atECMWF,bythetimetheanalysisiscompletethemostrecentobservationsthatwentintoproducingitarenearlytwohoursold.HerewedescribeanewconfigurationofECMWF's4D-Vardataassimilationsystemwhichallowstheanalysistobenefitfrommorerecentobservations.Byinsertingnewobservationsintothesystemaftertheassimilationprocesshasbegunweareabletousearoundoneandahalfhoursmoreobservationswhilemaintainingthecurrentproductdisseminationschedule.Inthisnew,morecontinuous,configurationtheassimilationwindowisextendeduptothecurrenttimetoallowallobservationsthathavearrived(includingvaluablelowlatencyobservationsattheendofthewindow)tobeassimilated.ResultswillbepresentedtohighlighthowinitiativessuchasDBNet/EARSwhichimprovethetimelinessofsatelliteobservationscanleadtoimprovedcoverageattheendoftheassimilationwindowandultimatelytobetterforecasts.12p.14 CurrentUseofFY-3microwaveinstrumentsandFuturePlansPresenter:BrettCandy,UKMetOfficeAuthors:BrettCandy&FabienCarminatiThemicrowavehumiditysoundersonboardthechinesesatellitesFY-3BandFY-3C,makeanimportantcontributiontoNWPglobalmodelanalysesandhavebeenroutinelyassimilatedattheMetOfficesinceNovember2016.WereportonaninitialassessmentofthemicrowaveinstrumentsonboardFY-3D,usingfirstguessdeparturesfromouroperationalglobalmodel.InadditiontothiswewillshowourlatestimpactstudiesusingtheMWRIonFY-3C.Thisinstrumenthasaknownsignificantascending/descendingbiasandwewilldiscusshoworbitalpredictors,updatedviathevariationalbiascorrectionscheme,havebeenusedtoaccountforthis.FinallyaninitiativetoimprovetheimpactfromMWRIonwindspeedovertheoceanwillbediscussed.

12p.15 AssimilatingFengYun-3CMicrowaveSoundingDataoverLandintheSouthwestVortexPrecipitationinChinaPresenter:KeyiChen,ChengduUniversityofInformation&TechnologyAuthors:KeyiChen,JiaoFan,ZhipengXianTheEuropeanCentreforMedium-RangeWeatherForecasts(ECMWF)havebeenassimilatingtheFY-3CMWHS-II(MicrowaveHumiditySounders-2)dataintheoperationalforecastingsystemsincetheApril4th,2016.Thoughitismoredifficulttoassimilatemicrowaveobservationsoverlandandseaicethanovertheopenoceanduetohigheruncertaintyinlandsurfacetemperature,surfaceemissivityandlesseffectivecloudscreening,Chenet.al.(2018)compareapproachesinwhichtheemissivityisretrieveddynamicallyfromMWHS/FY-3Bchannel1(150GHz(V))withtheuseofanevolvingemissivityatlasfrom89GHzobservationsfromtheMicrowaveHumiditySounders(MHS)onNOAAandEUMETSATsatellites.Theassimilationoftheadditionaldataoverlandwiththedynamicemissivityimprovesthefitofshort-rangeforecaststootherobservations,andtheforecastimpactsaremainlyneutraltoslightlypositiveoverthefirst5days.Itisalsoimportanttostudytheimpactsofassimilatingmicrowaveobservationsontheintenseprecipitatingforecastsoverthecomplexterrain,liketheSichuanBasin,whichstrengthenstheprecipitatingforecastdifficulties.ManycasescausedbytheSouthwestVortexesarestudiedbyassimilatingMWHS-2/FY-3Cwithemissivityatlasandwithdynamicemissivityretrievedfromthewindowchannels.TwotypicalcasesarepresentedheretoshowtheimpactsoftheassimilationontheforecastsandconcludedthatthecyclingexperimentsassimilatingtheMWHS-2/FY-3Cobservationsdoshowimprovementsintheinitialfieldsandtheforecasts,especiallythosewiththeemissivityatlas.Moredatadidbeusedintheexperimentswiththedynamicemissivity,butnotintheobservedprecipitatingarea,plustheretrievedemissivitymighthavelargerbiasesthantheatlasoverthecomplexterrain,whichmightreducetheimprovementsfortheinitialfieldsandtheforecasts,andmoreworkneedstobedonetogivedetailedexplanations.12p.16 UnifiedObservationProcessingPresenter:BenjaminRuston,NavalResearchLaboratoryAuthors:BenjaminRuston,NancyBaker,PatPauley,SarahKingandEricSimonTheamountofdataassimilatedbytheenvironmentalsystemsisgrowingrapidly.Thestrategiesforusingthisdataneedtobere-examinedforreusabilityandportability.ThisallalignsverywellwithmanyinitiativesbeingundertakenacrosslabssuchasJCSDAJEDIInterfaceforObservationDataAccess(IODA)andECMWFODB-Candwillintegratewithtechniquessuchascontinuousdataassimilation.ThisinvolvesmodifyingthecurrentprocessingchainandassimilationstrategyatNRL,andrefactoringwhennecessarytoabstractfunctionalityconceptsfromcomponentsandallowthemtobeusedincommonforthevariousdatatypesused.Observationsfromin-situandsatelliteplatforms,andfromvariousenvironmentalsystemslikeoceanicorionospheric,allcontaincommontraits.Thehandlingoftheingestandthebasicunderstandingofthemeasurementisrequired,butafocusondefiningcommonattributesthedatahaveincommonanddefiningtheseintofamiliesisafocusgoingforward.Whenthesearebroughtintoamorecommonframework,actionsonfamiliesofattributescanbeconstructedtodovariousoperationssuchasdatathinninganderrorassignmentwhichcanthenbedynamicfortheparticularapplication.Environmentalobservations,particularlyintheU.S.areincreasinglyreliantoncommercialprovidersandtheevolvingsmallsatelliteera.Thesedatawillbelargeinvolume,withpoorlydefinedsourcesandfluctuatingqualitycontrolapproaches.Furtherwemayexpectformatchangesascompanies(providers)mayfailorbeacquired.Movingtowardsthisnewstrategywillmakethesystemmoreadaptivetonewdatatypesandformats,moreaccessibletomachinelearningandartificialintelligence(AI)approaches,andsystemsshouldreadilyadapttonewdatatypesandcanmoreeasilybeequippedwithbiasanderrormitigation.12p.17 OperationalUseofNOAA-20ATMSandCrISRadianceDatainJMA'sGlobalNWPSystemPresenter:HidehikoMurata,JapanMeteorologicalAgencyAuthors:HidehikoMurataandNorioKamekawaTheJapanMeteorologicalAgency(JMA)begantoassimilatedatafromtheAdvancedTechnologyMicrowaveSounder(ATMS)andCross-trackInfraredSounder(CrIS)onboardNOAA-20intoitsglobalNumericalWeatherPrediction(NWP)systeminadditiontothoseofSuomiNationalPolar-orbitingPartnership(Suomi-NPP)on5March2019.Thisreportoutlinestheimpactsoftheaddeddataonthesystem.TheATMSinstrumentisamicrowavesounderwith22channels,includingtemperatureandhumiditysoundingchannels.Qualitycontrol(QC)anderrorhandlingfortheassimilationofNOAA-20/ATMSradiancedata,suchas

