gewex data analysis panel an overview · 2018. 2. 1. · (v3.0) 3-hourly, monthly averaged...

GEWEX DATA ANALYSIS PANEL An overview Report for SSG-30

RémyRoca(CNRS) and TristanL’Ecuyer(UW-AOS)remy.roca@legos.obs-mip.fr tristan@aos.wisc.edu

A New GDAP Vision Anintegratedapproachtoenergy-water-massconsistencybasedonrefineduncertaintycharacteriza8on

Sealevel

Gravimetry

Precipita8on

Radia8on

CloudSurfaceFlux

Consistencyasawayoflife

A mission

RunCorebuissness•  SponsorproducGonandanalysisofdatasetfromsatellite(e.g.,ISCCP,GPCP)•  Sponsorassessmentofdatasettoimproveuncertaintyes8ma8onfordatarecords•  Sponsorgroundbasednetwork(e.g.,BSRN,GPCC)

ActasaninterfacebetweentheGEWEXac8vi8esandthedata•  PROES,GAP,GASS…,Concept-Heat,conference2018organisa8on

RepresentGEWEXatvariousWCRPmee8ngs,WMOandotherrequests

•  FromtoassessadequacyofobservingsystemtodataprizetoDOI…..

The GDAP acDviDes porEolio and who’s doing what PanelmembersRémyRoca,chairTristanL’Ecuyer,vice-chairWouterDorigoAndrewHeidingerSeijiKatoChris8anKummerowHirohikoMasunagaIsabelTrigoClaudiaStubenrauchTianjunZhou

GrounddatanetworkWouterDorigo ISMNA.BeckerandUdoSchneider(DWD) GPCCChuckLong(NOAA) BSRNJimMather(ARM) ARM

«GEWEX»datasetsproducGonPaulStackhouse SurfaceRadia8onBudgetBobAdler GlobalPrecipita8onClimatologyProject(GPCP)StefanKinne GlobalAerosolClimatologyProject(GACP)CarlosJimenez LandFluxCarollAnnClayson SeafluxBillRossowandNOAANCEI ISCCPPBrownandCKummerow GEWEXMergedandIntegratedProduct

«GEWEX»AssessmentsClaudiaStubenrauch(CNRS)CloudsMarcSchröder(DWD) WaterVaporJeffreyReid(NRL) AerosolsHirohikoMasunaga Precipita8on

InvitedmembersGraemeStephensSSGChairSoniaSeneviratneSSGChairPetervanOevelenIGPOWilliamRossowFounder

GEWEXPROESClaudiaStubenrauch(CNRS)UTCCSueVanDenHeever(CSU)GAP

2toberenewed2moreneeded

GDAP in GEWEX and WCRP •  Tristanpar8cipatedinGAP

•  RémyattheWCRP/WorldDataAdvisoryCouncilmee8ng,inMarchinFrasca8,Italy(reportherehfps://www.wcrp-climate.org/wdac-ac8vi8es/163-unifying-themes/working-groups-wdac/wdac-ac8vi8es/wdac-6/1084-wdac-6)

•  GaveanoverviewofGDAPac8vity•  FindoutaboutTIRAandthesurfacefluxTT•  TIRA,Obs4mip,GCOS/IP,RRR,CEOS/CGMSWGClimate,CEOS/CGMSIPWG

•  TrytopromoteGDAPneedstoTIRA(firstmee8ngsetupandcancelled,rescheduledlastweekbutIfailedtojointhewebconference)•  WDAChaspublished«BestPra8cesforAssessment»(!)

•  Ac8on:InviteSOLASrepresenta8veatGDAPmee8ng.Ididbuttheinviteecouldnotmakeit

•  DATAPrize:itispossibletonominatefolksforaprize(notonlyyoungnotonlyscien8sts)

•  Requestforlefersinsupportofproposals•  No!

•  DiscussiononThursdaymorning

•  RequesttoreportGDAPac8vityat2017IRSconferenceinSouthAfrica•  No8me!•  Requesttocontributetothenext2020IRSconferencesessionselabora8on

•  TandRareco-organizersofConcept-Heatmee8nginOctober2017

The 2017 GDAP meeDng, Boulder, CO, USA

JointwithCONCEPT-HEATmee8ngHostedbyK.ThrenberthatNCARfacility.VeryniceseongandlocalsupportfromKevin!2daysofverybusymee8ngHalfadayofGDAPcentereddiscussionwithonlythepanelmembersAllusualac8vity+newpresenta8ons(LST;OHCfromAlimetry-GRACE;TWS)NextmeeGnginlateNov2018inPortugalhostedbyIsabelTrigo

Outline of the presentaDon

•  Endorseddataproducts• Ground-basednetworks• Assessments• Newitemsandothers..

ENDORSED DATA PRODUCTS 1.  Cloud(ISCCP)2.  Precipita8on(GPCP)3.  Surfaceturbulentflux(Seaflux)4.  Radia8on(surf+toa)(SRB)5.  Aerosols6.  Landflux7.  IntegratedProduct8.  Soilmoisture

Allteamsareinvolvedinproducing,validaGngandimprovingtheirproducts

normally produced ~ 3 months after observation time

[although produced using different data sets and algorithms, products are integrated,i.e. they add up]

GPCPGlobalPrecipita8onProductsVersion2.3

NASA,NOAA,DWD,UMD,GMU,others

•Monthly,2.5°MergedAnalysis(1979-present) Adleretal.(2003),J.Hydromet.

