Aerosol, Cloud, Ecosystems (ACE) Final Study Report Silva1247.pdf2015/02/12 · 1 Aerosol, Cloud,...

NASA/TP–20205007337

Aerosol, Cloud, Ecosystems (ACE) Final Study Report

Arlindo M. da Silva, Hal Maring, Felix Seidel, Michael Behrenfeld, Richard Ferrare, and Gerald Mace

September 2020

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September 2020

Arlindo M. da SilvaNASA Goddard Space Flight Center, Greenbelt, Maryland

Hal MaringNASA Headquarters, Washington, DC

Felix SeidelNASA Headquarters, Washington, DC

Michael BehrenfeldOregon State University, Corvallis, Oregon

Richard FerrareNASA Langley Research Center, Hampton, VA

Gerald Mace University of Utah, Salt Lake City, UT

National Aeronautics and Space Administration

Goddard Space Flight Center Greenbelt, Maryland 20771

NASA/TP–20205007337

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ACEScienceStudyTeamArlindoM.daSilva,HalMaring,FelixSeidel

MichaelBehrenfeld,RichardFerrare,andGeraldMace

withcontributionsfromRobertSwap,BrianCairns,DavidDiner,LisaCallahan,ChrisHostetler,RalphKahn,KirkKnobelspiesse,RojMarchand,J.VanderleiMartins,DavidStarr,Matthew

McGill,DerekJ.Posselt,SimoneTanelli,NicholasMeskhidze,JohnE.Yorks,GerardvanHarten,andFengXu

September, 2020

ACE Key Personnel and Affiliations

List of main contributors to the ACE Science Study effort since 2009.

Sub-Teams Name Affiliation Sub-Teams Name Affiliation Management HQ Maring, Hal NASA HQ Clouds

Felix Seidel NASA HQ Theory Jensen, Eric NASA ARC Bontempi, Paula NASA HQ Modeler Stephens, Graeme NASA JPL Turner, Woody NASA HQ Feingold, Graham NOAA/ESRL Neeck, Steve NASA HQ Wu, Dong NASA GSFC

Science Lead da Silva, Arlindo NASA GSFC Marchand, Roger Univ Wash Starr, David NASA GSFC Fridlind, Ann NASA GISS Wu, Dong NASA GSFC Jackson, Gail NASA GSFC (now HQ)

Coordinator Vane, Deb NASA JPL Hou, Arthur NASA GSFC ESTO Famiglietti, Joseph NASA GSFC (ret.) Retrievals Ackerman, Steve Univ Wisc Ocean Biogeochemistry Feng Xu NASA JPL Theory/ Behrenfeld, Mike Oregon State Platnick, Steve NASA GSFC Modeler Boss, Emmanuel Univ Maine Mace, Jay Univ UT

Follows, Mick MIT Haddad, Ziad NASA JPL Siegel, Dave UCSB Radar Im, Eastwood NASA JPL

Retrievals Ahmad, Zia NASA GSFC Heymsfield, Gerry NASA GSFC Wang, Menghua NOAA/NESDIS Racette, Paul NASA GSFC Gordon, Howard Univ Miami Durden, Steve NASA JPL Arnone, Bob NRL Tanelli, Simone NASA JPL Frouin, Robert Scripps

OES Smith, Jay NASA GSFC Aerosols Waluschka, Gene NASA GSFC Theory/ Ferrare, Rich NASA LaRC Wilson, Mark NASA GSFC Modeler Colarco, Pete NASA GSFC Kotecki, Carl NASA GSFC Toon, Brian Univ CO Meister, Gerhard NASA GSFC Reid, Jeff NRL Holmes, Alan NASA GSFC Retrievals Remer, Lorraine NASA GSFC Brown, Steve NASA GSFC Mishchenko, Michael NASA GISS

Cal/Val Hooker, Stan NASA GSFC Kahn, Ralph NASA GSFC Maritorena, Stephane UCSB Hu, Yong NASA GSFC Nelson, Norm UCSB Polarimeter/ Diner, David NASA JPL Stramski, Dariuz Scripps Imager Martins, Vanderlei UMBC Halsey, Kimberly Oregon State Cairns, Brian NASA GISS

Radiation Lidar Yorks, John NASA GSFC Loeb, Norm NASA LaRC Hostetler, Chris NASA LaRC Kato, Sejii NASA LaRC McGill, Matt NASA GSFC Pilewskie, Peter Univ CO Welton, Judd NASA GSFC

Mission Design Winker, David NASA LaRC Callahan, Lisa NASA GSFC Hair, John NASA LaRC Ellis, Armin NASA JPL Cal/Val Starr, David NASA GSFC

Global Modeler Redemann, Jens NASA ARC Ghan, Steve PNNL Knobelspiesse, Kirk NASA GSFC

Aerosol/Ocean Science Saltzman, Eric UC Irvine Mahowald, Natalie Cornell Univ Gasso, Santiago NASA GSFC Meskhidze, Nicholas NC State Gao, Yuan Rutgers Univ

Table of Contents Executive Summary ..................................................................................................... 4 Introduction.................................................................................................................................................................4ScientificMeritandContinuedRelevanceoftheMission........................................................................7TheGoalsofACE........................................................................................................................................................9ExpectedBenefitsofACE....................................................................................................................................10Contributiontolong-termEarthObservationalRecord........................................................................12SynergieswithExistingandPlannedObservationalSystems............................................................12TechnicalReadinessandKeyRisksandRiskReductions.....................................................................12

1 Introduction ............................................................................................................ 15 2 Mission Science Objectives and Measurement Requirements ................................. 18 2.1Aerosols...............................................................................................................................................................182.2Clouds...................................................................................................................................................................212.3Summary.............................................................................................................................................................312.3OceanBiologyandBiogeochemistry......................................................................................................342.4Aerosol-Ocean...................................................................................................................................................38SummaryandRecommendations....................................................................................................................43

3 Assessment and Instrument Concept Development ................................................ 45 3.1Radar.....................................................................................................................................................................453.2Polarimeters......................................................................................................................................................543.3Lidar......................................................................................................................................................................643.4OceanColorSensor.........................................................................................................................................763.5OceanColorValidationSensors................................................................................................................77

4 Measurement Algorithms ....................................................................................... 79 4.1Aerosol.................................................................................................................................................................794.2Clouds...................................................................................................................................................................894.3Ocean....................................................................................................................................................................964.4Aerosol-Ocean.................................................................................................................................................107

5 Field Campaigns .................................................................................................... 109 5.1AerosolRelatedCampaigns......................................................................................................................1095.2CloudRelatedCampaigns..........................................................................................................................1225.3OceanRelatedFieldCampaigns..............................................................................................................133

6 ACE and the 2017 Decadal Survey ......................................................................... 138 6.1AerosolsObservable....................................................................................................................................1396.2Aerosol-OceanEcosystemsSynergisms..............................................................................................1416.3OtherCross-cuttingaspects......................................................................................................................141

7 Programmatic Assessment and Recommendations ............................................... 143

Executive Summary Introduction

FromitsfirstimagesoftheBlueMarbleonthroughtoitsMissiontoPlanetEarth(MTPE)andEarthObservingSystem(EOS),NASAhasforeverchangedhumanunderstandingoftheinterconnectednessandcomplexityoftheEarth’sphysicalandbiologicalsystems.WiththemandatetoadvancetheintellectualfoundationprovidedbyMTPEandEOS,theNationalResearchCouncilconducteditsfirstDecadalSurveyin2007toprovideavisionregardingtheimperativesforearthsystemsscience.WithitsopeningstatementoftheExecutiveSummary,“Understandingthecomplex,changingplanetonwhichwelive,howitsupportslife,andhowhumanactivitiesaffectitsabilitytodosointhefutureisoneofthegreatestintellectualchallengesfacinghumanity,”theDecadalSurveyPanelimparteditsvisionforNASA,NOAAandtheUSGS,avisionsharplyfocusedonincreasinginterdisciplinaryscienceofbiogeophysicalprocessesrelatedtothefunctioningofthecoupledhuman-naturalearthsystem.Asthereportprogressed,amorespecific,intellectualchallengefortheEarthSciencesemerged:howdoaerosol-cloud-ecosystemsandtheirinteractionsmodifythephysicalandbiogeochemicalprocessesoftheearthsystem?