channelselection,thinningdistance,observationerrors,rain/clouddetectionandbiascorrection(staticscanbiascorrectionandvariationalbiascorrection)followthoseimplementedforSuomi-NPP/ATMSdataassimilation.Currently,tropospherictemperature-soundingchannels(6–9)andhumidity-soundingchannels(18–22)areassimilated.TheCrISinstrumentisahyperspectralinfraredsounderwithatotalof2,211channelsinfullspectralresolution(FSR)mode.QCanderrorhandlingfortheassimilationofNOAA-20/CrISradiancedataalsofollowthoseforSuomi-NPP/CrIS.Currently,27channelsfortemperature-soundingareassimilated.ThechannelsareselectedfromtheCO2absorptionbandinthelong-waveIRband(LWIR)includedinthedisseminated431channeldataset.Observingsystemexperimentscoveringperiodsineachofborealsummer2018andwinter2019wereperformedtoevaluatetheimpactsofNOAA-20instrumentsontheNWPsystem.Thestandarddeviationsofthefirst-guess(FG)departure(i.e.,thedifferencebetweenobservedandcalculatedbrightnesstemperature),whichareusedasanindicatorofdataquality,weresimilartoorsmallerthanthoseofSuomi-NPP.Againstbaselineexperimentsinwhichthefocusingradiancedataofbothsatelliteswerenotassimilated,theimpactsofSuomi-NPPandNOAA-20onFGandforecast-fieldweresimilar.AtestassimilationexperimentwiththeadditionofNOAA-20/ATMSandCrISdatatogetherwasperformed.Experimentsforindividualinstrumentswerealsoperformedtodeterminetheirspecificcontributions.DecreasesinthestandarddeviationoftheFGdepartureoftheAMSU-AandMHSmicrowavesounders,whichimplytheimprovementsoftemperatureandwatervaporofFGfield,wereobserved.Theseimprovementswiththetemperaturesoundingchannels(AMSU-A/ch4-8)andhumiditysoundingchannels(MHS)aremainlyattributabletotheassimilationofNOAA-20/ATMSdata,andthosewiththestratospherictemperaturesoundingchannels(AMSU-A/ch9-14)areattributabletotheassimilationofNOAA-20/CrISdata.Improvementsingeopotentialheightforecasts,especiallyforthemid-latitudes,wereobservedinthetestexperimentinwhichNOAA-20/ATMSandCrISdatawereassimilated.Session13:AdvancesinSatelliteDataAssimilation(oralpresentations)13.01 Implementationofslant-pathradiativetransferinEnvironmentCanada’sGlobalDeterministicWeatherPredictionsystemPresenter:MaziarBaniShahabadi,EnvironmentandClimateChangeCanadaAuthors:MaziarBaniShahabadi,MarkBuehner,JosepAparicio,andLouisGarandIntheprocessofradiancedataassimilation,verticalprofilesofthetrialfieldshavebeensofarhorizontallyinterpolatedtothelocationoftheradianceobservationsprojectedatthesurface.Inarecentstudy(BaniShahabadietal.,Mon.Wea.Rev,2018),horizontalgradientsofatmosphericvariableswereusedofflinetoapproximatetheslantlineofsightandthusimprovetheforwardoperator,especiallyforhighpeakingchannels.Inthisapproach,analysisincrementswerestillcomputedassumingverticalcolumns.Positiveimpactswereshownforforecastsuptofourdays.Inarecentdevelopment,theslantpathinterpolationisdoneinline,i.e.withintheassimilationprocedure,andisappliedforallradiances.Boththetrialfieldsandanalysisincrementsarehorizontallyinterpolateddirectlyontotheslantprofile.Theprocedureiscomputationallyaffordableasthesizeofcontrolvectordoesnotchange,anditcanbeusedforanyobservationtype,includingradianceandradarobservations.Thisgeneralizedprocedureispresentedalongwithresultsdemonstratingtheimpactonanalysesandforecasts.13.02 Impactofthemid-loopforsatelliteradianceonahybriddataassimilationskillPresenter:Hyo-JongSong,KoreaInstituteofAtmosphericPredictionSystemsAuthors:Hyo-JongSong,Ji-HyunHa,andHyoung-WookChunSince1979socalledthesatellitedataassimilationera,thequalityofnumericalweatherprediction(NWP)isintheprocessofcontinuouslyimprovinginnovatively.Especially,thedataassimilation(DA)skillinthesouthernhemispherehasbeenapproachingtheskillscoreinthenorthernhemispherenearlyinthisperiod.IntheDAprocedure,apropersimulationofradiancedatausingefficientradiativetransferoperatorsuchasRadiativeTransferforTIROSOperationalVerticalSounder(RTTOV)isessentialtomaketheeffectivenessofthesatelliteradiancesoundingtothemaximum.Forthis,re-calculationofRTTOVisconductedwithanimprovedguessfortheradiancedatainthemiddleofiterationforthecostfunctionminimization,whichiscalled‘mid-loop.’

KoreaInstituteofAtmosphericPredictionSystems(KIAPS)hasbeendevelopinganoperationalNWPmodel,KoreanIntegratedModel(KIM),whichincludesitsownDAsystem.Itisashapeofhybridvariational-ensembleDA,hybrid4DEnVar.Thisstudywillbeatrialforinvestigatingaroleofthere-calculationofRTTOV,anobservationforwardoperatorforsatelliteradiancedata,inthehybrid4DEnVar.Anotherissuethatneedstobeconsideredinthisstudyistheroleofre-checkingthequalityofthebrightnesstemperatureduringthecost-functionminimization.Thechangeintheavailabilityofusingthesatellitesoundingdataeverymid-loopisexpectedtoyieldasignificantmodificationtotheresultantanalysisincrement.Inthesituationofasemi-operationalconfigurationinvolvingtheKIMforecastsystem,itisinvestigatedthewayandthemagnitudehowtheapplicationofmid-loopthatincludesthere-calculationofRTTOVandre-checkingthesatellitedataaffectsthehybridDAskills.13.03 ObservationselectionforvariationalbiascorrectionPresenter:RuthTaylor,UKMetOfficeAuthors:R.B.E.TaylorVariationalbiascorrection(VarBC)hasbeenusedforallsatelliteradiancesassimilatedbytheMetOfficeglobalNWPsystemsinceMarch2016,andbytheUKregionalNWPsystemsinceJune2017.TheintroductionofVarBCledtosignificantimprovementsinglobalforecastscoresandthesystemhasprovedrobustandstable[CameronandBell,2018].TheoriginalimplementationofVarBCwassuchthateveryobservationassimilatedalsoinfluencesthebiascorrection.Herewedescribeanapproachforselectingtheradianceswhichdeterminethebiascorrection,accordingtothecircumstancesoftheobservation.Thiscapabilitybecomesparticularlydesirableasweseektoextendouruseofobservationstomorecomplexconditions(forexample,byintroducingradiancesaffectedbycloudorawidervarietyofunderlyingsurfaces).Thebiasesofsuchobservationswithrespecttomodelledequivalentsarelesswellunderstoodandtheymayhaveanadverseeffectonthederivedbiascorrection,possiblytotheextentthatpositiveimpactsfromtheirintroductionarenegated.Theresultsofsomeinitialexperimentsusingobservationselectionforbiascorrectioninthevariationalframeworkwillbepresented.Cameron,J.andBell,W.(2018).Thetestingandimplementationofvariationalbiascorrection(VarBC)intheMetOfficeglobalNWPsystem.MetOfficeForecastingResearchTechnicalReport631.13.04 SurfaceDependentCorrelatedInfraredObservationErrorsintheFV3FrameworkPresenter:KristenBathmann,IMSG@NOAA/NCEP/EMCAuthors:KristenBathmann,AndrewCollardResearchwithcorrelatedsatelliteobservationerrorhasbeenongoingatNCEPandhasprimarilyfocusedonestimatingandaccountingforinter-channelerrorcorrelationsinAIRSandIASIobservationsoverseasurfacesonly.IntheGlobalForecastSystem(GFS),accountingforerrorcorrelationsinIASIobservationshadaslightlypositiveforecastimpact,whereasresultswithAIRShaveproventobeneutral.ThispresentationwilldiscusstheassimilationofinfraredsatelliteobservationswithcorrelatederrorintheFiniteVolumeCubed-Sphere(FV3)dynamicalcore.Errorcorrelationsoverseasurfacesandlandsurfacesarecomputedandtreatedseparately.Inaddition,theseexperimentsadoptstricterqualitycontrolthatdependsonthesmaller,diagnosedobservationerrors,resultinginimproveddetectionofcloudcontaminateddata.Theforecastimpactduringatwomonthperiodinwinter2018-2019isexamined,andcomparedtopreviousexperimentswiththeGFS.13.05 Understandingthelinkbetweensatelliteradiancethinningandobservationerrorvarianceinflationinglobal4D-EnVarPresenter:JoëlBédard,EnvironmentandClimateChangeCanadaAuthors:JoëlBédard,AlainBeaulne,MarkBuehner,andPatriceBeaudoinThemodelanddataassimilationcomponentsforanewglobalnumericalweatherprediction(NWP)systemwith15kmgridspacingarecurrentlybeingdevelopedandtested.Thedataassimilationcomponentisbasedon4D-EnVarandusesbackgrounderrorcovariancespartiallyobtainedfromaglobalensembleKalmanfilterwith256membersat39kmgridspacing.InEnvironmentandClimateChangeCanada'scurrentlyoperationaldataassimilationsystems,allassimilatedradianceobservationsarethinnedtoa150kmhorizontalspacing.Thisisjustifiedbythefactthaterrorsassociatedwithdensesatelliteobservationscanhavesignificantspatialcorrelationsandassimilationalgorithmsgenerallyassumespatiallyuncorrelatedobservationerrors.However,therecentincreaseinhorizontalresolutionoftheanalysisgridshouldallowfortheassimilationofdenserobservationnetworks.Withinthiscontext,variousstrategiesarebeingexploredtoincreasethedensityof