Huffmanetal.(2009)GRL

•Pentad,2.5°MergedAnalysis(1979-present) Xieetal.(2003)J.Climate

•Daily,1°MergedAnalysis(1997-present) Huffmanetal.(2001)J.Hydromet.

GlobalPrecipita8on(1979-2016)

Land+Ocean

Land

Ocean

UpdatedfromAdleretal.(2017)Rev.Geophys.

Timeseriesoftropicalprecipita8on(25oN-25oS)

Issues/Plans

•  Version3behindscheduleandfullcomple8onisatriskduetofundingques8ons

•  Produc8onofV2.3willcon8nue,althoughsomedifficul8esintermsoftransfertoNOAAandconcernaboutaddressinganyproblemsthatmightcomeup.

•  Expectproduc8onofbothGPCPVersionsbetween2-5yearsasuserscompareandfindissuesthatneedafen8on.

•  Con8nuedproduc8onofV3hopefullysupportedbyNOAAand/orNASA

CarolAnneClayson,WHOIWithBrentRoberts,MSFCAndJeremiahBrown,PrincipalScien8ficCompu8ng6thGDAPMee8ng

11October2017Boulder,CO

UpdateonSeaFlux

SeaFlux •  Interna8onalprojectundertheauspicesoftheGEWEXDataandAssessmentsPanel:toimproveourunderstandinganddeterminaNonofoceansurfaceturbulentfluxes

•  Ourmainques8ons:•  Whatisfeasibleintermsofresolu8onandlength-of-8meseriesforsatellitedata?•  Canweproduceahighresolu8ondatasetusingsatellitesthatisbeferthanconven8onalclimatologyandNWPproducts?

•  Whatarethebestmethodsforcrea8ngthisdataset?•  Howdothedifferentdatasetsperformundervaryingapplica8ons?

•  Elementsoftheprojectinclude:•  Evalua8onofglobalfluxproducts•  Providinglibraryoffluxdatasetsandinsitudatasetsforeasycomparisonsbyresearchers

•  Produc8onofahigh-resolu8on(1o,3hourly)turbulentfluxdataset

SeaFlux CDR version 2 ¨  Near-surfaceairtemperature,humidity,

andwinds¤  BasedonRobertsetal.(2010)neuralnettechnique

n  CLWcontentusedtoremoverain-contamina8on(exceptforF08)

n  F10–F18,pixelssegregatedbyclear/cloudyskyn  OneneuralnetforF08,twoforallothers(total)

¤  SSM/IandSSMISfromCSUFCDR¨  SST

¤  Pre-dawnbasedonReynoldsOISST¤  Diurnalcorrec8on¤  UsesSRB,CERES,FLASHFluxforradia8on,HOAPS,

GPCPandnowMERRAforprecipita8on¨  LandmaskfromNOAAGSHHG,icemask

fromAVHRRicefrac8on,ISCCPiceshelf¨  UsesneuralnetversionofCOARE¨  Gap-fillingmethodology--useofMERRA2

variability–3hour

¨  Availablefrom1988throughmid-2017

1999LatentHeatFlux

1999SensibleHeatFlux

Under WCRP Data Advisory Council (WDAC)

•  Discussionofneedforcoordina8onandhighligh8ngsurfacefluxissues•  Land,ocean,ice•  Biogeochemical,heat,moisture,momentum•  Turbulent,radia8ve•  Insitu,remote

•  “promoteastrongerdialogueandprofileoffluxeffortsacrossWCRPandwithsisterprogrammes“

•  FormedSurfaceFluxTaskTeam(C.A.Clayson/BrianWard,chairs)•  CutsacrossGEWEX,CLIVAR,otherWCRPgroups•  Members:

•  CarlosJimenez(ObservatoiredeParis,land,satellite,obs;•  JimEdson(U.Conn,ocean,obs);•  Pierre-PhilippeMathieu(ESRIN,satellite);•  PeterGleckler(LLNL,modeling);•  RonaldBussdeSouza(Na8onalIns8tuteforSpaceResearch,Brazil,ocean,obs)•  PaulStackhouse(NASALangley,radia8vefluxes,satellite,scien8stextraordinaire);•  HansPeterSchmid(KarlsruheInst.Tech.,biosphere,obs);•  AntonBeljaars(ECMWF,land,modeling);•  SaigusaNobuko(Japan,Na8onalInst.forEnv.Studies,land,obs);•  PetraHeil(UniversityofTasmania,seaice,obs,remotesensing,modeling);

19

LandFlux: data producDon – V1

•  V1:1984-2007 3-hourly global runs of latent heat flux from GLEAM, PT-JPL, PM-MOD with “GEWEX-like” forcings (mainly SRB), publicy available from the LandFlux website.

https://hydrology.kaust.edu.sa/Pages/GEWEX_Landflux.aspx

20

LandFlux: data producDon - V2?

•  Past lines of funding (ESA WACMOS-ET, NASA NEWS, KAUST …) are terminated and we do not foresee any new opportunities to fund these activities.