TheearthsystemssciencecommunityinterestedinphysicalattributesoftheradiationbudgethasconvergedaroundthebroadareaofAerosol-CloudInteractionsandtheirimpactsonglobalradiation,hydrologicalandbiogeochemicalsystems.Theopeninglineofthe2013IPCC’sChapter7ExecutiveSummarystatesthat“cloudsandaerosolscontinuetocontributethelargestuncertaintytoestimatesandinterpretationsoftheEarth’schangingenergybudget”(p.573).Theauthorsfurtherassertthat“…untilsub-gridscaleparameterizationsofcloudsandaerosol–cloudinteractionsareabletoaddresstheseissues,modelestimatesofaerosol–cloudinteractionsandtheirradiativeeffectswillcarrylargeuncertainties.”(p.574).TheseunansweredquestionsfromboththedecadalsurveyandthemostrecentIPCCpointtothecontinuedneedforasatellitemissiontoproducethenecessaryobservationstosupportprocessstudiesrequiredtounderstandhowachangingclimateaffectstheroleofaerosolsandcloudsinthetransferandbalanceoftheearth’sradiation,andhowinteractionsbetweenaerosolsandcloudsmodifycloudstemporally,spatiallyandphysicallyfromtheirformationthroughtheirtransitionintoprecipitationsystemsandbeyond.

Inparalleltotheseimportantclimatesystemuncertainties,theearthsystemssciencecommunityinterestedinconsequencesofclimatechangeonthebiospherehasconvergedaroundthebroadareasoftrophicenergytransfer,ecosystemfeedbacks,andbiosphere-atmosphereinteractions.Uncertaintiesinourunderstandingofbiosphericresponsestoclimatechangeareevengreaterthanuncertaintiesinclimatepredictionsduetoaerosol-cloudinteractionsand,critically,itisthesebiosphericresponsesthatmostdirectlyimpacthumanwelfare.Oceanecosystemspresentaparticularlychallengingproblembecausetheyrespond

quickly(orderdays)toclimatefluctuations.Theobservableresponse(e.g.,changeinstandingstock)underrepresentsitssignificancetocarboncycling,andmuchoftheplanktonbiomassoftheglobaloceanexistsbelowthedetectiondepthofheritageoceancolorsensors.Currently,consensushasnotyetbeenreachedon,forexample,thesign(i.e.,increaseordecrease)ofchangeinglobalplanktonproductivityinresponsetoawarmingsurfaceocean.Addressingtheseissuesrequiresglobalsatelliteobservationsfrommultiplesensortechnologiesandinconjunctionwithimprovedcharacterizationsofatmosphericproperties(i.e.,accurateoceanretrievalsrequireaccurateatmosphericcorrections).

Inresponsetothesediverseandinterdisciplinaryquestions,theNRCDecadalSurveyproposedtheAerosol-Cloud-Ecosystem(ACE)missionasaTier2DecadalSurveymissionfocusingonAerosol,Cloudsystems,oceanEcosystems,andtheinteractionsamongthemsoastoreducetheuncertaintyinclimateforcingduetoaerosol-cloudinteractionsandassessmentsofconsequencesforoceanecosystemCO2uptake(NRCDecadalSurvey(2007),pg.4-4).AsoneofitsfifteenrecommendedsatellitemissionsputforwardbytheDecadalSurvey,theACEmissionbringstogetheraerosol,cloud,oceanecosystemandotherearthsystemscientistsinamultiple-sensor,multiple-platform,lowearthorbit,sun-synchronoussatellitemissionthatcombinesactiveandpassivesensorstoobservetheEarthatmicrowave,infrared,visibleandultravioletwavelengths.

ACEhasbuiltuponexperiencegainedfromthecurrentgenerationofEarthobservingsatellitese.g.theNASATerra,Aqua,TRMM,CloudSat,CALIPSO,SeaWIFSandGPMplatforms.Indoingso,theACEmissionhasmadesignificantprogressregardingmissionrequirementsandinstrumenttechnicalreadinessduringitspre-formulationphasebyusingthemissionresourcesandleveragingopportunitieswell.ShouldACEbecomeafully-fledgedfree-flyermission,itwillextendandcomplementsimilarobservationsproducedbytheafternoonconstellation(A-Train)andtheplannedESAEarthCARE(Cloud,AerosolandRadiationExplorer)mission.

ThefundamentalsciencequestionsthatACEintendstoaddresshavenotchangedoverthecourseofpre-formulationactivities,neitherhasourfundamentalapproachtoaddressingthosequestions.Themissioncontinuestofocusonunderstandingphysicalprocessesthatrequiresynergistic,vertically-resolved,activeandpassiveremotesensingmeasurementsforthoseprocessestobediagnosedobservationally.ACEhasandcontinuestoleveragetheadvancesintechnicaldevelopmentandreadinessofbothinstrumentconcepts(withESTOsupport)andtheirrelatedalgorithmdevelopment(withACEDecalSurveyStudysupport).Accordingly,ACEhasinitiatedaseriesofpolarimeterandradarfielddefinitionexperimentsoverthepast3years.ThePolarimeterDefinitionExperiment(PODEX)tookplaceinJanuary-February2013,whilethefirstRadarDefinitionExperiment(RADEX-14)wasexecutedinMay-June2014,withthesecondRADEX-15conductedinNovember-December,2015.Foroceanecosystemscience,ACEpre-formulationhasleveragedseparately-fundedfieldcampaigns(Azores2012,SABOR,NAAMES).ACEleadership

hasalsoinitiatedmonthlyteleconferencesfortheLidarandPolarimetryworkinggroups.

PerhapstheclearestdemonstrationofthescientificrelevanceofACElieswiththesizeablescientificdemandfromthecommunityfortheparticipationofACEscienceteaminaseriesofhighprofilefieldcampaigns(seeTableE.1).ACEscienceandinstrumentteamshavebeenentrepreneurialandsuccessfulintheirleveragingthescientificdemandbythelargercommunityfortheuseoftheirACEinstrumentsimulators.MajorsupportfortheparticipationofACEscientistsandinstrumentteamsinaseriesofhighprofilefieldcampaignsduringthepasteightyearshascomefromavarietyofsourcesfromwithinNASA,andexternalpartnerssuchastheDoE,theNSF,aswellasEuropeansources,e.g.theU.K.AtlanticMeridionalTransect(AMT)Program.

Field Campaign Name Funding Organization

SEAC4RS - Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys

NASA RSP

SABOR - Ship-Aircraft Bio-Optical Research NASA OBB

DISCOVER-AQ - Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality

NASA EVS

NAAMES - North Atlantic Aerosols and Marine Ecosystems Study NASA EVS-2

ORACLES - ObseRvations of Aerosols above CLouds and their intEractionS NASA EVS-2

2012 Azores Campaign NASA AITT, CALIPSO

OLYMPEX - the GPM Olympic Mountain Experiment NASA OBB, ACE, CALIPSO

TCAP - Two-Column Aerosol Project DoE, NASA GPM, ACE, RSP

CHARMS - Combined HSRL and Raman Measurement Study DoE

Table E.1. List of major field campaigns that have utilized ACE-related instrument concepts and related science questions in their observational framework. Responsible funding organizations are also listed.

SeveralACErelatedconcepts,suchasthe,theCloudAerosolTransportSystems(CATS)lidarandtheHyper-AngularRainbowPolarimeter(HARP)haveevendrawn

theattentionandsupportofISSandESTOfundingsourcesenablingtheirdeploymentontheISS(CATSinJanuary2015;HARPschedulefora2016launch).