assimilatedradianceobservationsforinitializingglobalforecasts.Resultsfromidealizedone-dimensionalexperimentsshowthatanalysescanbeimprovedbyincreasingthedensityofobservationswithspatiallycorrelatederrors,whilestillassuminguncorrelatederrorswithinthedataassimilationalgorithm.However,resultsalsoshowthatwhenincreasingthedensityofsuchobservations,theobservationerrorvariancemustbeinflatedtoavoidover-fittingthelargescales,whichisoftenoverlookedintheliterature.ThelinkbetweenobservationerrorvarianceinflationandspatialthinningisrevisitedinthecontextofthisnewhigherresolutionglobalNWPsystem.Resultswillbediscussedfromexaminingthespectralvariancesof1)thehybridbackgrounderrorcovariancesand2)theimpactofsatellitebrightnesstemperatureobservationsontheanalysesforarangeofvaluesforthespatialthinninganderrorvarianceinflationappliedtotheseobservations.Resultsfromfullycyclingassimilationandforecastingexperimentsinanearoperationalcontextwillalsobepresented.13.06 SurfaceskintemperatureforsatellitedataassimilationatECMWFPresenter:CristinaLupu,ECMWFAuthors:CristinaLupu,AntonyMcNallyAnaccuratespecificationofthesurfacetemperatureisimportanttotheassimilationofradiancesprovidinginformationontemperatureandhumidityinthelowertroposphere.ThesurfaceskintemperatureproducedbyNWPmodelscanhavelargeuncertaintiesandbiasesandthereisnoindependentinformationforwhatabsolutesizeoferrorshouldbeassignedtothemodel'sestimateofthesurfaceskintemperatureseenbysatellites.Itisrecognizedthatfurtherprogresswiththeassimilationofsurface-sensitivechannelsintheECMWFsystemwillrequireconsiderablerevisionofthemethodsusedtospecifytheskintemperature.Thispresentationhighlightsworktowardsimprovingthehandlingofskintemperatureforsatellitedataassimilation.Giventhatinthecurrenthybrid4D-Var,anEnsembleofDataAssimilations(EDA)isusedtogeneratesituation-dependentbackgrounderrorsforthehigh-resolutiondeterministicforecastitisproposedheretoreplacetheconstantvaluesofthebackgrounderrorsstandarddeviationforsurfaceskintemperaturewithestimatesfromtheEDA.Thenewsystemintroducesthespatialandtemporalvariabilitytotheassumedskintemperatureerrors,withtheoverallscalingofthevarianceoptimisedonthebasisoftrialanderror.Resultsillustratingtheimpactonanalyses(e.g.,improvedfirstguessfittoradiosondetemperaturedata)andforecastswillbepresentedalongwithalookaheadtoplanneddevelopmentsforevolutiontowardscoupledmodelsandcoupleddataassimilation.Session13:AdvancesinSatelliteDataAssimilation(posterintroductions)13p.01 LocalUnscentedTransformKalmanFilterforHighlyNonlinearSystemPresenter:KwangjaeSung,KoreaInstituteofAtmosphericPredictionSystemsAuthors:KwangjaeSungTheLUTKFalgorithmcanestimatethestateofhigh-dimensionaldynamicsystemswithasmallnumberofensemblemembersbycombiningboththeunscentedtransformation(UT)byJulierandUhlmann(2004)andthelocalizationmethodusedintheLETKF.UnliketheLETKF,whichdeterminestheensemblemembersbyrandomsamplingattheinitialtime,thesamples(ensemblemembers)intheLUTKFalgorithmareselecteddeterministicallyusingtheUTmethodineverytimestep.Hence,theLUTKFalgorithmcanestimatethestateofasystemusingasmallerensemblesizecomparedtotheLETKF.IntheLUTKFalgorithm,aminimalsetof2nsamplesforann-dimensionalsystemisusedtopropagatethestateestimateanditserrorcovariance(uncertainty)throughnonlineartransformations.WhiletheLETKFhasanestimationaccuracyuptoafirstorderterminaTaylorseriesofthetruevalueduetolinearizationforanonlinearsystem,theLUTKFcanguaranteeaccuracyuptoathirdordertermintheTaylorexpansionbyusingtheUT.13p.02 ImpactofSSMISBCmethodconsideringbackground-errorinKIAPSDAsystemPresenter:Jeon-HoKang,KoreaInstituteofAtmosphericPredictionSystems(KIAPS)Authors:Jeon-HoKang,Hyo-JongSong,Hyoung-WookChun,andIn-HyukKwonTheSSMISloweratmosphericsounding(LAS)radianceshadbeenassimilatedontheHybrid4DEnsemble-Variational(Hybrid-4DEnVar)dataassimilationsysteminKIAPS.Weknowthattherearesomebiasesinthebackgroundfieldaswellbutitisnoteasytoseparatethemwithinthefirst-guessdeparture(O-B)whichisusedtofigureoutthebiasesoftheobservation.FortheaccuratebiascorrectionoftheSSMISdatatomaximizetheassimilationimpactintheforecastanewmethodconsideringthebackgrounderrorfortheorbitanglebased

biascorrectionwasimplemented.Andassimilatingchannelsarere-selectedornewlyaddedinDAsystem.ItwouldbepresentedanewstrategydesignedtoreducetheeffectsofbackgroundbiasesexpectedtoattenuatetheaccuracyofthebiascorrectionwiththeirimpactsontheKIAPSDAcycleexperiment.13p.03 DevelopmentandProgressofHighResolutionCMALandSurfaceDataAssimilationSystemPresenter:ShuaiHan,NationalMeteorologicalInformationCenter(NMIC)Authors:ShuaiHAN,ChunxiangSHI,BinXU,LipengJIANG,ShuaiSUN,TaoZHANGThispaperreviewsthedevelopmentofHRCLDAS,ahigh-resolutionlanddataassimilationsystem,focusesontheimportantprogressandbreakthroughsinHRCLDASresearchanddevelopment,andsummarizesthecontributionofthesedevelopmentstoHRCLDASoperation.Andthepapermainlyintroducesthe1kmresolutionmeteorologicaldatabyusingmulti-gridvariationanalysistechnique.Basedonthedataof1kmvisiblechannel,high-resolutionterrainandsurfacealbedoofFY-2satellite,thegroundRadiationproductqualityandspatialresolutionhasbeenimproved.Simulationofgroundsolarradiationusinghybridmodelandgroundstationsunshinehours,airtemperatureandsoon,andtheuseofmultiplegridvariationanalysistechnologytoachievetheintegrationoftheseinformation;toachievetheEastAsiasatelliteintegratedprecipitationproducts(EMSIP)and4millionautomaticstationsobservation.Aimingatthecharacteristicsofhighterrestrialsimulationresolutionandlargedatavolume,aparallelcalculationschemeofblockparallelandmodeisdesignedtorealizeefficientsoilmoisturesimulation.HRCLDASpromotesthemeteorologicaldepartmentsatalllevelstocarryoutrelatedoperationapplications.13p.04 EvaluationofVariationalBiascorrectionusinganiterativebiascorrectionagainstanalysisPresenter:In-HyukKwon,KoreaInstituteofAtmosphericPredictionSystems(KIAPS)Authors:In-HyukKwon,Hyoung-WookChun,YuJinJuhn,Ji-HyunHa,Hyo-JongSongandHanbyeolJeongThebiascorrection(BC)isoneofthemostimportantprocessesinthesatellitedataassimilation(DA).TheKoreaInstituteofAtmosphericPredictionsystems(KIAPS)hasdevelopedanoperationalDAsystemforthecubedspheregridglobalmodelcalledKoreaIntegratedModel(KIM).ItassimilatesmostsatelliteradianceobservationsusedintheKoreaMeteorologicalAdministration(KMA)DAsystem.TheKIAPShasbuilttheirownobservationprocessingsystemcalledKIAPSPackageofObservationProcessing(KPOP)toprovidequalifiedreal-timeobservationsfortheDAsystem.Toremoveobservationbias,theKPOPperformsanadaptiveBCmethodthatcalculatestheBCcoefficientsagainstthebackgroundattheanalysistimeratherthanusingstaticBCcoefficients.Thismethodcanmakethedifferencebetweenobservationandbackground(O-B)almostzero.However,theadaptiveBCmethodislikelytopushobservationstothebackgroundtoomuch.AsystematicmodelbiaswasfoundintheKIM,whichiswarmanddrybiasaround850hPa.Inthiscase,weshouldavoidsendingtheobservationtooclosetothebackground.Otherwise,itreinforcesthemodelbias.RecentlytheVariationalBC(VarBC)methodisimplementedintheKIAPSDAsystem,sothebiasparametersareupdatedjointlyandsimultaneouslywiththecontrolvariablesduringtheminimizationinVariationalDAsystem.TheVarBCislessaffectedbymodelbias.ThisisbecausetheVarBCtriestofittheobservationintotheanalysisratherthanthebackground.Therefore,itisexpectedthattheVarBCpreventsover-fittingthecorrectedobservationwithabackground.TobetterunderstandtheVarBCmethodintheKIAPSdataassimilationsystem,wehavedevelopedaniterativebiascorrectionagainstanalysis.Thismethodminimizesthedifferencebetweenobservationandanalysis(O-A)whichisthesameconceptasVarBC.ItperformstheadaptiveBCandDAiteratively.TheDAusesthesamebackground,buttheadaptiveBCusesupdatedanalysisinsteadofthebackground.Thismethodisrelativelystraightforward.ThisstudyinvestigatesO-Aforeachiteration.Theiterativebiascorrectionrequiresalotofcomputationtime,soitishardtouseinanoperationalsystem.However,itwillbeausefultoolforevaluatingVarBC.13p.05 LeveragingModernAItechniquesinNWPandEnhancingSatelliteDataExploitationPresenter:SidBoukabara,NOAAAuthors:S.A.Boukabara,E.Maddy,N.ShahroudiandR.HoffmanArtificialIntelligence(AI),machine/deeplearningtechniques(includingdeepneuralnetworks,DNNs)haveadvancedconsiderablyinrecentyearsacrossanumberofareasandapplications:inmedicine,self-drivingcars,socialmedia,thefinanceindustry,etc.TheastonishingincreaseinaccuracyandapplicabilityofAIhasbeensignificantintheprivatesector,drivenbytheease,efficiency,cost-effectiveness,speedandauto-learningfeaturesofAI.SignificantadvanceshavealsobeenmadeinapplicationofAIindifferentareasof