•  Model used for LandFlux have been updated by their respective developers and run further in time, so their individual global runs are likely to be more demanded than the LandFlux products.

•  Possibly the only justification for a V2 LandFLux product would be in the framework of a GEWEX integrated product, where some common forcings (e.g. SRB radiation) will allow a more consistent multi-product analysis. In that case, we may find the man power to re-run the LandFlux models with the new forcings.

•  The sensible heat fluxes are still pending ….

GEWEX Data and Assessments"Panel Meeting"Boulder, CO"

10-12 October, 2017""

The GEWEX Surface RadiaDon Budget Project: Release 4 Integrated Product

Progress and Plans

PaulW.Stackhouse,Jr.–NASALaRCStephenJ.Cox,J.ColleenMikovitz,TaipingZhang–SSAI

SRB Release 3 Data Products (SpaDal ResoluDon: 1o x 1o; 7/83 – 12/07)

Data Types Model Name Temporal Resolution Parameters

SW

GEWEX SW (Pinker/Laszlo)

(v3.0)

3-hourly, Monthly Averaged 3-hourly, Daily and Monthly Averaged

(UTC and local sun time)

All-sky: Surface down, up, PAR down; TOA Down, Up

Clear-Sky: Surface Down, Up; TOA Up

LPSA (Staylor/ Gupta) (v3.0) Daily, Monthly

All-sky: Surface Down, Net, and Albedo

Clear-sky: Surface Down

LW

GEWEX LW (Fu/Liou/

Stackhouse) (v3.1)

3-hourly, Monthly Averaged 3-hourly, Daily and Monthly Averaged

All-sky and clear-sky: TOA up; Surface Up and Down

LPLA (Gupta) (v3.0)

3-hourly, Monthly Averaged 3-hourly, Daily and Monthly Averaged

All-sky Surface Downward, Net; Cloud Radiative Forcing

Input Property

CLDPROPS 3-Hourly

Surface emissivity, skin temperature, atmospheric

profile; cloud phase, fraction, optical depth and LWC

Note: The LPSA and LPLA algorithms are also used in CERES Surface-Only"11Oct2017

22GDAP2017

SRB Rel 4 versions, global fluxes: 2007

1Dec2016 GDAP2016 23

Rel3.0 Rel4baseline(OLDalgorithm,NEWinputs–HXSBeta1)

Rel4gamma(NEWalgorithm,OLDinputs-DX)

Rel4iota(NEWalgorithm,NEWinputs-HXSBeta1)

Rel4nu(NEWalgorithm,NEWinputs–HXSV01)

Surfacedown 186.1 186.1 182.3 184.3 184.8

Surfacedowndiffuse 104.1 105.8 95.0 96.2 100.1

TOAUp 104.4 104.2 103.1 100.4 100.5

Clearsurfacedown 247.6 246.8 240.5 239.9 239.9

Pris8nesurfacedown 258.5 258.5 252.8 252.1 252.2

Surfacealbedo 0.131 0.132 0.137 0.126

Surfacenet 163.5 163.7 158.7 161.9

CloudRadia8veEffect -61.5 -60.7 -58.2 -55.6 -55.1

24

GEWEX SRB: Summary Status

• GEWEXSRBRel4-IP:•  NewinputsfromISCCPnnHIRSandHXSv1obtained(inSeptember)wereprocessedat1ox1o

•  versionsdeliveredforSRBRel4-IPBeta

•  AnalysisshowsimprovedSWfluxesrela8vetoBSRNandoceanbuoymeasurementsandalsonewinputsandalgorithmsrela8ve

•  Oceanfluxesreduced;landfluxesincreased•  TOAreflectancereduced

•  AnalysisshowsLWs8llnotimproved•  HXScloudrenderingtes8ng/assessmentongoing;homogeniza8onwithnewmicrophysics

•  Assesslandsurfacetemperatures•  Oceanfluxesappearedbiasedcomparedtooceanbuoys

11Oct2017 GDAP2017

GEWEX SRB: Next Steps • Willre-accessassump8onsregarding=>cloudmergingandgridding(1-2months)

•  CompareandhomogenizewithHGG•  Reprocess2005-2009withrevisedcalibra8ontablesandcloudproper8es

•  Willbeassessingchangestocloudproper8es•  ComparetoCERES,BSRNandassess•  Produc8on8merequiredfor1x1:10yearspermonth

•  Redeliverat1x18meperiodsneeded;consideringnotreprocessing30yearsat1x1=>2000-2009

•  Progressofconversionto½ox½onearlycompleted•  Usingfullequalareasamplinganddataproduc8ongrid•  Willprovidedataprovidesatthe½ox½oequalangle•  Tes8ngaspectsonCloudbasedsystem

11Oct2017 GDAP2017 25

MACv2fromsunphotometrytoaerosolcomponentdistribuGons

spaGalcontextbyglobalModeling:AeroCom

Aerosols(StephanKinne)

what updates ?