ThisreportdetailshowtheACEmissionhas,initspre-formulationphase,workedtowardsitsgoalofextendingkeymeasurementsmadebytheaforementionedsensorsthroughitsincorporationofseveralnewairbornesensors,bothpassiveandactive,specifically,amulti-anglepolarimetricimager,ahigh-spectral-resolutionlidarandamultiplefrequencyDopplercloudradar.Theadditionalmeasurementsprovidedbythesenewsensorswillenabledeterminationofpropertiesassociatedwithmanycloud,aerosolandocean-ecosystemsinteractionsthateithercannotbedeterminedfromcurrentsatellitesorcanonlybedeterminedwithlargeuncertaintiestoadvancestateoftheartearthsystemmodels.Examplesofthesepropertiesincludeverticaldistributionsofcloud,precipitationwatercontentandparticlesize,aswellasaerosolnumberconcentrationandsinglescatteringalbedo.Accuratedeterminationofmicrophysicalpropertiessuchastheseiscriticaltoconductingprocessstudiestofurtherourunderstandingofcloud-aerosolinteractionsthatdrivemuchoftheuncertaintyinourunderstandingofclimatechange.Detailsrelatedtothisapproachhaveevolvedoverthepasteightyearswithadvancesinunderstanding,modelingcapabilities,andtechnologyandarepresentedindetailinSections3,4and5ofthisreport.

ScienceTraceabilityMatricesfortheACEmissionarepresentedinmoredetailinSection2andbroadlycoverfiveequally-importantthematicareas:

1) AerosolSources,Processes,TransportsandSinks(SPTS)

2) DirectAerosolRadiativeForcing(DARF)

3) Aerosol-CloudInteractions(ACI);

4) Clouds(Morphology;MicrophysicsandAerosols;Energetics);and

5) Oceans(StandingStocks,CompositionandProductivity(SSCP);BiogeochemicalCycleDynamics;MaterialExchangebetweenAtmosphere/Oceans;ACIimpactsonOceanBiogeochemisty;ImpactsofPhysicalProcessesonOceanBiogeochemistryandOceanBiogeochemistryonPhysicalProcesses;DistributionofHarmfulAlgalBloomsandEutrophicationEvents(HABandEE,respectively).

Scientific Merit and Continued Relevance of the Mission

Callsforthistypeofsciencereachbeyondthe2007DecadalSurveyandtheIPCCandcanbefoundacrossarangeofwhitepapers,proceedings,andthescientificliterature.AgrandchallengeforEarthSystemscienceinthecomingdecadesismovingbeyondsimpleresource-basedviewsofclimateinteractionstowardmechanisticinterpretationsofobservedchangethataddressthecomplexityofnaturalcommunitiesandresolvekeyfeedbackssuchthatthisnewunderstandinginformsandadvancescoupledearthsystemmodels.

Forexample,theWorldClimateResearchProgramhasemphasizedthenecessityofaddressingagrandchallengeassociatedwithobservingandmodelingclouds,circulationsandclimatesensitivityandofworkingacrosstheirnumeroustimeandspacescales(http://www.wcrp-climate.org/gc-clouds;Bonyetal.(2015)).Examplesofthetypesofoutstandingscientificquestionsproducedaspartofthe2014NSF-supportedsynthesisoftheEarthCubeEnd-UserWorkshopseries,the“EngagingtheAtmosphericCloud/Aerosol/CompositionCommunity”workshop1includethefollowing:

1) Whataretheexactrolesofthecloudsinthecloudsystemsandintheentireearthsystem?

2) Howdocloudsaffectthecloudfeedbackonclimatesensitivity?

3) Whatistheroleofcloudsonbiosphereorecosystemsandviceversa?

4) Whatisthespatial,temporal,sizedistributionandcompositiondistributionofaerosolparticlesintheatmosphereandtheaerosolparticleemissionsglobally?

5) Whataretheexactrolesofaerosolsinthecloudandclimate?

6) Whatistheimpactofaerosolonseveremarinestorms?

7) WhatarethechangestoCloudCondensationNuclei(CCN)withchangesinaerosolloading?

Fromthestandpointoftheglobalearthsystemmodelingcommunity,substantialprogressontheaforementionedsciencequestionsnecessitatesataminimumanobservingsystemcapableofprovidingcoincidentaerosol,cloudandprecipitation.

Furtherexamplesofkeyemergentquestionsregardingoceanecosystemchangeare:

1) Howexactlydochangesinupperoceanphysicalproperties(e.g.,temperature,stratification,stormfrequency,surfacemixing)impactplanktonecosystemsandcarbonbiogeochemistry?

2) Howdoaerosolsandcloudsinfluenceoceanecosystemsand,inturn,whatrolestooceanecosystemsplayinaerosolsandclimate?Howdokeymaterialexchangesprocesseschangefromtheland-oceaninterfacetotheopenocean?

1Retrievedfromhttp://earthcube.org/sites/default/files/doc-repository/CombinedSummaries_12Dec2014.pdf,p.70

3) Whataretheimplicationsofglobaloceanecosystemchangeongoodsandservicesforhumanity?Howcanimprovedunderstandinginformimprovedmanagementofoceanresources?

The Goals of ACE

Inordertoaddressanumberoftheaforementionedgrandchallenges,ACEsetouttoassistinreducinguncertaintiesrelatedtoEffectiveRadiativeForcing(ERF)andBiosphericImpactsofClimatebyansweringfundamentalsciencequestionsassociatedwithaerosols,clouds,andoceanecosystems.ACEintendedtoaccomplishthisbymakingimprovedandmorecomprehensivemeasurementsthroughtheuseofinnovativeandadvancedremotesensingtechnologies.AerosolsmeasuredbyACEincludethoseofbothman-madeandnaturalorigins,thelatterofwhichiscontributedsignificantlybyoceanecosystems.

Foraerosols,ACEseekstodistinguishaerosoltypesandassociatedopticalpropertiesandsize.Forcloudsystemsandprocesses,themissionasconceivedwillprovideuniqueinformationthatwillallowfordiagnosisofmicrophysicalprocessesthatcauseclouds,perhapsasmodifiedbyanthropogenicaerosol,toproduceprecipitationwithinturbulentverticalupdrafts.Thisconnectiontoprocesswillbeachievedviamultipleindependentobservationalconstraintsonmicrophysicalpropertieswithintheverticalcolumn.

PlanktonicecosystemsoftheEarth'ssurfaceoceanareacruciallinkintheglobalcarboncycle.Theseecosystemsarehypothesizedtoimpactthecloud,precipitationandclimateprocessesthroughtheirproductivityandtheiremissionoftracegasesthataresubsequentlyconvertedtoaerosols(e.g.MeskhidzeandNenes,2006;KrügerandGraßl,2011).Likewise,thewetanddrydepositionofbiogeochemicallyimportantspeciestotheoceansurfacearehypothesizedtoimpacttheproductivityofthesegloballyimportantecosystems(e.g.Duce,1986;Jickellsetal.,2005;andMeskhidzeetal.,2005).ACEmeasurementswillallowthefirst-everdepth-resolvedcharacterizationofoceanecosystems,includingthestandingstocksofphytoplanktonandtotalparticulatepopulations,ecosystemcomposition,andphotosyntheticcarbonfixation.ACEmeasurementswillfurtherpermitglobalassessmentsofecosystemhealth(throughdiagnosticsofstress),improvedseparationofoptically-activein-waterconstituents,andthefirstdetailedcharacterizationofplanktonannualandinterannualchangesinhigh-latitudepolarregions,whereimpactsofclimatechangehavebeenparticularlysevere.Withtheseadvancedobservations,coupledtotheatmosphericmeasurementsofACE,afarimprovedunderstandingwillbegainedonclimateimpactsonoceanecologyandthegoodsandservicestheyprovide,aswellasfeedbacksbetweenoceanecosystemsandaerosols,clouds,andclimate.