meteorologyandoceanography.However,untilrecently,farfewerAIapplicationsweredevelopedintheareaofenvironmentaldataexploitationofsatellitedata,high-levelinformationextractionintheareaofNumericalWeatherPrediction(NWP),dataassimilationandforecasting,aswellasforextremeweatherpredictionandnowcasting.TherehavebeenencouragingsignsthatAIisincreasinglyconsideredfortheseapplications,withpromisingresults–includingpredictiveskills–andthistrendisexpectedtocontinuewiththeever-increasingvolumeofsatellitedataandtheincreasedsocietalrelianceonimprovedforecastingaccuracyandresolutions.Theincreaseofdatavolumecomesfromhigherresolutionsatellitesandsensors,fromagrowinglistofnewsensors(traditionalaswellassmallsats/cubesats),andfromanexplosionofnewvirtualobservingsystemsmadepossiblebytheinternetofthings(IoT).Exploitingallthesedatasourcesisexpectedtopresentmajorchallenges,andAIhasemergedasapotentiallytransformationalandmitigatingtechnology,especiallybecauseofthepotentialofwhatmightbecalledmeta-TransferLearning–thetransferofknowledgeandexpertisefromfieldinwhichAIhasbeenfirmlyestablishedtoNWPandrelatedenvironmentalsciences.ThisstudywillpresentrecentresultsobtainedinusingAIforsatellitedatacalibration,simulationthroughradiativetransfer,inversion,dataassimilationandfusion,aswellasforpost-forecastcorrection,includingforextremeweatherevents.13p.06 TheassimilationoftheIASIfullspectrumusingreconstructedradiancesPresenter:MarcoMatricardi,ECMWFAuthors:MarcoMatricardiWorkhascontinuedatECMWFontheassimilationofPrincipalComponent(PC)basedIASIdatashiftingthefocusfromthedirectassimilationofPCstotheassimilationofradiancesreconstructedfromtruncatedprincipalcomponents(hereafterRR).AlthoughatatheoreticalleveltheassimilationofPCsorRRscanbeconsideredequivalent,thelatterhavetheadvantageofbeingabletoexploitexistingscienceandinfrastructuredevelopedforrawradianceassimilation,inparticularcloudandaerosolscreening.Tothisend,theECMWF4D-Vardataassimilationsystemhasbeenmodifiedtoallowtheassimilationofaselectednumberof400IASIreconstructedradianceswhicheffectivelyencapsulatetheinformationcontentofthefullnumberofIASIchannelsinthelong-waveband-1(2221temperature,surface,andozonesoundingchannels)andthemid-waveband-2(3201watervapoursoundingchannels).ThelatestversionoftheRRsystemmakesuseofnewhumiditybackgrounderrors,amodifiedclouddetectionschemeandafullyrevisedobservationerrorcovariancematrixfortheIASIreconstructedradianceswheredifferentweightshavebeenappliedtoobservationsindifferentIASIspectralregions.TenmonthsofassimilationtrialsshowthatbackgroundforecastsproducedbytheRRsystemfitradiosondeandsatelliteobservationsbetterthanbackgroundforecastsproducedbytheoperationalsystem,especiallyforwatervapour.Regardingthequalityofthemediumrangeforecastslaunchedfromtheanalysesproducedbytheassimilationofreconstructedandrawradiances,theRRsystemdemonstratesanimpressiveperformanceadvantageovertheoperationalsystem,especiallyintheSouthernHemisphere,where500hPageopotentialscoresareimprovedupto2.5%inthemedium-range.13p.07 QuantifyingtheSensitivityofNCEP’sGDAS/GFStoCrISDetectorDifferencesPresenter:AgnesLim,UW-MadisonCIMSS/SSECAuthors:AgnesLim,SharonNebuda,JamesJung,DaveTobinandMitchGoldbergDesignofInfrared(IR)soundinginstrumentshasadvancedtouselargearraysofdetectorstomakesimultaneousobservations.Differentbias,noiseandcorrelationpropertiesbetweendetectorscanresultintheneedtotreatobservationsfromeachdetectordifferently.HavingtotreateachdetectorasanindependentinstrumentisnotdesirableforNumericalWeatherPrediction(NWP)centers.Currentradiometricuncertaintyandnoise-equivalentchangesinradiance(NEdN)requirementsforIRsounderinstrumentsdonotfullyaddressorconstrainthenoisecharacteristicsbetweendetectors.Detectorproperties,suchasquadraticnonlinearityanddetectornoise,willcontributetointer-detectorbias.NosystematicstudieshavebeenconductedtodeterminethedegreeofmatchneededbetweendetectorsinanarrayusedforIRsoundinginstrumentsthatsupportNWP.Thegoalofthisworkistounderstandwhatleveltheseinter-detectorbiasesbegintoaffectNWPanalysisandforecastsystemsandhelpdefinetheinter-detectordesignrequirements.Instrumentproviderswillneedthisinformationtoassurethatallinstrumentfield-of-views(FOVs)arematchedwellenoughtosupportNWPradianceassimilation.Asingledetectorisperturbedwithseveraldifferentdetectorquadraticnonlinearityparametercharacterizationstodeterminetheireffectonanalyses.ClearskyCrISspectraradiancesaresimulatedfromtheNASAGEOS-5analysesassuminganaquaplanet.Instrumentnoiseisthenaddedtotheseradiances.Wewill

focusononeCrISsurfacechannel.Acontrolrunwillbegenerated.Itassimilatesconventionaldata,microwavesatelliteradiancesandsimulatedCrISobservationswithconstantinstrumentnoiseonallFOVs.TheFOV5detector(centerofa3x3arrayofdetectors)NEdNwillbeused.ExperimentswillbeconductedwiththesameconfigurationasthecontrolbutwillassimilateperturbedCrISobservationsatFOV7.ThemagnitudeoftheperturbationswillbemultiplesoftheFOV5NEdN.StatisticssuchasFOVselected,O-BbiasandRMS,willbedeterminedwithrespecttoeachdetectorandpresented.13p.08 AlanddataassimilationstudybasedonLISwithFY3ClandsurfacetemperatureandmicrowavebrightnesstemperaturePresenter:ChunxiangShi,NationalMeteorologicalInformationCenter(NMIC),CMAAuthors:ShiChunxiang,JiaBinghao,SunShuai,ZhangShuai,LiangXiao,JiangLipengDuetothespatiotemporallimitationsofgroundobservationsandthelargeuncertaintiesassociatedwithatmosphericforcingandlandsurfaceparameterizations,landdataassimilationhasbecomeaneffectivewayofsynthesizingcomplementaryinformationfrommeasurementsandlandsurfacemodels(LSMs)intoasuperiorestimateofgeophysicalfieldsofinterest(e.g.,soilmoisture).Asanew-generationChinesepolarorbitingmeteorologicalsatellite,theFY-3seriesconsistsoftwoexperimental(FY-3A/B)andatleastfouroperationalsatellites(FY-3C/D/E/F).However,fewstudiesinvestigatedthesimultaneousassimilationofmicrowavebrightnesstemperature(Tb)andlandsurfacetemperature(LST)retrievalsfromFY-3C.ThisstudycarriedoutthefirstexperimenttosimultaneouslyassimilatethemicrowaveTbandLSTretrievalderivedfromFY-3Cintoalandsurfacemodel,theNoah-MPmodel.AndthisstudyusingCLDAS(CMALandDataAssimilationSystem)forcingdata.Assimilatingexperimentsdemonstratethereasonabilityoftheassimilationschemedevelopedinthisstudy.ItalsosuggeststhatthesimultaneousassimilationofFY3CLSTandTbhasthepotentialtoestimatesoilmoisturewithhigheraccuracythantheindividualTbobservations.Foundation:ThisstudywasjointlysupportedbytheNationalNaturalScienceFoundationofChina(91437220),NationalDepartment(meteorology)PublicBenefitResearchFoundation(GYHY201306045;GYHY201206008;GYHY201306022),andInternationalScientificandTechnologicalCooperationProject(2011DFG23150).Session14:PreparationforNewHyperspectralInstruments14.01 MTG-IRS:scientificimprovementsforauser-friendlymissionPresenter:DorotheeCoppens,EUMETSATAuthors:DorotheeCoppens,BertrandTheodore,ThomasAugust,TimHultberg,CedricGoukenleuque,JochenGrandellTheMeteosatThirdGeneration(MTG)seriesoffutureEUMETSATgeostationarysatellitesconsistsoftwotypesofsatellites,theimaging(MTG-I)andthesounding(MTG-S)satellites.TheInfraredSounder(IRS)isoneofthetwoinstrumentshostedonboardMTG-S.ItisanimagingFouriertransformspectrometerwithaspectralresolutionbetterthan0.754cm-1intwospectralbands,theLong-WaveInfraRed(LWIR,700–1210cm-1)andtheMid-WaveInfraRed(MWIR1600–2175cm-1).Afteritslaunchin2023,itwillperformmeasurementsoverthefullEarthdiskwithparticularfocusonEurope(thatwillberevisitedevery30minutes),withaspatialresolutionof4kmatnadir.Withsuchmeasurementscharacteristics,theIRSmissionisexpectedtomassivelyimpactbothnumericalweatherpredictionandnowcastingapplications.InordertoenableuserstomakethemostofIRSproducts,EUMETSATstrivestoadaptthemtoallapplications.Thisincludes:

• Timeliness:IRSLevel2processinghasbeenfullyreconditioned,buildingontheIASIexperiencetoprovideatmosphericprofilesontheentirediskwithin30minutesafteracquisition.IRSLevel1processinghasalsobeenoptimizedsothatcalibratedspectraareavailableinlessthan15minutes;

• Compression:duetothehugeamountofdata,theIRSspectrawillbedisseminatedasprincipalcomponentscores.Ahybridapproach,inwhichglobalPCscoresaresupplementedbyasmallnumberoflocalPCscores,hasbeendesignedtocapturepossiblenewspectralcharacteristicsnotincludedinglobalPCs;

• Informationcontent:theradianceswillbeuniformisedforeaseofuseandspectralsamplingoptimizedtoabout0.6cm-1tokeeptheinstrumentsamplingandavoidintroducingharmfulartefacts

relatedtoresampling.Inaddition,anevaluationofthecloudfractionandtheheterogeneityofthescenewithineachIRSpixelwillbeavailableincontrasttowhathadbeenplannedinthepast;

• Userawareness:UsersareassociatedtoalldecisionsregardingtheIRSprocessingthroughthemissionadvisorygroup.TestdataarebeingpreparedtotrainfutureusersoftheIRS.