• monthlyAODvariability(ratherthanonevalue)withimpactontheAODfine-sizefrac8on

•  basedon6hrsdatafromICAPensembleassimila8ons(bysevengroups)over2years

• mergingfine-moderadius(asofAngstrom)•  AngstromisinaccurateatlowAODvalues•  moreexactfine-moderadiuspermitsbeferes8matesforassociatedCCNàforaerosoloncloudimpactsimula8ons

Aerosols(StephanKinne)

•  Testperiod:1Jan2007–31Dec2007•  1°,3-hourlyequal-areagrid•  ISCCPClouds,MACV2.0Aerosols,SRBRadia8on(TOA&Sfc)

•  Land/SeaFluxforSensibleandLatentheatflux,•  GPCPPrecipita8on•  ERA-InterimforWaterVaportransport,CAPE,dynamicalcontext

The “Integrated Product”

An example from integrated products from P Brown and C Kummerow (CSU) E −P =∇⋅Q

A Water Budget Closure Test over tropical oceans

−200

−100

0

100

200

300

Ave

rage

(mm

.mon

th−1

)

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 200827282930

SST

(°C

)

SF EGPCPSST

SF E − GPCPERA ¢Q

Tropical Indian

Deep convective cloud fraction from ISCCP

2007GEWEXIntegratedproducts

Net surface heat flux

E, P and Div(Q)

PBrownandCKummerow,2012

CourtesyofCKummerow

Releaseof20yearsQ12018ScienceworkshopMarch2019inSpain

Towards a long-term mulD-satellite data set

•  ESACCIECVsoilmoisturedatasetservesover3700registeredusers:•  CurrentlyTHEstate-of-theartmul8-satelliteSMproduct

30

[Dorigo,W.A.,2015,IGARSS]

ESA CCI merging in a nutshell

•  Individualdatasetsareharmonized,error-characterized,andmergedinto•  anac8ve-onlyproduct•  apassive-onlyproduct•  acombinedac8ve-passiveproduct

31

[Gruberetal.,2017,Dorigoetal.,2017,RSE]

ESA CCI merging in a nutshell •  Recentevolu8onfromasimpleternary-classbasedac8vepassivemergingtoaleast-squaresbasedmergingusingerrores8matesobtainedfromtriplecolloca8onanalyses

•  Con8nuousweigh8ngofdifferentproducts•  Mergingofmorethan2productspossible

32

Blending weights for merging AMSR-E, Windsat, and SMOS into a merged passive dataset (July 2010- October 2011) [Gruber et al., in prep.]

Blending weights for merging ACTIVE and PASSIVE into COMBINED (period January-December 2014) [Dorigo et al. 2017, RSE]

Towards operaDons •  TheCopernicusClimateChangeService(C3S)

•  Providesdaily,decadalandmonthlysoilmoistureses8mates•  Witha10-daylatency•  Scien8ficupdatesfromtheCCIimplementedwithayeardelay(forvalida8onpurposes)

33

Next steps…

•  Whatdouserslike?•  Long8meseries•  Goodspa8al-temporalcoverage•  Wellerror-characterized

•  Whatshouldbeimproved?•  Longer8meseries•  Beferspa8al-temporalcoverage/resolu8on•  Reducingerrors•  Beferdefini8on/harmoniza8onoflayer-depth•  RemovingdependenceonLSMs(!)

•  Issuesprogressivelytackledinnewproductversions!•  ESACCISMv04.xtobereleasedend2017•  ESACCISMv05.xalreadyunderdevelopment•  Nodirectfundingforscien8ficimprovements!

34

GROUND-BASED NETWORKS SoilmoisturePrecipita8on

SurfaceRadiaGon(BSRN)ARM

ISMN overview

•  BasicprocessingchainoftheISMN•  Datafromdifferentnetworksarecollected…•  …harmonized…

•  format,units,sampling,…•  …quality-controlled…

•  Automatedflaggingofsuspiciousvalues•  …storedintoadatabase…•  …andmadeavailableforthepublic

throughawebportal•  Variousviewinganddownloadingop8ons

36ExampleoftheISMNdataviewershowing8meseriesofdifferentvariables.

Some facts… •  CurrentlyavailableattheISMN:

•  58networks•  2436Sta8ons•  Historicaldatasets

•  reachingbackto1952•  Opera8onaldatasets

•  UpdatedinNRT

37

§  Addi8onalvariables:•  Soiltemperature•  Airtemperature•  Precipita8on•  Snowdepth•  Snowwaterequivalent•  „Sta8c“variables(texture,satura8onpoint,..)

What’s next?

•  TheISMNisaGEWEXsuccessstory,servingalargeinterna8onalscien8ficcommunityandhavinglargeimplica8onswellbeyondscience,e.g.through

•  Improvementofweatherandclimatemodels•  Valida8onofremotesensingproducts•  Supporttoagriculture•  …

•  Good-value(costs~50kEUR/y)•  Financialsupportfor2009–June2016hascomefromESAEOPSMOS•  Currentnego8a8onsforafundingextensionofanother1.5years(underESA’sSensorPerformance,Productsand

Algorithms(SPPA)element)

•  ESA‘smandateisnotopera8ons,thereforeanewmechanismneedstobefound(soon!)•  Noconcretealterna8vessofar…•  InterestoftheGermanFederalIns8tuteofHydrology(BfG;well-knownfortheGlobalRun-offDataCentre)