ThespecificgoalsofACEwere:

1. ProvideadatastreamofNear-RealTime(NRT)observationsofhighlyresolvedtemporalandspatialdistributionsofcoincidentaerosols,cloudsandprecipitatingsystemstotheglobalearthobservingmodelingcommunity;

2. ImprovedunderstandingofEarthsysteminteractionsspecificallyamongaerosols,cloud-precipitationsystems,andoceanecosystems;

3. Quantificationofthedirectradiativeeffectofaerosolsatthesurfaceaswellasatthetopoftheatmosphere;

4. Assessmentoftheindirecteffectsofaerosolsthroughmodificationofhydrometeorprofilesincloud-precipitationsystemsandcloudradiativeproperties;

5. Assessmentofchangesincloudpropertiesinresponsetoachangingclimate;

6. Providingthefirst3-dimensionalreconstructionofglobalplanktonecosystemstoimproveunderstandingonhowtheseecosystemsrespondtothe3-dimensionalphysicalandchemicalforcingsthatgovernthem.

7. Provideadatastreamofcoincidentatmosphereandoceanretrievalstoreduceuncertaintiesinallretrievedgeophysicalproducts,betterdistinguishkeyoceanecosystemscomponents,andidentifycriticalocean-atmosphereforcingsandfeedbacks.

8. Observationanddistinguishabilityofthoseoceanecosystemcomponentsthatactivelytakeupand/orstorecarbondioxide;

9. Measurementandquantificationofthelinkagesbetweenatmosphericaerosolsandunderlyingoceanecosystems.

AchievementofthesegoalswillresultinenhancedcapabilitiestoobserveandpredictchangesintheEarth'satmosphere,biosphere,hydrologicalcycleandenergybalanceinresponsetoclimateforcings.

Expected Benefits of ACE

Scientific 1. Reduceduncertaintyinaerosol-cloud-precipitationandradiative

interactionsandtherebyquantificationofthenetroleofaerosolsinclimate.

2. Improvedknowledgeofcloudprocesses,especiallyadvancingknowledgeofthepartitionofliquidandice-phase.

3. Accuratemeasurementscharacterizingthenetradiativeeffectsofmulti-layerclouddecks,especiallylowcloudsinthetropicsandmid-latitudesthatwill

helpclimatemodelersmakemorepreciseandaccuratepredictionsofclimate.

4. Measurementoftheoceanecosystemchangesresultingfromaerosol-cloud-precipitationsysteminteractions.

5. Improvedairqualityforecastingbydeterminingtheheightandspeciationofaerosolsbeingtransportedlongdistances.

6. Leveragedandextendedobservationsfromexistingspace-basedassetscurrentlydeployedbyNASAandourinternationalpartners.

7. Improvedunderstandingoftheimpactsonoceanecosystems,includingtheoceanbiologicalcarbonpump,byatmosphericaerosolsandclouds,aswellasbyclimatechangeatlarge.

Programmatic 1. Establishandincentivizethenextgenerationofearthsystemsciences

throughtheirinvolvementwiththemissionfromundergraduate/graduatestudentsonthroughtoprofessionals.

2. HarnessandleveragetheexpertiseresidentatthreemajorNASAcenters-GoddardSpaceFlightCenter,LangleyResearchCenterandtheJetPropulsionLaboratory.

Societal Relevance 1. Improvedaccuracyofclimateprediction,includingthepredictionofclimate

changeimpactontemperature,precipitationandwateravailabilityresultinginthepossiblereductionofhuman,economicandmarinebiodiversitylossaroundtheworld.

2. Improvementofandextensionofairqualitymonitoringandforecastingonaglobalscale.

3. Improvedpredictionsofpotentialclimatechangeimplicationsonthemarineecosystemplayingavitalroleinhumanwelfare.

4. Improvedunderstandingofthefunctioningoftheremoteregionsoftheworld’soceans.

5. Advancementofearthsystemscienceasameanstoachievingthesegoals,whilenotjustbeinganendinitself.

6. DevelopmentofaNRTcoupledobservation-modelingarchitectureforearthsystemscience.

Contribution to long-term Earth Observational Record

Whilecontributingtothelong-termclimateeffortisalaudablegoal,ACEleadershipismindfulthatprogrammaticresourceconstraintscouldreducetheabilityofthemissiontoprovideanadditionalobservationalcontinuityoverandabovewhatispossiblefromtheoperationalmissionsoftheS-NPP/JPSSandGOESprograms.However,ACEwillcontributebyextendingtheobservationalrecordsofuniqueA-Trainassets(CALIPSO,CloudSat,PARASOL),SeaWIFS.PACE,aswellasEarthCAREandCATS.

Synergies with Existing and Planned Observational Systems

TheACEmissionhaspotentialsynergywiththefollowingactivities:

Solarreflectanceimagery/polarimetry–MissionforClimateandAtmosphericPollution(MCAP):polarimeter,CSAAPOCC(AtmosphericProcessesOfClimateanditsChange)aswellasthe3MIpolarimeterontheEumetsat2ndgenerationpolarsystem(EPS-SG),JPSSmissions,GEOS-Rmissions,MAIA,andACCP.

Precipitation–SnowSat(35/94-GHzDopplercloudradar):CSAAPOCC;AMSR2/GCOM-W2,-W3:JAXA;GPM;ACCP

AtmosphericComposition–GEO:TEMPO,GEO-CAPE,GEMS,SENTINEL-4;LEO:3MI(Meteosat),ACCP

OceanEcosystems-PACE

Other–EarthCare,JPSSS-NPP.

Technical Readiness and Key Risks and Risk Reductions

Technical Readiness TheACEteamhasmadedemonstrableprogressintheevolutionanddeploymentofnewsensortechnology,theacquisition,assimilationandanalysisoftheresultingdataastheconceptsembracedbyACEcontinuetomovefromtechnologydevelopment,tosub-orbitalandeventotheISSontheirwaytoacompletemission.ThisprogresshasbeentheresultofACEleadershipinvestingheavilyoverthepasteightfiscalyearsintwogeneralareas:scienceandriskreduction.Thedevelopmentofsensors,relatedalgorithmsandopportunitiestotestthelargerACEsciencemissionconceptinthefieldhaveoccurredthroughinvolvementofESTOanditsrelatedR&Dprograms,inadesignatedACE-ledfieldcampaign,orbyleveragingpayloaddeploymentopportunitiesrelatedtofundedEVSandR&Afieldcampaigns.

Specifically,thetechnicalreadinesslevelandevolutionofsensortechnologyhasbeenadvancedwithrespecttothedevelopmentofthreepolarimeterconcepts,tworadarconceptsandtwolidarconcepts.Regardingthepolarimeters,theAirMSPIinstrumentTRLiscurrently5withananticipatedincreaseto6byearly2016.The

RSPAPSinstrumentTRLcurrentlystandsat8or9whereasthePACSinstrumentstandsataTRLof6.AdvancesintheACERADconcepthaveseenitsTRLriseto5andisanticipatedtoincreaseto6bytheendof2017.TheTRLoftheLaRCHSRL,currentlystandsbetween4and5andhasflownsuccessfullyonER-2testflightsinMay2015.ThisairborneHSRLisalsoscheduledtobedeployedontheER-2fortheORACLESEV-SmissioninAugust,2016.Additionally,therecentlylaunchedCATSlidarisnowoperationalonboardtheISS.

Technical Risks StartinginFY13,ACEhasincreasinglyprioritizedinvestmentsinriskreduction,specificallyviaalgorithmdevelopmentandthedataacquisitionandanalysestosupportthatactivity.Furthermore,ACEleadershipnowsupportsarobustmulti-sensoralgorithmdevelopmentactivityinthecloudsciencearea.Thisisregardedasacriticalareatoreducetechnicalriskandrapidlyadvancepriormissionformulation,similartoon-goinginvestmentsinaerosolalgorithmdevelopmentbythepolarimeterteams.