ThispaperwillgothroughthevariousimprovementsbroughttotheIRSlevel1andlevel2processing,thecurrentresults,limitationsandthefutureplan.14.02 IASI-NGProgram:GeneralStatusOverviewPresenter:FranciscoBermudo,CNESAuthors:F.Bermudo,E.Jurado,F.Bernard,C.Lefèvre,A.Deschamps,SGuibertCNESwilldeveloptheInfraredAtmosphericSoundingInterferometerNewGeneration(IASI-NG),akeypayloadelementofthesecondgenerationofEuropeanmeteorologicalpolar-orbitsatellites(METOP-SG),dedicatedtooperationalmeteorology,atmosphericcomposition,andclimatemonitoring.IASI-NGwillcontinueandimprovetheIASImissioninthenextdecades(2020-2040)withnotableimprovementsonperformances.TheperformanceobjectiveismainlyaspectralresolutionandaradiometricerrordividedbytwocomparedwiththeIASIfirstgenerationones.FortheIASI-NGprogram,acooperationagreementisimplementedbetweenCNESandEUMETSAT.Underthisagreement,CNEShasoversightresponsibilityforthedevelopmentandprocurementoftheinstruments,thedefinitionofinstrumentinflightoperations,theLevel1Cdataprocessingsoftware(L1CPOP)andtheIASINGTechnicalExpertiseCentre(IASTEC)inchargeofthein-flightcalibration,validationandcontinuousperformancemonitoring.EUMETSATisinchargeofdevelopingtheEPSSGSystemandoperating,archivinganddistributingIASI-NGdatatotheusers.ThepaperreportsonlateststatusofIASINGprogram,withspecificfocusonthesenewhighlights.TheinstrumentmeasurementtechniqueisbasedonwidefieldFourierTransformSpectrometerbasedonaninnovativeMertzcompensatedinterferometertomanagetheso-calledself-apodisationeffectandtheassociatedspectralresolutiondegradation.Further2yearsofinstrumentdefinitionconsolidationandengineeringmodelsactivities,IASI-NGprogramwillreachanimportantmilestoneinfall2019withthecompletionoftheInstrumentCriticalDefinitionReview(CDR)withAirbusD&S.Thedesignoftheinstrument,thedevelopmentstatusofthemainunits,criticaltechnologiesandsub-systemswiththefirsttestresultsperformedonengineeringmodelswillbepresented.ThestatusofthemostrecentCNESactivitieswillbealsoprovided:CNESachievedthedefinitionoftheLevel1CdataProcessingalgorithmsandstartedthedevelopmentoftheL1COperationalprocessorwithPDRmilestonesuccessfullyreachedendof2018.TheIASI-NGSystemCriticalDefinitionreviewfocusedonmissionperformances,validationactivitiesandIASTECdefinitionisplannedinQ12020toconcludetheongoingdefinitionactivities.14.03 IASI-NGL1processing:howtoestimatetheinstrumentresponsefunctioninreal-time?Presenter:AdrienDeschamps,CNESAuthors:A.Deschamps,C.Luitot,F.Bernard,E.Baldit,A.PenquerThedevelopmentoftheIASI-NGSystem,underresponsibilityofCNES,includesthedevelopmentanddeliveryofIASI-NGinstrumentsandLevel0Processor(ICPU)tobeflownontheMetop-SGASatellites,thedevelopmentoftheLevel1Processor(L1POP)aspartoftheEPS-SGgroundsegment,andthedevelopmentofaTechnicalExpertiseCentre(IASTEC)inchargeofthein-flightcalibration,validationandcontinuousperformancemonitoring.TheIASI-NGinstrumentpresentsatechnologicalgapcomparedtotheIASIFouriertransformspectrometer.Inordertobeabletodeliverdatawithbothatwicelowerradiometricalnoiseandatwicebetterspectralresolution,theIASI-NGinterferometerdesignisbasedontheMertzprincipleandusesmovableprismstocompensatetheso-calledself-apodizationeffects.Inordertodeliverspectrallyconsistentdata,theinstrumentalspectralresponsefunction(ISRF)ofthespectrometeriscontinuouslyestimatedon-groundandremovedbythelevel1processing.ThispaperpresentsthealgorithmsthathavebeendevelopedtoestimatethisISRFtakingbenefitfrombothaninstrumentalmodelandobservableparameterscomingfrommetrologybeams,aFabry-Perotinterferometer

orabsorptionfeaturesintheatmosphericspectra.Wewilldescribethetwomainpartsofthisalgorithmicchaindedicatedononeparttotheestimationofthespectralshiftandontheotherparttheestimationoftheinstrumentalshapeerror.Thecorrectionofthesetwoeffectsarecorrectedsimultaneouslyintheon-groundprocessingbylocaldeconvolution.TheestimatedISRFisthenremovedandreplacedbyaperfectGaussianfunction.Thisalgorithmisappliedtoeachinterferogramandforeachwavenumberbecauseofthehighchromaticeffect(i.e.thevariationofthespectralshiftwiththewavenumber)duetotheinstrumentalconcept.ThefirstvalidationstudiesontheISRFestimationmodulethathavebeenconductedbytheIASI-NGL1teamwillbeshown.Accordingtotheseresults,thechallengingmissionrequirementtohavearesidualspectralshiftlowerthan10-6withinanysingleorbitshouldbemet.AnoverviewofthestatusfortheothermainIASI-NGperformanceswillbefinallygiven.14.04 DevelopmentandVerificationchallengesoftheIASI-NGsystemPresenter:EricJurado,CNESAuthors:EricJurado,ClémenceLeFevreIASI-NGsystem,developedunderCNESresponsibility,aimsatproducingdataformeteorologicalandatmosphericchemistryuser’scommunity.ItispartoftheEUMETSATEPS-SGsystem.Itiscomposedofaspacesegmentandagroundsegment.ThespacecomponentistheIASI-NGinstrument,onemodelflyingoneachofthethreeMETOP-SGAthatwillbelaunchedbetween2022and2036.Thegroundsegment,consistsintheIASI-NGLevel1Processor,theso-calledL1CPOP,whichisintegratedinEPS-SGPayloadandDataAcquisitionProcessinggroundsegment,andintheIASI-NGTechnicalExpertiseCenter(IASTEC),inCNESpremises,dedicatedtoperformancemonitoringofbothspaceandgroundsegments,anomalyinvestigation,anddevelopmentofimprovedprocessingsoftware.IntheframeofthecooperationagreementwithEUMETSATforIASI-NG,CNESisresponsibleforensuringthefunctionalityandperformancesofboththeIASI-NGinstrumentflyingaboardMETOP-SGandtheL1CproductsdisseminatedthroughEUMETSATsystem.TheIASI-NGsystemverificationapproachhastodealwiththreemainchallenges:itscomplexandinnovativeinstrument,theambitiousscientificperformancesrequired,andtheintegrationwithintheEPS-SGsystem.Thevalidationofperformancesisreachedbyanalysisortestsconsideringmodelswithanincreasinglevelofcomplexityandrepresentativeness,firstatinstrumentlevel,thenwiththeinstrumentintegratedon-boardthesatellite,andfinallyininterfacewiththeEPS-SGgroundsegment.Severaltestscampaignsarerequiredtocoveralltechnicaltopicssuchasoptical,mechanical,thermal,electrical,functionalandoperationalqualification.Animportantissueisalsothevalidationofthescientificalgorithmsimplementedon-groundwithintheL1CPOP.InthisperspectivetheInfra-RedInterferometerSimulator(IRIS)isdevelopedtobothsimulatetheinstrumentbehaviorandoptimizethescientificpost-processing.Thispresentationwilladdressboththelogicandthetechnicalchallengesofthedevelopmentandverificationoftheperformancesofthiscomplexsystem.14.05 EvaluationofafirstIASI-NGchannelselectionforNumericalWeatherPredictionPresenter:FrancescaVittorioso,CNRM,Météo-France,CNRSAuthors:FrancescaVittoriosoIntheframeworkoftheEUMETSATPolarSystem-SecondGeneration(EPS-SG)preparation,anewgenerationoftheInfraredAtmosphericSoundingInterferometer(IASI)instrumenthasbeendesigned.TheIASI-NewGeneration(IASI-NG)willmeasureatadoubledspectralresolutioncomparedtoitspredecessorandwithasignal-to-noiseratioreducedbyafactor2.Measurementprecisionwillbeimprovedaswell.ThehighamountofdataarisingfromIASI-NGwillpresentmanychallengesintheareasofdatatransmission,storageandassimilationandthenumberofindividualpiecesofinformationwillbenotexploitableinanoperationalNumericalWeatherPredictions(NWP)context.Forthesereasons,anappropriateIASI-NGchannelselectionisneeded,aimingtoselectthemostinformativechannelsforNWPmodels.