38

The Atmospheric RadiaDon Measurement (ARM) Climate Research Facility

JIMMATHERARMTECHNICALDIRECTOR

October11,2017

Overview of ARM Facility

40

Long-term, comprehensive atmospheric observing network "   Since 1992, providing measurements of cloud & aerosol properties, & their

impacts on Earth’s energy balance "   Network of 3 fixed & 3 mobile atmospheric observatories providing

comprehensive instrument suites across diverse climate regimes "   Manned & unmanned aerial measurement platforms "   Extensive data management infrastructure that has accumulated over

1 petabyte of data to support climate research "   Broad array of freely available data products to support the advancement of

climate research & climate model development "   200 staff spanning 9 DOE laboratories & other institutions "   Work closely with the DOE Atmospheric System Research (ASR) program &

serve the international climate research community

FY18 Science Product PrioriDes

•  Productssuppor8ngLASSO•  Suppor8ngValueAddedProductsforARMMobileFacili8es•  RadarProductsthatmakeradardataaccessibletousers•  ARMProductsformodelers

•  Varia8onalAnalysisbasedForcing•  ARMBestEs8mate•  Diagnos8cs&Metrics

•  Improveddataqualityanduncertainty•  Dataepochs(virtualfieldcampaigns)•  MachineLearning

January31,2018 41

BaselineSurfaceRadiaGonNetworkC.Long

(CourtesyC.Long)

•  BSRNincludes59sta8onswithcontributeddata•  >750sta8on-yearsofobserva8ons•  Dispersedfrom90OSthrough82ON

•  8newsiteshavebeenprovisionallyapproved•  Increasingrecogni8on,use,andscien8ficimpact

Upcomingmee8nginJuly2018(atNCAR)

Neumayer

hfp://bsrn.awi.de/

U. Schneider, A. Becker, A. Meyer-Christoffer, M. Ziese, P. Finger

Deutscher Wetterdienst Department Hydrometeorology

6th GDAP-Meeting, 09-12 Oct. 2017, Boulder, CO, USA

Global Precipitation Climatology Centre

Status report and outlook of the Global Precipitation Climatology Centre

(GPCC)

presented by R.F. Adler (GPCP)

Data base for different GPCC products

MP extended backward to 1982

new data loaded since May 2015

6th GDAP-Meeting, 09-12 Oct. 2017, Boulder, CO, USA

GPCC‘s Precipitation Climatology V.2015 for July on a 0.25° grid

6th GDAP-Meeting, 09-12 Oct. 2017, Boulder, CO, USA

Ø  GPCC is providing the following daily gridded data sets:

Daily precipitation analysis products

•  A First Guess Daily Analysis available within 5 days after the end of the month via internet Period: Jan. 2009 to present Data base: ca. 7,000-8,100 stations described in Schamm et al. (2014)

•  The Full Data Daily Analysis (V.1) updated from time to time Period: Jan. 1988 to 2013 Data base: ca. 10,000-30,000 stations

6th GDAP-Meeting, 09-12 Oct. 2017, Boulder, CO, USA

ASSESSMENTS 1.  Aerosols2.  Soilmoisture3.  Cloud4.  Watervapor5.  Precipita8on(includingtheJointIPWG-GEWEXeffort)6.  TowardsaguideforGEWEXassessment

why new assessments ?

•  forAOD(andsize)fromspace•  MODISisthemainworkhorse

•  twicedailycoverage,NRTavailable•  BUT

•  therearedifferentMODISretrievalsandstar8ngtocombinestrengthsisVERYslow•  whathappens,ifEOSpla�ormfails:aresub-sequentretrievals(e.g.VIIRS)matureenough?

•  cancomplexretrievals(e.g.GRASP)deliverontheirpromise?(e.g.with3MIsensordata)

Aerosols(StephanKinne)

what next •  thepast(s8llnotreleased)assessment

•  agoodreferenceonaerosolremotesensing•  butonlyaddressed1x1griddeddata

•  NOWaddi8onalques8ons•  newsensorsandtheirretrievals•  highertemporalandspa8alevalua8onalsoexamining(provided)retrievaluncertainty

• whohasassessedlately?•  ESA’saerosolCCI…comestoanendsoon•  AeroSATis(atleast)apla�ormfordiscussions•  NickSchutgens:isdoingdetailedvalida8ontoAERONET…invitehimtoafuturemeeNng?+manyothers…somerecentliteraturesummary?

Aerosols(StephanKinne)

Water: Soil Moisture Assessment

AlexanderGruber,WouterDorigo(andmanyothers)

GEWEXGDAP-EEImee8ng

October9–12,2017

NCAR,Boulder,CO

Triple collocaDon analysis •  Doingtriplet-wisedatainter-comparisoninsteadofclassicalpair-wise(e.g.,R,RMSD)allowsforsepara8ngsignal-andrandomerrorcomponents

•  Uncertain8esmaybeexpressedas•  RMSE•  R(w.r.t.truth)•  Signal-to-NoiseRaGo(SNR)

(*)DoesnotresolvesystemaNcerrors!!