ACELeadershiphasalsoconvenedworkinggroupswhereparticipantsfromavarietyofinstrumentconceptteamsarebroughttogetherregularly(onamonthlytobi-monthlybasis)todiscuss,inatransparentforum,advancesandchallengesoftheirconceptasitrelatestothelargerACEmission.ThishasbeensuccessfulwiththePolarimeterandRadarworkinggroups,andmostrecently,withthecreationofaLidarworkinggroup.TheopencompetitionoftheinstrumenttechnologyrelativetoACEmissionobjectivesensuresthedevelopmentandenhancedTRLofmultipleinstrumentdesignstherebyensuringenhancedoptionalityforACEmissionleadershipregardinginstrumentsandtheirdeployment.

Assessment and Recommendation Firstandforemost,thescientificvisionstillstandsandisasmuchindemandnowasitwasin2007.TheACEmissionasfirstconceivedputsforthaboldandambitiousvisionregardingtheobservationandstudyofAerosol-Cloud-Ecosystemprocesses,especiallyitsvisionforseekingtocombinethebestofasurveyingandaprocess-orientedmission.Overthepasteightyears,ACEScienceTeamLeadershiphasactedupontherecommendationsthelastDecadalSurveyandthedirectiveofNASAESDleadershipandmadesignificantprogressduringthepre-formulationstageofthemission.

Furthermore,theACEStudyTeamwasactivelyprovidinginputintotheNationalAcademiesofSciences,EngineeringandMedicine’sSpaceStudiesBoard’s2017DecadalSurveyforEarthScienceandApplicationsfromSpaceprocess.ACEleadershipandScienceTeammembersarepartofthelargerdialoguethatwilldefineNASAEarthSciencemovingforwardandopentoadvancinginthemostparsimoniousfashionpossible.AnumberofwhitepapershavebeencontributedbytheACEStudytorecentRequestforInformationbythe2017-2027DecadalSurveypanelwhereACEsciencequestionsandmeasurementsconceptplayacentralrole.

Inlightoftheaforementionedscientificrelevance,continuedprogressandsuccessinthematurationofinstrumenttechnologyandalgorithmdevelopment,ACEleadershiphasthefollowingrecommendations:

1) Continuetoevolve/maturetheTRLsofpolarimeter,radarandlidarconcepts

2) Continuetoevolve/matureassociatedalgorithms

3) ContinuetoworkcloselywithPACEMissionleadershiptoexploitpointsofintersectionandleveragePACEandACEconceptstoenhancescientificreturnoninvestment.

4) DeveloporextendanexistinganairbornecampaigntojointlyflyACE-relatedlidarandpolarimeterconceptsonboardtheNASAER-2suborbitalplatformstotestandrefinecombinedactive-passiveaerosolandcloudretrievalalgorithms.

5) ProgresstheACEMissionfrompre-formulationtoformulationphaseinanadaptivefashioninharmonywiththerecommendationsof2017DS.

1 Introduction OneofthemostpressingcontemporaryEarthSystemSciencequestionsis,incontrovertibly,howwilllifeonEarthrespondtoclimatechangeoverthecomingcentury?Globalsatellitemeasurementsalreadyprovideamongthegreatestinsightsintothisquestionbyobservinghowtoday’soceanandterrestrialecosystemsrespondtonatural,andtosomeextentanthropogenicformsofclimatevariation.However,newandinnovativemeasurementapproachesarerequiredtoadvanceourunderstandingofthelivingEarthSystem.Currentlimitationsareparticularlyacuteforstudiesofoceanbiology,fordirectaerosolclimateforcing,forcloud-aerosolinteractions,andforprecipitation-producingprocesses.Forexample,NASA’soceancolormissionsfailtoobservehigh-latitudeecosystemsovermuchoftheannualcycle,yettheseclimate-criticalecosystemsareexperiencingthegreatestrateofclimate-drivenchange.Furthermore,heritageoceancolorsensorsonlydetecttheplanktonpropertiesinathinlayeroftheocean’ssurface,leavingmajoruncertaintiesinourunderstandingofoceanproductivity,biomassdistributions,andinteractionsbetweenbiologicalstocksandrates,andrelatedphysicalforcingsthatwillbestronglyalteredbyachangingclimate.

WithinthisgrandEarthSystemScienceChallengeofunderstandinghowthebiospherewillrespondtoclimatechangearetwoprimarysub-questions:(1)Howwilltheseresponsesofthebiospherefeedbackonatmosphericfactorscontrollingclimate?and(2)Towhatextentandwherewillchangesinclimateforcingimpactthephysicalenvironmentinwhichthebiosphereexists?Withrespecttothislattersub-question,oneparticularuncertaintysupersedesallothers:aerosol-cloudinteractionsandtheimpactofcloudsandaerosolsonglobalradiation,hydrological,andbiogeochemicalsystems.Indeed,theExecutiveSummaryofChapter7inthe2013IPCC’sstatesthat“cloudsandaerosolscontinuetocontributethelargestuncertaintytoestimatesandinterpretationsoftheEarth’schangingenergybudget”(p.573).Theunderlyingissuesarefurtherclarifiedbynotingthat“…untilsub-gridscaleparameterizationsofcloudsandaerosol–cloudinteractionsareabletoaddresstheseissues,modelestimatesofaerosol–cloudinteractionsandtheirradiativeeffectswillcarrylargeuncertainties.”(p.574).Itisalsowidelyrecognizedthatthetreatmentofmeteorologicalinfluencesoncloudsandaerosolsisanequallyimportantsubjectthatneedstobeconcurrentlyaddressed.

TheseoutstandingissuesfromthemostrecentIPCCassessmentpointtoaseriesofunansweredquestionsregardingtherolesofaerosol,clouds,andprecipitationinEarth’schangingclimatesystem.Thesequestionshighlightthecontinuedneedforglobalobservationsallowingprocessstudiesaddressinghowthetransferandbalanceofenergyinachangingclimateareinfluencedbyaerosols,clouds,andprecipitation,andhowtheinteractionsbetweenaerosolsandcloudsfromtheirformationthroughtheirtransitionintoprecipitationsystemsinfluencetheresponseoftheEarthsystemtoarapidlychangingatmosphereandoceancomposition.Thus,tofullyunderstandthethreatthatclimatechangeposestolifeonEarthinaquantitativemanner,itisessentialtorelateobservedchangesinthecontemporary

biospheretothemagnitudeoffuturechange,whichinturnrequiresprocess-levelunderstandingofbiologicalfeedbacksonclimatealongwiththedetailsofaerosol-cloudandotherinteractionsofthephysicalclimatesystem.

InresponsetoasimilarsetofquestionsposedbytheEarthSciencecommunity,andrecognizingthescientificandobservationaloverlapsinoceanecosystemandatmosphericsciences,the2007theNRCDecadalSurveyrecommendedtheAerosol-Cloud-Ecosystem(ACE)mission.Atthetime,ACEwasrecommendedasaTier2,pre-formulationmissionfocusingonobservationalrequirementstoadvanceunderstandingofoceanecosystems,aerosols,andcloudsandtheirinteractionsandfeedbacks.(NRCDecadalSurvey,2007,pg.4-4).Asoneofitsfifteenrecommendedsatellitemissions,ACErepresentstheprimaryglobalmissiontoadvanceunderstandingoftheclimate-biospheresystem.Itbringstogetherecosystem,aerosol,cloud,andotherEarthsystemscientistsinamultiple-sensor,multiple-platform,lowsun-synchronoussatellitemission.Therecommendationstressesthattoachievemissionobjectivesactive(primarilylidarsandradars)andpassivesensorsneedtobecombinedtoobservetheEarthatmicrowave,infrared,visibleandultravioletwavelengths.