Theworkhasbeencarriedoutonadatabaseofsimulatedobservations[Andrey-Andrésetal.(2018)],producedwiththespecificpurposetoserveasasupporttothechannelselection,inadditiontoone-dimensionalvariational(1D-Var)experimentstoevaluatetemperatureandhumidityretrievals.Thestandarditerativechannelselectionmethodology,whichisbasedontheoptimallinearestimationtheory[Rodgers(1996),Rabieretal.(2002)],hasbeenappliedtoasubsetofthesesimulateddata.However,theprocedurehasbeenadjustedsoastoallowspectrallycorrelatederrorstobeproperlyevaluated[Desroziersetal.(2005)].TheentiresimulatedIASI-NGspectrumhasbeeninvestigated,finallyfocusingthechannelselectionprocedureonthewavelengthrangesthemostinterestingfortheassimilation.Methodologiesandresultsonretrievalskillswillbemorecarefullydescribedduringthecourseofthepresentation.14.06 Icecloudproperties,aninformationcontentanalysisfromhighspectralresolutionmeasurementsinthethermalinfrared:ApplicationtoIASIandIASI-NGPresenter:LucieLeonarski,UniversitédeLille-LOAAuthors:LucieLeonarski,LaurentC-Labonnote,JérômeVidot,AnthonyJ.Baran,PhilippeDubuissonIceorliquidcloudcolumnsandprofilepropertiesretrievalfrompassiveandactivemeasurementsrespectivelyhelpusinreachingabetterunderstandingofclimateprocesses.Iftheinformationprovidedbythelatteriscomplete,itsuffersfromspatialcoveragecomparedtopassivemeasurements.Itisthereforeimportanttobettercharacterizecloudpropertiesfrompassivemeasurementsbyusing,forexample,highspectralresolutioninstruments.Besidestheirstrongcontributiontoweatherforecastimprovementthroughdataassimilationinclear-skyconditions,thermalinfraredsoundersonboardpolarorbitingplatformsarenowplayingakeyroleinmonitoringchangesinatmosphericcomposition.However,itisnotoriouslyknownthatclearskyobservationsareonlyasmallpartoftheentiresetofmeasurements,theremainingpartbeingpoorlyusedastheyarecontaminatedbyeitheraerosolsand/orclouds.Thepresentstudyaimstoquantifythepotentialforretrievingicecloudpropertiesandmorespecifically,theIceWaterPath(IWP)togetherwithlayerposition,fromthermalinfraredsounderssuchasIASIandthefutureIASI-NG.Tocharacterisetheobservingsystem,weuseddifferenticecloudprofilecomingfromaglobaldatabase(seehttps://www.nwpsaf.eu/site/software/atmospheric-profile-data/)whereprofilesarechosentoencapsulatenormalconditions,typicalvariabilityandtheextremesofthemodel’sbehavior.Aninformationcontentanalysis(ICA)basedonShannon'sformalismhasbeenusedtodeterminethelevelandthespectralrepartitionoftheinformationabouttheicecloudproperties.BasedonthisICAaretrievalalgorithmhasbeendevelopedandtestedoverthepreviouslydefineddatabase.InthisICAwetookintoaccounttheSignal-to-Noiseratioofeachspecificinstrumentandtheinherentnon-retrievedatmosphericandsurfaceparametererrors.TheforwardmodelusedisthefastradiativetransfermodelRTTOV(Saundersetal.,1999,Matricardi2004),whichhasbeendeveloppedforsatellitedataassimilationinNumericalWeatherPrediction(NWP)models.ForoperationalrequirementsRTTOVisfast,accurateandstable.Theicecloudmicrophysicalmodelusedinthisstudyisbasedontheensemblemodel(BaranandLabonnote,2007),wherethebulkiceopticalpropertieshasbeenparametrizedasafunctionoftheIWCandincloudtemperature.ResultsshowthatthisobservingsystemprovidesinformationonIWPaswellaslayerposition,andshouldthereforebewellretrievedwithexpectederrorsthatdecreasewithcloudopacityuntilthesignalsaturationisreached.Thenumberofdegreesoffreedomforthewatervaporprofileissignificant,leadingtothepossibilityofretrievinghumidityprofileincaseofmeasurementscontaminatedbyiceclouds.Thestudyofmultilayerprofiles(i.e.icecloudaboveliquidcloud)showsthattheinformationabovetheliquidcloudissometimegreaterbecausethelatterreducestheinfluenceofthesurface.

Session15:PolarRegions(oralpresentations)15.01 ArcticObservingSystemExperimentsatECMWFfortheAPPLICATEprojectPresenter:HeatherLawrence,ECMWFAuthors:HeatherLawrence,NielsBormann,IrinaSandu,JonathanDay,JackyFarnan,PeterBauer,LinusMagnussonInthistalkwepresentastudycarriedoutintheframeworkoftheAPPLICATEHorizon-2020project,toassesstheimpactofdifferentArcticobservingsystemsonshortandmediumrangeweatherforecastsatECMWF.ObservingSystemExperimentswereperformedremovingdifferentobservationtypesfromthefullobservingsystemnorthof60degrees,andtheimpactsonshortandmediumrangeforecastswereanalysed.TheanalysisissupportedbyacomparisontoresultsofGlobalOSEs,andtoForecastSensitivitytoObservationImpactdiagnostics.AllArcticobservationswerefoundtohaveapositiveimpactonforecastskillintheArcticregionwiththelargestimapctsontroposphericforecastsduetomicrowavesoundingdatainthesummerandin-situobservationsinthewinter.ObservationsintheArcticwerealsofoundtohaveapositiveimpactonforecastsinthemid-latitudesatlongerlead-times.Thelowerrelativeimpactofmicrowavesoundingdatainwinterislikelyduetodifficultiesassimilatingthedataoversnowandsea-ice,butthereisalsothesuggestionofanincreasedimportanceofconventionaldatainwinter,andotherfactorsmayalsoplayarole.15.02 ImpactofobservationsontheAROME-ArcticregionalmodelPresenter:ZhengQiWang,NorwegianMeteorologicalInstitute(forRogerRandriamampianina)Authors:RogerRandriamampianinaIntheframeoftheApplicateproject(https://applicate.eu),ECMWF(EuropeanCentreforMedium-RangeWeatherForecasts)isrunningseveralobservationsdenialexperiments.HavingaccesstotheresultsoftheseexperimentsopensagoodopportunityforustofulfillourobligationwiththeAlertnessprojecttostudytheimpactofArcticobservationsonouroperationalArcticregionalmodelAROME-Arctic.Inourpresentation,wewillshowindetailstheimpactofdifferentobservationsontheanalysesandforecastsoftheAROME-Arctic.ThepeculiarityofourexperimentsisthatalmosteachobservationdenialexperimentwillbedrivenbysimilarexperimentusingtheECMWFglobalmodel.Thisisverydifferentfromwhatweusuallydo,whereallregionalobservationdenialexperimentsaredrivenbytheoperationalECMWFmodelusingfullsetofobservations.SinceECMWFwaskindlyprovidingtous2setsofobservingsystemexperiments(OSE)–Arcticandglobal–weareabletochecktheimpactofdifferentobservationonourregionalmodelanalysesandforecaststhroughdataassimilationandalsothroughlateralboundaryconditions(LBCs).Furthermore,weareabletochecktheimpactofnon-Arcticobservationsonourregionalmodel.WewillbehappytopresenttheresultsofthisuniqueopportunityatITSC-22.15.03 Continuousobservationofhighlatitudesfromspace:areviewofmediumEarthorbit(MEO)andhighlyellipticalorbit(HEO)optionsPresenter:LouisGarand,ECCCAuthors:A.P.Trishchenko,LGarand,andL.D.TrichtchenkoStilltoday,polarregionsarenotobservedfrommeteorologicalsatellitesathightemporalresolution(10minorless),thatiswithsimilarrefreshratestothosecurrentlyavailablefromgeostationary(GEO)satellites.ThenumberofsatellitesrequiredtogetsuchrefreshratesfromLowEarthOrbit(LEO)satellitesisprohibitivelyhigh(morethan30at60N/S).Severalstudieshaveshownthepotentialtofillthepolargapfromaconstellationof,ataminimum,twosatellitesperpolarareainahighlyellipticalorbit(HEO),orfoursatellitesinmediumEarthorbit(MEO,circular).Theseoptionsareherereviewed.HEOorbitsbetween12-hand16-hareconsidered.InthecaseofMEO,the24-horbitispreferred,characterizedbyaheightverysimilartothatonGEO(~35,800km).Moststudiesbasedtheircoveragerequirementassumingamaximumviewingangle(VZA)of70deg.Forseveralapplications,aVZAlimitoftheorderof62oismoreappropriate.Aswell,anoverlapwithGEOdowntoalatitudeofabout45oisdesirable.WithathirdsatelliteintheHEOconstellationforeachpolararea,ortwomoresatellitesintheMEOconstellation(totalof6),thatcoveragerequirementismetandthepixelresolutioniscomparabletothatachievedfromGEO.Criteriaotherthancoverageandresolutionmustbeconsidered,suchascomplexityofdataprocessingandpotentialexposuretoionizingradiation.Overall,bothMEOandHEOconstellationscanachievecontinuousimagingofpolarregions.