51

)Var()Var(log10]dB[SNRi

i εΘ

=

0dB:signal=noisevariance+3dB:signal=doublenoisevariance-3dB:signal=halfnoisevarianceEvery+/-3dBcorrespondstoadoubling/halvingofthera8o[Gruberetal.2016]

Work in progress… •  AValida8onGoodPrac8ceDocument/Protocol

•  AgroupeffortledbyAlexanderGruber•  ini8atedwithinanISSIteamworkshop•  TobesubmifedFebruary2018(attheverylatest)

52

§  ClementAlbergel§  AmenAl-Yaari§  BrianBarreZ§  LucaBrocca§  AndreasColliander§  MichalCosh§  WadeCrow§  RicharddeJeu§  WouterDorigo§  SeyedHamedAlemohammad§  MarNnHirschi§  TomJackson§  AlexandraKonings

§  WilliamLahoz§  KaighinMcColl§  NadineNicolai-Shaw§  RobertParinussa§  ChristophPaulik§  ChiaraPratola§  ChrisRuediger§  SoniaSeneviratne§  Chun-HsuSu§  RobinvanderSchalie§  WolfgangWagner§  SimonZwieback§  (listnotcompleteyet!)

53

ESA Cloud_cci: AVHRR, AATSR, MODIS (Retrieval based on Optimal estimation)

GEWEXCAreference:average(ISCCP,MODIS-ST,PATMOSX,AIRS-LMD)

Cloud_ccidatasetsslightlymisscirrusduringday,beeerduringnight(IRspectrum)Cloud_ccipeakoflowcloudsslightlytoohighCMSAFHIRS-HECTORsimilartoAIRS-CIRSCMSAFAVHRR-CLARA:lesssensiGvetolow-levelclouds

CM SAF: AVHRR-CLARA, HIRS-HECTOR

CloudAssessmentextendedPhase(C.Stubenruach)

54

GEWEXCloudAssessmentdatabase:12 global ‘state of the art’ datasets (in 2008)

Ø jointefforttobuildconsistentdatabase: 1) developing strategy for L2 -> L3 processing (2010 workshop)

2) Iterative process: analyses -> problems -> feedback to teams -> correction by teams some inconsistencies in L2->L3 processing remained; AM-PM definition CALIPSO, MODIS-CE histograms not usable…

UGlityofdatabase:Ø assessmentofnewdatasetsØ climatestudies (CFMIP-OBSdatabaseusedformodelevalua8on)->demandfromuserstoupdatethedatabaseUpdate(same data format, similar website structure in cooperation with AERIS):Ø  atleast7par8cipa8ngdatasets+6newdatasetsØ  1analysis&notetoBAMSwithsummaryØ  Improvementsduetoretrievalproblems,butitlookslikemainconclusionstays

thesame:sensiNvitytocirrusinstrument/retrievalmethoddependentØ  Ancillarydataaffectlow-levelcloudamountØ  NextstepshouldbereallymulN-instrumentretrievalsorintelligentcombinaNon

ofdifferentdatasets/variables

CloudAssessmentextendedPhase

ConDnued Analysis of the GEWEX Cloud Assessment Data

(within the ICWG)AndrewHeidinger

NOAA/NESDIS@CIMSS

MikeFosterMadison,WisconsinUSAMar8nStengel,DWD,Germany

*LeadsoftheICWGClimateGroup55

Next InternaDonal Cloud Working Group (ICWG)

•  Fall2018inMadisonWisconsin

•  HostedbyProfessorRalfBennartzofVanderbilt

•  Allarewelcome.

•  Registra8onpagebeingsetup.

•  Agendaandgroupac8vi8esinfluxandpleasefeelfreetocollaborate.

•  WillhaveIWWGandIPWGpar8cipa8onaswell.

Change in ICWG OrganizaDon 1.  Algorithms(PhilWaZs)

•  ReviewoflatestalgorithmsforgeostaNonaryimagers.•  UseofCombinedSensorsforCloudRetrievals

2.  Assessments(A.Walther)

•  Assessmentoflevel-2CloudParameterRetrievals

•  AssessmentofRetrievalUncertainNes

•  ArcNcCloudDetecNon

3.  ClimateApplica@ons(M.Stengel,/M.Foster)

•  GEWEXCloudAssessmentConNnuaNon(C.Stubenrauch?)

•  Level-3griddingissues

4.  WeatherApplica@ons(Tbd)

•  CloudHeightforWindApplicaNons(withIWWG)

•  SevereWeatherApplicaNons

•  PrecipitaNonApplicaNons(withIPWG)

Wedecidedthe12orsotopicalgroupsatthelastmee8ngwasunsustainable.Wedecidedtoform4permanenttopicalgroupswhicharecomprisedofsub-groupsthatformanddissolveaswarranted.WesuggestmakingatopicalgroupintheClimateApplica8onsgroupdedicatedtothecon8nua8onoftheGEWEXCloudAssessment.Sub-groupsarebeingfinalized.

The Water Vapor Assessment Phase 2 Lead.MSchröder(DWD),Co-leads:H.Brogniez(LATMOS)andB.HO(BCAR)

Climateorientedassessment(stability,absolutevalue,PDF,etc…).Numerouspeer-reviewedpublica8ons.In2017aWCRPreportthatcontainsresultsandrecommenda8onstointerna8onalbodies,spaceagenciesandindividualPIsproducingwatervapourdatarecordsPUBLISHEDAsummaryisinpreparaGonforBAMSThedatafromtheFirstassessmentarealsounderconsideraGonforpublicaccessEUMETSATiscon8nuingitsfundingofthecentraldatabase,workshopsandpresenta8onsatGDAPmee8ngs.

hfp://gewex-vap.org.