ThefundamentalsciencequestionsthatACEaddresses,andthefundamentalapproachtoaddressingthosequestions,haveonlycomeintosharperfocusoverthecourseofthepre-formulationactivities.Themissionconceptcontinuestotargetcollectingsynergisticactiveandpassivemeasurementsthatwillaidunderstandingofoceanbiologicalstocks,rates,andchangesfrompole-to-poleandfromthesurfacetodeepcommunities,alongwiththephysicalprocessesassociatedwiththeEarth’swaterandenergycycles.ACEactivitiesinvolveparticipationfromabroadsegmentoftheEarthSciencecommunity,inparticularfromtheoceanecologyandbiogeochemistry,aerosol,cloud,precipitation,andradiationdisciplines.

SincetheACEmissionrecommendationbythe2007DecadalSurveyReport,pre-formulationactivitieshavemademajoradvancestowardrefiningitsobservationalandsciencerequirements.Thesedevelopmentshaveresultedinseveralreports.Mostrecently,theoceansciencecommunityhasproducedaverydetaileddescriptionofrequirementsfortheACEadvancedoceancolorsensoraspartofthePre-ACE(PACE)ScienceDefinitionTeamactivities;thePACEScienceDefinitionTeamReportisavailablefromhttp://decadal.gsfc.nasa.gov/pace-resources.html.Inaddition,guidanceonnumerousACE-relevantobjectiveswereprovidedinarecentNASASMDcommunitymeeting(May,2014,NASAAmesResearchCenter);recommendationsfromthisworkshopwerepublishedinareportentitled“OutstandingQuestionsinAtmosphericComposition,Chemistry,Dynamics,andRadiationfortheComingDecade”,availablefromhttps://espo.nasa.gov/home/content/NASA_SMD_Workshop.Theradiation,aerosols,clouds,andconvectionssectionsofthatreporthighlightquestionsandpossibleobservationalcoursesofactionthatpertaintotherolesofaerosols,clouds,precipitationintheclimatesystem.Thenovelobservationalapproachesattendto

significantshortcomingsinourpresentobservationalsystemsfortacklingthegrandchallengesinEarthscienceforthenextdecade.

IncompliancewithguidancereceivedfromtheAssociateDirectorofFlightProgramsintheEarthScienceDivisionoftheScienceMissionDirectoratebyeach2007DecadalSurveyMissionTeam,theACEScienceTeamhasproducedthepresentdocumentthatsummarizestheresultsofthepasteightyears(2011-2018)ofpre-formulationworkaccomplishedbytheACEmissionteam.Thepaperdetailstheefforts,accomplishmentsandplansoftheACEmissionteamforthefollowingaspects:Instrumentconceptdevelopmentandassessment;measurementalgorithms;fieldcampaigns;missionarchitecture;andmissionfundinghistory.Further,theACEmissionteamprovidedanoverallassessmentaswellasitsownrecommendationsregardingthefutureoftheACEmission.

ThisReportisstructuredinthefollowingmainsections:

1. Introduction

2. MissionScienceObjectivesandMeasurementRequirementsintheformatofScienceTraceabilityMatrices

3. AssessmentandInstrumentConceptDevelopmentfortheradar,polarimeter,lidarandoceancolorinstrumentconcepts

4. MeasurementAlgorithmsforaerosols,cloudsandoceans

5. FieldCampaignsforaerosol,cloudandoceanrelatedcampaigns

6. ACEandthe2017DecadalSurvey

7. ProgrammaticAssessmentandRecommendations

Thesesectionsarefollowedbysectionscontainingreferencesandlistofacronyms.

2 Mission Science Objectives and Measurement Requirements

2.1 Aerosols

Globalmeasurementsofthehorizontalandverticaldistributionsofaerosols,andtheiroptical,microphysical,andchemicalpropertiesarerequiredtoquantifytheimpactsofaerosolsonhumanhealth,globalandregionalclimate,cloudsandprecipitation,andoceanecosystems.AlthoughspaceborneinstrumentsontheAqua,AuraandTerrasatelliteshavesignificantlyimprovedourglobalunderstandingofaerosols,criticalmeasurementsareeitherabsentorhaveunacceptablylargeuncertainties.Therefore,theACEaerosolScienceTraceabilityMatrix(STM)hasbeendesignedtoaddressobjectivesthataresignificantlybeyondthecapabilitiesofcurrentsatellitesensors.

FollowingthereleaseoftheNASreport,workbeganduring2007and2008onthedevelopmentofawhitepaperandSTMtocapturethespecificaerosol-relatedsciencequestions,aerosolandcloudparametersrequired,andmeasurementandmissionrequirements.AmeetingofNASAaerosolscientistswasheldatGSFCinFebruary2009toacceleratedevelopmentoftheaerosolSTM,includingrequirementsfortwocoreaerosol-relatedinstruments:polarimeterandlidar.Followingthismeeting,furtherdiscussionandrevisionsoftheSTMwerefacilitatedbyregulartelecons.ArevisedversionoftheSTMwaspresentedforcommentatameetingheldinSantaFe,NMinAugust2009.Basedoncommentsreceivedatthismeeting,theSTMwasrevisedfurther.Mostnotably,increasingtheemphasisonthecloud-aerosolinteractions(CAI).

TheaerosolSTMaddressesthreemajorsciencethemes:1)sources,processes,transportandsinks(SPTS);2)directradiativeaerosolforcing(DARF);and3)cloud-aerosolinteractions(CAI).Thefirstthemeaddressestheglobalaerosolbudget,long-rangetransport,andairquality.Comparisonsamongcurrentglobalaerosolchemicaltransportmodelsreveallargediversityinthemodeleddistributionandattributionofaerosolspecies,whichindicatessignificantuncertaintiesinmodelchemicalevolution,microphysics,transport,anddeposition,aswellassourcestrengthandlocation.Asmodelsbecomemoreadvancedandsimulateaerosolmass,number,andsizeformultipleaerosoltypesandmodes,aerosolcharacterizationrequiresadditionalmeasurementsbeyondtotalcolumnaerosolopticaldepth(AOD).Consequently,theACEapproachistoprovidemeasurementstopermitimprovedestimatesofaerosolsourcestrengthandlocation,verticaldistribution,anddistributionsofaerosolopticalproperties,mass,number,andcomposition.TherequiredparametersincludeverticallyresolvedmicrophysicalpropertiestotranslateretrievedAODandaerosoltypetomass,numberconcentration,andsizedistributionandtopartitionthetransportedaerosolintodifferentaerosolcomponents.

ThesecondthemeaddressedbytheSTMisthedirectradiativeaerosolforcing(DARF).HereACEaimstoprovidefirm,observationally-basedestimatesofDARFanditsuncertaintieswiththeultimategoalofbetterconstrainingfutureclimatepredictionsofDARF.ACEgoesbeyondaddressingtopofatmosphere(TOA)radiativeforcingbyprovidingglobalestimatesofsurfaceandwithinatmosphere,verticallyresolvedradiativeforcing;thelatterisespeciallyimportantforrepresentinghowatmosphericheatingbyabsorbingaerosolsaffectsonclouddevelopmentandprecipitation.Inordertoderivewithin-atmosphereDARF,akeyACEobjectiveisforthefirsttimetoprovidelayer-resolvedmeasurementsofaerosolabsorptionfromspace.HereACEgoeswellbeyondcurrentsatellitemeasurementcapabilitiesandprovideaglobal,comprehensivedatasetofthreedimensionalaerosolpropertiestoconstrainaerosoltransportmodelestimatesofgloballyaveragedDARFwithintheatmosphereandatthetopandbottomboundaries.