Extendedsoundingcapabilitycouldbeenvisionedbeyondthatavailablefromwatervaporsensitiveimagingchannels.Session15:PolarRegions(posterintroductions)15p.01 PRECISE–ProductionofaregionalReanalysisforEuropewithintheCopernicusclimatechangeServicesPresenter:ZhengQiWangAuthors:ZhengQiWang,SemjonSchimanke,PerUndén,MartinRidal,LarsBerggren,PatrickLeMoigne,EricBazile,RogerRandriamampianinaPRECISE(ProductionofaregionalReanalysisforEuropewithintheCopernicusclImatechangeSErvices)isaCopernicusservicethatwillbelunchedundersummer2017.Thegoaloftheserviceistoprovidearegionalreanalysis(RRA)fortheatmosphere,whichwillbeupdatedinnearreal-time.Inthispresentation,wewillgiveanoverviewonwhatcanbeexpectedfromtheservicewithinthenextyears.Inthefirstphase,PRECISEproductswillbebasedonthemodelsystemdevelopedinthepre-operationalprojectUERRA.WhileUERRAproduceddatasetsarefortheperiod1961-2015,PRECISEwillfillthegapthroughout2016andearly2017.Thiswillbefollowedbymonthlyupdatesinnearreal-time.Hence,PRECISEwillofferaconsistentRRAfrom1961tonearrealtime.Theonsetoftheoperationalmonthlyupdatescanbeexpectedinlate2017.AlldatawillbesavedwithhourlyresolutionandwillbefreelyavailableviatheCopernicusDataStore(CDS).DetailsoftheproductionsystemaswellaschallengesofproducingaRRAoperationalinnearreal-timewillbediscussed.Inthesecondphase,itisplannedtoswitchtoamoreadvancedsystemwhichwillbedevelopedwhiletheoperationalproductionoftheRRAcarriesonduringphaseone.ThenewPRECISEreanalysissystemwillbebuiltonusingandextendingtheUERRAsysteminseveralways:Thehorizontalresolutionofthedataassimilationsystemwillbeenhancedfrom11kmto5.5kmandalsotheverticalresolutionwillbeincreasedfrom65toabout90levels.Thetopofthemodelwillberaisedtoallowassimilationofsatelliteradiances.Moreover,thenumberoflayersintheboundarylayerwillbeincreased.TheMESCANsurfaceanalysisoftemperatureandhumiditywillbeintegratedinthereanalysisproductionsincenodownscalingwillbenecessary.Newinputdatasetswillbetestedandintegrated.Further,anumberofnewobservationtypesorinstrumentswillbetested.ThelateralboundarywillbeforcedbytheglobalERA5reanalysis.Theonsetoftheproductionwiththenewsystemushersthesecondphaseoftheservicein,whichisscheduledfor2019.TheRRAdatasetwillstartintheearly1980'sandwillbeupdatedoperationalinnearreal-time.Detailsoftheadvancedsystemaswellasthetimeschedulewillbediscussed.15p.02 TheArcticRegionalReanalysisoftheCopernicusClimateChangeServicePresenter:ZhengQiWang,NorwegianMeteorologicalInstitute(forHaraldSchyberg)Authors:HaraldSchyberg,HeinerKörnich,RogerRandriamampianina,EivindStøylen,XiaohuaYangWewillpresentstatusandplansfortheArcticregionalreanalysisoftheCopernicusClimateChangeService(C3S).TheprojectaimsatproducinganArcticregionalreanalysisovertwoArcticsubdomainsofinterestforchangeprocessesandeconomicactivities.Thereanalysiswillcovertheperiod1997-2021withahorizontalresolutionof2,5km.Additionallyaproof-of-conceptforapan-Arcticreanalysiswillbeprovidedforaperiodofoneyear.ThesystemtobeusedisbasedontheHARMONIE-AROMENumericalWeatherPrediction(NWP)system,withadditionsandconfigurationchoicesforreanalysispurposeswiththatsystem.GlobalreanalysisdatafromERA5willbeusedforlateralboundaries.TheArcticreanalysiswilladdvalueversustheglobalreanalysisbyprovidinghigher-resolutionandbyusingregionaldatanotavailableintheglobalreanalysissystem.Developmentstoadaptthesystemforreanalysispurposeincludedmodificationsintheassimilationsetup,3D-Varbackgrounderrorstatisticsanduncertaintyestimation.Theupperairassimilationusesconventionalobservationsand,sincetherearegapsintheconventionalobservingsystem,hasemphasisonusingsatellitedatasetswhichhavegoodcoverageintheArctic.Thisincludesimportantpartsofthesatelliteobservingsystemsuchasmicrowaveandinfraredradiances,atmosphericmotionvectors,scatterometerwindsand

GNSSradiooccultationdata.Handlingof“coldsurfaces”inthesurfacescheme,suchassnow,seaiceandglaciers,whichareimportantintheArctic,hasalsoreceivedspecialattentionwiththeaimtogiveabetterrepresentationthanintheglobalreanalysis.Inadditiontothedevelopmentofthesystemitself,wepresentthepresentstatusofitsproductionandwegiveanassessmentandanalysisofthequalityofthereanalysisproducts.15p.03 ImpactofTerrestrialandSatelliteObservationsoverthePolarRegionsontheECCCGlobalWeatherForecastsduringtheYOPPSpecialObservingPeriodsPresenter:StephaneLaroche,EnvironmentandClimateChangeCanadaAuthors:StephaneLarocheandEmmanuelPoanOnegoalofYOPPistomakerecommendationstoWMOandmeteorologicalcentersonthefutureconfigurationoftheobservingsysteminpolarregions.WiththegrowinghumanactivitiesandtheimplementationofnewMETEAREAstoprovideweatherinformationandforecastsintheArctic,itisindeedrelevanttoexaminethevalueofinsituandsatelliteobservationsinhigh-latitude.Theroleofthevarioustypesofobservationsovertheglobeinnumericalweatherpredictionatmid-latitudeisnowwellunderstood.TheimpactofobservationsintheArcticislessclearsinceterrestrialobservationsaresparseandsatelliteobservationsareaffectedbyiceandsnowforwhichtheemissivitypropertiescanbedifficulttoestimate.Furthermore,thereareveryfewaircraftreportsoverthepolarregions,whicharenowanimportantsourceofobservationsinthenorthernhemispheremid-latitude.YOPPprovidesagoodopportunitytoexaminetherelativeimportanceofthevarioussatelliteandterrestrialobservations,theforecastskillintheArcticwithrespecttothatinmid-latitudeandtheimpactofobservationsinhigh-latitudeontheforecastskillatmid-latitude.SimilarprojectswereundertakenearlierbyECMWFandafewEuropeanmeteorologicalcentersundertheEU-fundedAPPLICATEWorkPackage4(WP4).ECMWFcarriedoutaseriesofObservingSystemExperiments(OSEs)polewardof60degreesovertheNorthandSouthPolesforthethreefour-monthperiods:DecembertoMarch2018andJunetoSeptember2016and2018.Theselecteddatadeniedpolewardof60degreesarethefollowing:microwaveradiances,hyperspectralinfraredradiances,conventionalobservations,GPS-ROandAMVs.AfewadditionalOSEswerealsoconductedtoexaminetherelativeimpactoftemperatureandhumiditysensitivemicrowaveradiancesandtheimpactoftheadditionalradiosondesandbuoysthatwerelaunchedduringtheYOPPspecialobservingperiods.AtECCC,wecarriedoutthesameOSEsasproposedbyECMWFforthetwofour-monthperiodsof2018,incollaborationwithWP4partners.ThisjoinedeffortenablesthecomparisonofOSEsresultsfromtheparticipatingweathercentersandadeeperinvestigationoftheroleofthevariousobservingsystemsovertheArcticandAntarcticregions.Inthispresentation,wefocusontheOSEresultsobtainedatECCC.Theimpactsofdenyingthevariousobservingsystemsonshorttomedium-rangeforecastsusingdifferentmetricsareshown.Thecomparisonofimpactson24-hforecastsfromtheOSEsandFSOIovertheArcticregionsisalsopresented.Aparticularattentionisgiventothesummer2018period(secondYOPPSOP)duringwhichalargervolumeofadditionalobservationsweredeployed.International(oralpresentations)b.01 CommunicatingthevalueofpassivebandsusedbyTOVS-heritagemicrowaveinstrumentsinthecontextofradiofrequencyinterferenceandspectrumallocationPresenter:StephenEnglish,ECMWFAuthors:SEnglish,RKelley,MDreis,EDaganzo-Eusebio,VNozdrin,NBormann,ACollard,RFaulwetter,CKöpken-Watts,MKazumori,JMahfouf,CHarlow,BrettCandy,JEyre,MBanks,MBuehner,CTingwell,FSmith,IKwon,RRandriamampianina,SSwadley,etal.Manyapplicationsneedradiofrequencyspectrum.Meteorologyandearthsystemmodellingandpredictionisjustone.Inthepasttelecommunicationapplicationshavegenerallyoperatedinlowfrequencybands(e.g.P,L,CandXband),whereatmosphericattenuationislow.Howeverastheirbandwidthrequirementsincrease,andastheamountofavailablebandwidthatlowerfrequencyisverysmall,theyareincreasinglylookingtobandsathigherfrequency,despitethestrongerattenuation.Forexample5GhasrequestedbandsadjacenttothefrequenciesusedbysounderssuchasAMSU-A,ATMS,MWTS-2,MTVZA-GYandSSMISat23.6-24GHz,50.2-