Abroad,community-drivenandsustainedassessment3Essen8alClimateVariables(ECV)onwatervapour:

• Totalcolumnwatervapour(TCWV),• Uppertropospherichumidity(UTH),• Watervapourandrelatedtemperatureprofiles

•  Satelliteandreanalysisdatarecords•  OperaGonalsatellitedata•  Ground-based/in-situdatarecords

2016WorkshopatEUMETSATHQ

PrecipitaDon assessment under GDAP •  On-goingGEWEXGDAPprecipassessmentleads:H.Masunaga/C.Kummerow

•  Broadeningtomoreproductsasof2017•  Strongcontribu8ontotheJointIPWG-GEWEXassessment

•  0.5°,3-hourlyrainhistogramforselectedproductsJanuary2015sta8s8cs

Spreadislargerforextremerains(inpar8cularoverocean).

CourtesyMasunaga/FURUZAWA/KUMMEROW

PrecipitaDon assessment under GDAP •  Alotofvariabilityrelatedassessments:exampleEl-Nino

• NINO-3(le�)andNINO-west(right)Pvs.SOICC.

GSMaP CMAPCmorph

GPCP

IMERG

Persiann GSMaP

TMPA

CMAP

Cmorph

GPCP

IMERG

Persiann

Thecorrela8onisintheoryexpectedtobenega8veforNINO3andposi8veforNINO-West.Theresultsarehowevermixed.

TMPA

CourtesyMasunaga/FURUZAWA/KUMMEROW

PrecipitaDon assessment: Joint IPWG-GEWEX effort Leads:Z.HaddadandR.Roca

•  Ini8allytriggeredbyRRinApril2017withasmalldiscussiongroup

•  ExposedtoEuropeanscien8stsinMay2017(EuropeanprecipworkshopinOffenbach)

•  ExposedtoUS/interna8onalscien8stsinOctober2017(duringtheGPMscienceteammee8ng)

•  FirstAssessmentScopingMee8ng+ReportOctober2017(Saturdaymee8nga�ertheGPMscienceteammee8ng)

•  Callforinterna8onalpar8cipa8onNovember2017hep://ipwg.isac.cnr.it/reports/assessment_2018-21.html

# Name Leads Shortdescrip8on1Standardqualityassessment T.KubotaandH.

Masunagacataloguewithsummarydescrip8ons;intercomparisons;regimesortedsta8s8cs;quality&traceability(includingWDACdoc+FIDUCEO)

2Uncertainty J.TurkandP.Kirstefer uncertaintymetrics(detec8on,es8ma8on);intrinsicuncertainty(sensi8vity);algorithmlimita8ons;

3Consistency A.BeranghiandD.B.Shin

waterandenergybudgetsconsistency;regionalbudgets;ancillarydatasets(descrip8onandassessmentforrobustness)

4Evalua8onofanalysisdatafromnumericalmodels

H.J.KimandG.Balsamoperformancemetrics;modelscales(spa8alandtemporal)

5Groundbaseddata C.KiddandS.Durden sources(includingweatherradarwhereavailable);calibra8onanduncertaintycharacteriza8onofsources,includingpolarimetricgroundradars

6Valida8onatweatherscalesinregionswithoutgroundmeasurements

R.Ferraro consistencywithotherremotelysenseddataatweatherscales;consistencywithreanalysis

7Variabilityandtrends F.J.Tapiador sub-seasonal,seasonal,annual,inter-annual;extremesandtheabilitytocapturethemfaithfully;correla8onwithclimateindices;

8Endusersapplica8ons Z.HaddadandG.Huffman

phenomenologicalassessment(consistencywithagriculturalindices,etc);latencyissues;

9Recommenda8onstoalgorithmsdeveloppers

G.HuffmanfandZ.Haddad

assessmentofassump8onsunderlyingthealgorithms,includingretrievalsfromgroundmeasurements(physicalvalida8on);

10Programma8crecommenda8ons G.StephensandV.Levizzani

productsensi8vitytosatelliteconstella8onconfigura8on;sensi8vitytoinstrumentcapabilityandperformance,includingground/airborneinstrumentsproductsensi8vitytosatelliteconstella8onconfigura8on;sensi8vitytoinstrumentcapabilityandperformance,includingground/airborneinstruments

PrecipitaDon assessment: Joint IPWG-GEWEX effort

•  Shortterm(May2018)ongoingac8vity:Thesec8onleadersshouldelaborate3lists:

•  Thefirstlistwouldconsistofaninventoryofexis8nganalysisresults,reports,andpeer-reviewedpapers,thatcanbedrawnuponfortheassessment.

•  Thesecondlistwouldiden8fygapsthatcanrealis8callybeaddressedoverthenextthreeyears(i.e.by2020)byiden8fiedvolunteercontributorswhowillconducttheanalyses.