Thethirdthemeistoaddresstheinteractionsbetweenaerosolsandclouds.Theseinteractionsincludetheimpactsofaerosolsoncloudmicro-andmacrophysicalproperties,andthedegreetowhichcloudsandprecipitationimpactaerosolconcentrations.TheACEsatellitemeasurementsdescribedintheSTMareintendedtoprovidestrongconstraintsonthesensitivityofcloudradiativeforcingandprecipitationtoaerosolnumberdensity,verticaldistribution,andopticalproperties(e.g.,absorption).ACEmeasurementsareintendedtoconstrainmodelrepresentationsofcloudmicrophysicalandopticalpropertiesandmodelsimulationsofCloudCondensationNucleiCCNamountandaerosolabsorptionnearcloudsbyprovidingobservationaltargetsthatarecomprehensivelycharacterized.Hereagain,thedetailed,verticallyresolvedmeasurementsofaerosolopticalandmicrophysicalpropertiesfromACEgowellbeyondthecurrentsatellitemeasurementsoftotalcolumnaerosolmeasurements.TheultimategoalistoassimilateACEmeasurementsintoadvancedearthsystemmodelsrepresentingaerosolandcloudmicrophysicalprocesses,extendingtheinformationcontentofthemeasurementstoconditionsnotdirectlyobservedbysatellites(e.g.,underclouds).

Ingeneral,thegeophysicalparametersrequiredforthethreemajorthemesaresimilar.TheparameterslistedintheSTMareneededtocharacterizetheopticalandphysicalcharacteristicsoftheaerosoltospecifiedaccuracies,withacombinationofsatelliteandsuborbitalmeasurements.Therequiredaerosolcharacteristicsincludespectralopticalthickness,spectralsinglescatteringalbedo,spectralphasefunction,andcomposition.Theseparametersareretrievedfromthesatellitemeasurementsorderivedfromotherparameters(e.g.,sizedistribution,refractiveindex,nonsphericity)retrievedfromthesatellitemeasurements.Inthecaseofdirectradiativeforcingandaerosol-cloudinteractionthemes,layer-resolvedaerosoloptical(scattering,absorption)andmicrophysical(e.g.,effectiveradius,nonsphericity)propertiesarealsorequired.Therequiredspatialcoverageforthesemeasurementsvarieswithobjective.Forexample,whileresolvingglobalmonthlymeantrendsinAODanddetectingdecadalscaletrendsatcontinentalscalescan

likelybeaccomplishedusingnarrowswathmeasurements,widerswathswilllikelyberequiredtoreducetheuncertaintiesatregionalandseasonalscales.FurtherstudiesregardingsamplingshouldfocusontheimpactofmeasurementsonaerosolradiativeforcingandaddressaerosolabsorptionaswellasAOD,andconsiderdataassimilationasatoolforextendingtheusefulnessofthedata.Aerosolparticlenumberconcentrationisanadditionalparameterrequiredtospecificallyaddressaerosol-cloudinteractions.

TheACEmeasurementrequirementsadvancesthestate-of-theartofcloudandaerosolmeasurementsandthereforearetechnologicallyambitious.EvenwiththeseACEmeasurements,thereareimportantaerosolmeasurementsthatcannotbeachievedfromspace.Forexample,particlewateruptake(hygroscopicity),requiredtoaccountforhumidity-dependentparticleopticalpropertychangesaswellasparticleactivationconditionsthatinitiatecloudformation,cannotbederivedfromremote-sensingobservations.Similarly,insitumeasurementsarerequiredtoobtainaerosolMassExtinctionEfficiency(MEE),neededtotranslatebetweenremote-sensing-derivedparticleopticalpropertiesandaerosolmass,whichisthefundamentalquantitytrackedinaerosol-transportandclimatemodels.Anditisnotclearhowadequatelyevenadvancedremote-sensinginstrumentswillconstrainparticlespectrallightabsorptionproperties,akeytosimulatingatmosphericheatingprofiles,cloudevolution,especiallyinpollutedorsmokyenvironments,distinguishinganthropogenicfromnaturalparticles,andassessingbroaderaerosol-climateeffects.Astherearealwaysgapsinmeasurementspatialandtemporalcoverage,andvariationsindataquality,modelingprovidestheinformedinterpolation,extrapolation,andpredictionrequiredtocompletethepicture.Therefore,suborbitalmeasurements,includingsystematicmeasurementsofparticlemicrophysicalandchemicalproperties(e.g.,Kahnetal.,2017),andastrongmodelingcomponent,arecriticaltoaddresstheACEaerosolscienceobjectivesaswellastovalidatetheACEsatellitemeasurements.TheACEaerosolSTMcallsforacombinationofsatelliteandsuborbitalmeasurements,combinedwithacomprehensivedataassimilationcomponent,toadvancethecloud/aerosolscienceandenableanadvancedclimatepredictioncapability,withreduceduncertainties.

2.2 Clouds Amongotherobjectives,theACEmeasurementsuitewasdesignedtobetterconstrainthecharacterizationofcloudandprecipitationmicrophysicalproperties.Understandingcloudandprecipitationmicrophysicalpropertiesiscriticaltoimprovingtherepresentationofmanyphysicalprocessesinclimatemodels,whicharethemselvespoorlyconstrainedatpresent.Uncertaintiesinthecouplingbetweenmicrophysicalprocessesandatmosphericmotionsaretheunderlyingcauseofthelargespreadincloudfeedbacksandclimatechangeuncertaintyintoday’sclimatemodels(Knuttietal.,2013;Kleinetal.2013;StevensandBony,2013).Tomeetthisobjective,theACEwhitepaperthatwascompletedin2010andupdatedin2014identifiedadiversityofmeasurementsandsensorsthat,whencombinedsynergistically(Posseltetal2016;Maceetal.,2016,MaceandBenson.,2016),

wouldprovideindependent,verticallyresolved,andverticallyintegratedconstraintsonnear-cloud-aerosols,cloud/precipitationparticle-size-distributions(PSDs),andcloud-scaleverticalmotion.

ACEcloudscienceobjectivesweredirectedatunderstandingmicrophysicalprocessesthattakeplaceintheverticalcolumnthatconvertaerosolparticlestoclouddropletsandtosnowflakesandraindropletswithinturbulentverticalmotionsinclouds.Understandingtheseprocessescontinuetobethelimitingfactorinsimulatingthewatercycleintheatmosphere(Stephens,2005;StevensandBony,2013).Putanotherway,themicrophysical/dynamicalprocessesthatdrivetheaerosolindirecteffectsandcloud-precipitationmicrophysicalprocessesingeneral,especiallythosethatinvolvetheicephase,continuetobethemajorsciencemotivationofACEclouds;andallsciencequestionscontinuetobederivedfromthismotivation.

Cloudsandassociatedprecipitationhavelongbeenknowntobeintegralcomponentsoftheplanetaryenergybalance,accountingforalmosthalfofEarth’splanetaryalbedo(e.g.,Stephensetal.2012).Changesinthestatisticsofglobalcloudpropertiesinresponsetowarmingremainthelargestuncertaintyinaccuratelyprojectingthefutureclimateresponsetoanthropogenicforcing(SodenandHeld2006).Thefeedbacksduetochangesincloudsandprecipitationremainthesinglegreatestsourceofspreadingeneralcirculationmodel(GCM)estimatesofglobalclimatesensitivity(Kleinetal.,2013;BonyandDufresne2005;Zelinkaetal.2012,2013).