50.4GHzand52.6-53GHz.Itisthereforemoreimportantthaneverthatthevalueofallpassivebandsusedinmeteorologyandearthsystemscienceandpredictionisdocumentedandcommunicated.Itisalsoimportanttoassessandoutlinetheeffectinterferencewouldhaveontheexploitationofthemeasurementdata.TothisendandinresponsetoanactionfromITSC-21ECMWForganisedaworkshopin2018involvingmanyleadingNumericalWeatherPrediction(NWP)centresandspectrummanagersfromEuropeandtheUSA,aswellastheITU.TheworkshopfocussedexclusivelyonthevalueofthebandsinNWP.TheworkshoppresentationsshowedconsistentresultsfromtheNWPcentresindicatingthatpassivemicrowavebandsarethemostimportantsourceofobservationsformeteorology,providing30-40%ofallforecastskillfromobservations.Furthermoreitwasshownthattheirexploitationisbecomingevermoresophisticated,beingassimilatedinallweatherconditionsoverbothlandandoceansurfaces.Inordertoassessthesocio-economicimpactofadegradedNWPcapability,anumberofstudieswerepresentedattheworkshop.Theseemploydifferenttechniquesandlookatabroadrangeofcountriesandeconomies,butallthesestudiesconcludethattherearehugeweatherforecastbenefitstosocietiesbothintermsofpublicsafetyandalsodirectmonetarybenefits.Asthequalityofsatellitedataandtheforecastsystemsthemselvesimprove,thisbenefitwillfurtherincrease.Itisimportantthattheinformationcollectedandreportedatthisworkshopisregularlyupdatedandcomplementedwithmoreevidencetosupportagreedinterferencethresholdsinpassivebands.AlthoughtheworkshoporganisedbyECMWFfocussedonNWPapplicationsitisimportantthatallapplicationsofthedataarecapturedinthesereports(e.g.climate).ECMWFiscommittedtocontributingtotheorganisationofregularworkshopsonNWPapplicationsbutencouragesITWGtocoordinateaholisticapproachtothetopic.b.02 UpdatetoWorldRadiocommunicationConference2019andWRC-23itemsofinterestPresenter:RichKelley,AlionScienceforDOC/NOAA/NESDISAuthors:FredMistichelli,RichKelleyThedatesofthenextWorldRadiocommunicationConferencewillspanthoseofITSC-22.ThereforeWRCoutcomeswillbeunknown.ThistalkwillreviewandupdateitemsofinterestduringWRC-2019anddiscusspossibleitemsfortheWRCin2023.b.02 WMOSpaceProgrammeUpdatePresenter:HeikkiPohjola,WMOSession16:FutureMissions16.01 CombinedPolarHyper-spectralandGeo-multispectralData-DemonstrationoftheNeedForGeo-Hyper-spectralSounderPresenter:WilliamSmith,SSECAuthors:W.SmithSr.,R.Knuteson,H.Revercomb,E.Weisz,andQ.ZhangPolarhyperspectalsoundingandgeo-multi-spectralimagerydataarenowroutinelycombinedtoprovideahighspatialandtemporalresolutionsoundingssimilartothosethatcouldbeprovidedbyageo-hyperspectralsoundinginstrument.ThecombinedverticalprofileretrievalproductaccuracyisgenerallygoodwhenthegeographicalregionsobservedandtheobservationtimesofPolar-hyperspectralandGeo-multispectraldataarerelativelyclose,dependentupontheatmosphericcondition.ItisshownthataccuracyofanalysesofsoundingsderivedfromcombinedhighverticalresolutionPolarhyperspectralandhighhorizontalandtemporalresolutiongeo-multispectralsoundingimagery,isusuallysuperiortothatbasedonlyontherelativelypoorhorizontalspaceandtimeresolutionpolarhyperspectralsoundingretrievals.Thisresultclearlydemonstratestheimportanceofthehighhorizontalandtemporalresolutionsoundinginformationthatisprovidedbyageostationarysatelliteinstrument.However,thecombinedsoundingproductaccuracycandegradesignificantlyduetorapidlocalizedchangesinatmosphericconditions,suchasthoseassociatedwithconvectiveweatherdevelopment.Thedependenceofthecombinedsatelliteproductaccuracyonbothspaceandtimedifferencesofthetwosetsofdataisquantifiedwithrespecttotheatmosphericconditionbeingobserved.Thepotentialimpactoftheseerrorsonforecastaccuracyisdiscussed.Theresultsstronglysupporttheneedforageo-hyperspectralsounderto

improvetheaccuracyofforecastsachievablefromtoday’spolarhyperspectralandgeo-multispectralradiancedata.16.02 HyperspectralImagingInfraredSoundingfromgeostationaryorbitPresenter:JoeTaylor,UW-SSEC/CIMSS(forHankRevercomb)Authors:HankRevercomb,BillSmith,BobKnuteson,DaveTobin,JoeTaylor,FredBest,JonGeroMeasurementsfromanadvancedHyperspectralImagingIRSounder(HIIS)overtheUSwouldformthebasisforsevereweatherwarningswithsignificantlylongeradvancetimesthancurrentlypossible.Whencombinedwithindependentsurfacedata,HIISobservationswouldprovideauniqueabilitytodetectrapidchangesinatmosphericstabilityandthemoisturefluxconvergencethatservesbothasatriggeringmechanismforinitialstormdevelopmentandafuelsourceforcontinuedstormgrowth.Thegeostationaryorbitallowsforrapidrepeattimes(ashighas5-15minutes)andopportunespatialsampling;theadvancedimagingIRsounderisunmatchedinitsabilitytosenseverticallyresolvedtemperature,moisture,andcloud/moisture-trackedwindvectorswithspatialresolutionsashighas2-4kmhorizontaland1kmvertical.ManyoftheseimportantcapabilitiesoftheGEOadvancedsounderhavebeendemonstratedbythehighlypositiveimpactonglobalweatherpredictionofhighspectralresolutioninfraredsoundersinpolarorbit(IASIonMetOpspacecraftsandCrISonSuomiNPPandJPSS).ThisdemonstrationoftheimpactofverticalinformationontheglobalscalemakesastrongcaseforwhatwillbepossiblefromGEOobservationsthatcombinethesametypeofdetailedverticalprofileinformationwithrapidtimesequencing.VerticalprofileinformationwellbeyondthecapabilityofGEOimagersisneededtoexploitthenextfrontierofferedbyrapidsamplingdataforforecastinghighimpactsevereweathereventsincludingtornadoesandhurricanes.Aswillbesummarized,technologicalfeasibilityhasbeenwellprovenintheUSbypreviousstudiesfundedbyNASAandNOAAdatingbackto2006,andinnovativeapproacheshavebeendefinedtomakereasonablyrapidimplementationpossible.WewillalsoshowresultsfromthefirstinflightproofofconceptbyChina(onFY4-Ainlate2016)thatareencouragingforthefutureflightswithsignificantlyupgradedcapabilityplannedforthenextseveralyears.Also,asiswellknown,EUMETSATwillflytheIRSwithaverysophisticatedcapabilityonMTGin2022-23.TohelpsatisfyWMOrequirementsforaglobalconstellation,andtoprotectourowncitizens,theUSshouldputhighpriorityonprovidingthiscapabilityassoonaspossible.16.03 AcceleratingTowardNOAA'sNext-GenerationObservingArchitecturePresenter:KarenSt.Germain,NOAA/NESDISAuthors:K.St.Germain,F.Gallagher,M.Maier,D.SpencerNOAAhasbegunplanningforthefutureobservationarchitecturethatwillaugmentandeventuallyreplacetheJPSSandGOES-Rprogramcapabilities.Insupportofthiseffort,NOAArecentlycompletedafuturearchitectureanalysistoexaminespacecapabilityoptionsinthe2025to2050timeperiod.Informedbythisanalysis,NOAAisweighingdifferentcombinationsofspatial,spectral,andtemporalresolutionforatmosphericsoundingcapabilities.Weseektooptimallybalancemissionperformance,cost,andagilityinthefutureatmosphericsoundingarchitecture.ThispaperwilldiscussNOAA'scurrentthinkingandseekfeedbackfromtheATOVScommunityonhowtoweightandoptimizetheseoptions.16p.01 EUMETSATPlansPresenter:K.DieterKlaes,EUMETSATAuthors:K.DieterKlaesThispaperwillsummarizeEUMETSAT'scurrentstatusandtheplansofthefutureprogrammes.ThiswillincludethecurrentandfuturemandatoryprogrammesinLEOandGEOorbits,EPS,MSGaswellasEPS-SGandMTG.Thirdpartyandoptionalmissions,includingCopernicusMissionswillbeaddressedaswell,andalsothegroundsegments.