•  Thethirdlistwouldiden8fyimportantgapsforwhichthereiscurrentlynoclearcontributor.Thiswouldthenbealis8ngofopportuni8esfornewvoluntarycontribu8ons

PrecipitaDon assessment: Joint IPWG-GEWEX effort

SCHEDULE•  February2018:ac8onZiadandRémy::remindertosec8onleaders

•  May2018:(AttheGEWEXConf?Orteleconf)•  consolidateditemslistsfromthesec8onleaders

•  November2018:firstmee8ngatIPWG-9(Seoul)•  toadoptscopesummarydocumentpreparedfromtheelementleadlists

•  October2020:mee8ngatIPWG-10•  toreviewtheassessmentresultsandrecommenda8ons,andstartwri8ngthefinalreport.

OUTREACH

•  withinGEWEXHydrologywithGLAS?ToGAS?•  WithinWCRP:Ac8onGraemeStephens->linkwithChris8anJacob

PrecipitaDon assessment: Joint IPWG-GEWEX effort

ScienDfic Assessment framing under scienDfic big quesDons LiteraGrereview

ProgrammatocrecommendaGon

Variability&trends

consistency Uncertainty Groudnbaseddata

Standardqaulityassessment

Recommendtoprovidersoralgorithms

Status Constella8nosensi8vity

Annualandsubseasonnal

intercomparison Underlyingassump8ons

Wahtwegotfromthepast

Instrumentspec

interannual Assessingtheuncertainty

defini8on

programma8c

Clausiuisclapeyronextreme

Structural(regimesortedsta8s8cs)

Regimesortedsta8s8cs

Surface/insitunetwork

Correla8onwithclimateindices

Quality(tracability)IncludingWADCdoc+FIDUCEO

OSSEdatadenialanalysis

catalogue

Addexemplefromongoinggewexstuff(seafluxweatherregims,watervapor)WriteareportforWCRPwiththepanels

News Items and others

focusontropicalconvec@vesystems&cirrusoriginaNngfromlarge-scaleforcingØ  cloudsystemapproach,anchoredonIRsounderdatahorizontalextent&convec8vecores/cirrusanvil/thincirrusbasedonpcld,εcldØ  explorerelaGonshipsbetween‘proxies’ofconvecGvestrength&anvilsØ  buildsynergeGcdata(vert.dimension,atmosph.environment,temporalres.)Ø  determineheaGngratesofdifferentpartsofUTcloudsystemsØ  followsnapshotsbyLagrangiantransfer->evoluGon&feedbacksØ  invesGgatehowcloudsystemsbehaveinCRMstudies

&inGCMsimulaGons(underdifferentparameterizaNonsof convecNon/detrainment/microphysics)

meeNngs:Nov2015,Apr2016,Mar2017workinggrouplinkscommuniGesfromobserva8ons,radia8vetransfer,transport,process&climatemodelling

PROESUTCC(C.StubenrauchandG.Stephens)

Recent Advances from Satellite Gravimetry and AlDmetry Measurements

•  Sealevel(fromsatelliteal8metry)minusoceanmass(fromspacegravimetry)providesasatellite-basedalterna8vetoArgofores8ma8ngOHC

•  Thecurrentbestes8mateoftheOHCchangefromsatelliteis0.7±0.5Wm-2

over2005-2015•  GDAPendorsesexpansionofthisac8vitytoregionalandshorter8mescalesaspartofanewEEI-themedenergybudgetassessment.

Related AcDviDes •  GEWEXsponsoredorsupporteddatasetsonacommon1∘,3-hourly,equal-areagrid

•  Supportsregionalwaterandenergybudgetclosureanalyses•  UserworkshopinSpainin2019

•  Advanceland-ETandsurfaceradia8onmeasurementsbyexplicitlylinkingtonew/proposedlandsurfacetemperature,soilmoisture,terrestrialwaterstorage,andgroundheatstorageassessmentac8vi8es

•  GrewoutofCLIVARCONCEPT-HEATandNASANEWS•  Integratedassessmentofmethodsforquan8fyingfundamentaldriverofclimateandreconcilingtop-of-atmospherevs.surfaceperspec8ves

GEWEXIntegratedDataset

Earth’sEnergyImbalance

IntegratedSurfaceWaterandEnergyAssessments

ConDnuing a Key Climate Data Record: ISCCP-Next GeneraDon •  Cloudproper8escons8tuteacoregeophysicalclimaterecord•  Instrumentsandexper8seexisttogenerateacalibrated,global,10-channel,mul8-parametercloudat3kmwith30minutecoverage

•  Heritagetodealwithsuchdatavolumesalsoexists(e.g.AIRSandMODIS)•  ExcellentopportunityforcoordinatedNASAandNOAAac8vitytomaximizescien8ficbenefitsofnewgeosta8onaryandlow-earthorbi8ngsatellites

•  GDAPendorsestheforma8onofateamtodevelopaunifiedanalysisapproachbuiltaroundthecurrentgeosta8onaryradiancedatarecordaugmentedbyMODIS/VIIRSandsoundercloudinforma8on

•  Agencysupportforaseriesofinterna8onalworkshops•  Target2021forini8alimplementa8on

•  Amul8-ins8tu8onal(mul8-na8onal)processingchainsimilartoISCCPisencouraged•  Individualsatelliteoperators,collect,qualitycontrol,andsub-sampleradiancesandprovidethesedatatoananalysiscenterthatwouldconductarefinedcalibra8onandthequan8ta8vecloudanalysis.

•  Dataproductstobearchivedanddistributedbyexis8ngdatacenters.