The ACE Clouds Science Traceability Matrix Inthissectionwediscussthecurrentstateofmodelingandobservationofcloudsandposequestionsthatmightbeaddressedbyfutureobservingplatforms,includingbothsatellitemissionsandfieldexperiments.Ourfocushereisonthethermodynamic-dynamic-microphysical-radiativeprocesscouplingthatcontrolstheoccurrenceofcloudsandtheirarealcoveragewhenpresentandthusdeterminestheirfeedbacksunderclimatechange.However,thereisalwayssufficientaerosoltonucleateliquid-phaseclouds,andthusindirecteffectsonlybecomerelevantafterthedynamicsandthermodynamicsinitiatescloudformation.Thisdiffersmarkedly

TheACEcloudsciencerequirementsandtheimperativeformulti-sensorsynergytomeetthoserequirementshavenotchangedsincetheoriginalwhitepaperwascompletedin2010.TheACEteamsoughttodevelopacoherentandachievablestrategyforaccomplishingthesciencegoalsofACEusinginnovativeapproachesthatprovidetherequiredmeasurementsynergyinthemostefficientandcosteffectivemeanspossible.Changesinthemeasurementrequirementssincetheinitial2010reportweredocumentedinthe2014updateduetoadvancementsinbothtechnologyanddataprocessing.Theseadvancementswouldhaveallowedustoextractmoreinformationfromafocusedandstreamlinedsetofmeasurements.

fromthesituationforcirruswherenucleationitselfispoorlyunderstoodbecausetheconcentrationandpropertiesofnucleatingaerosolsintheuppertroposphereispoorlyknown.Indeed,thedocumentedcompensatingforcingandfeedbackerrorsthatallowmanyGCMstocorrectlysimulatethe20thCenturytemperaturerecord(Kiehl2007;Forsteretal.2013)canbethoughtofastwosidesofthecloudproblem–forcinguncertaintyduetoaerosolindirecteffectsandfeedbackuncertaintyduetodynamicandthermodynamicprocessesandtheirinteractionwithradiation.

Ithasbecomeclearthatthereisanaturalbreakinmeasurementstrategybetweenshallowcloudsthatcanbestronglyinfluencedbyaerosolanddeepercloudsystemswhere“nearby”aerosolscan'tbeobservedandwhereicemicrophysicstendtobeimportant.Accordingly,ACEcloudsciencequestionsdividenaturallyintoaerosol-cloud,cloud-radiationandcloud-precipitationthemesaccordingtothemeasurementsneededtoaddressthosequestions.ACEscienceobjectivesarefocusedonmicrophysicalprocessesthattakeplaceintheverticalcolumn,convertingaerosolparticlestoclouddropletsandtosnowflakes,icecrystals,andraindropletswithinturbulentverticalmotions.Understandingtheseprocessescontinuestobethelimitingfactorinsimulatingthewatercycleintheatmosphere.Themicrophysicalanddynamicalprocessesthatdrivethe1stand2ndaerosolindirecteffectsandcloud-precipitationmicrophysicalprocessescontinuetobethemajorsciencemotivationofACEclouds.Ofspecialinterestandimportancearequestionsthatinvolveicephaseprocesses.

Withanaturalbreakinmeasurementstrategyoccurringbetweenshallowcloudsthatcanbestronglyinfluencedbyaerosol(1stand2ndindirecteffects),anddeepercloudsystemswhere“nearby”aerosolscannotbeobservedandwhereicemicrophysicstendtobeimportant,ACEsciencequestionsweredividedalongaerosol-cloudandcloud-precipitationthemesaccordingtothemeasurementsneededtoaddressthem.Inthefollowingparagraphs,wediscussissuesthatcutacrossbothclassesofquestionsandbroadpriorities,thenweconsiderhowmeasurementneedsdifferbetweenaerosol-cloud,cloud-precipitationandcirrusclouds.AsimplifiedScienceTraceabilityMatrixispresentedfollowingthisdiscussion.Throughoutthetext,wepayspecialattentiontotheevolutioninourthinkingthathasinfluencedthefinalrevisedSTMandoverallmissionstrategy.

TheACECloudsSTMisconstructedaroundtherealizationthattheprocessesthatcoupleatmosphericmotionstocloudandprecipitationprocessesarethefundamentalissuesthatunderpinuncertaintyinclimateprediction(BonyandDufresne,2005;DufresneandBony,2008;StevensandBony,2013).Whilethedetailsoftheseprocessesvaryacrosscloudgenre(i.e.cumulus,stratocumulus,cirrus,altostratus,etc.),adistinctneedforfurtheringourunderstandingoftheseprocessesisquiteindependentofcloudtypeandourfinalSTM(Table2.2),therefore,utilizesageneralframeworkindependentofcloudtypebutfocusedontheaerosol-cloud-precipitationnexus.

TherevisedACEcloudsSTMisorganizedaroundoverarchingscientificthemes(left-mostcolumn).Thesethemesrangefromhowtheoverallthree-dimensionaldistributionofcloudsandprecipitationmaybechangingtowhatthedistributionsofcloudandprecipitationmicrophysicalpropertiesare.Asmentioned,weareparticularlyinterestedintheprocessesthatcausepopulationsofclouddropletsandicecrystalstoevolveintoprecipitation–inparticularhowaerosolandatmosphericmotionsmodulateandfeedbackontheseprocesses(i.e.MaceandAbernathy,2016;MaceandAvey,2016).Ultimately,whatwelearnfromACEmeasurementsandassociatedmodelingstudieswillhelpustounderstandbettertheenergeticsoftheearth’satmosphereorjusthowcloudsandprecipitationparticipateinthepolewardtransportofenergyandhowthatmaybechangingastheclimatesystemevolves.

AddressingthesethemesultimatelycomesdowntosciencequestionsforwhichrigorousanswerscanbeformulatedfromACEmeasurements.Wepresent4broadcategoriesofquestionsthataredrawnfroma2014communitywhitepaperentitledOutstandingQuestionsinAtmosphericComposition,Chemistry,DynamicsandRadiationfortheComingDecade,availablefrom:

https://espo.nasa.gov/home/content/NASA_SMD_Workshop

Thesequestionsarefocusedontheroleofcloudsandaerosolinunderstandingclimatesensitivity,changestoshortwaveandlongwaveclimateforcing,andtheprocessesthatcontrolthewatercycleandenergeticsoftheclimatesystem.Acarefulreadingoftheclouds,radiation,aerosol,andconvectionsectionsoftheAmes2014whitepaper,wherethesequestionsarediscussedindetail,suggeststhatanswerstothemrestonbetterobservationsofcloudandprecipitationmicrophysics,cloud-scaleverticalmotion,andaerosolmicrophysics.

WhatgeophysicalparametersareneededtoaddresstheACEsciencequestionsarelistedintheSTMandreferencedbacktothesciencequestionsandthemes.Broadly,thesegeophysicalparametersincludecloudandprecipitationmicrophysics,verticalmotion,aerosol,andradiativepropertiesthatwillallowustoderiveheatingrateprofilesaswellastopofatmosphereandsurfaceradiativebudgets.Usingitalicizedandboldfonts,wesuggestthenotionaltradesthatwouldoccurifathresholdsetofinstrumentswereusedinsteadofamoreaggressivebaselinesetofinstruments.Thethresholdsetofmeasurementswillallowustoretrievegeophysicalparametersthataddressmanyifnotmostofthesciencequestionswhilethebaselinemissionwouldallowformoreaccurategeophysicalparameterretrievalsoverabroaderrangeofconditions.

AcenterpieceoftheACEinstrumentsuiteisadualfrequencyDopplercloudradarthatwilloperateintheKaandWbands.TheACEradarcombinestheCloudSatandGPMcapabilitiesandgoeswellbeyondwhateitherofthoseinstrumentscouldaccomplishscientifically.Thisradarwillalsoincludepassiveradiometercapabilitiesthatallowforpassivemicrowavemeasurementsatleastalongthenadirtrackthatwillenableaccurateretrievalsofcloudandprecipitationpropertiesinoptically

deepcloudsystemssuchasfrontsandshallowconvection.Additionally,radarandmicrowaveretrievalsofmanycloudtypesbenefitgreatlybyknowingthevisibleandnearinfraredreflectancesbecausetheyconstrainclouddropletpropertiesintheupperportionsofmanycloudtypeswheretheradarandmicrowaveretrievalsarechallengedbythesmalldropletsizes.CombinedwithaHighSpectralResolutionLidar,thevisiblemeasurementswillprovidethresholdconstraintsonthesurroundingaerosol.Whilethissetofmeasurementsisherecharacterizedasathresholdorminimumset,wemustnotethatthisminimumsetgoeswellbeyondthecapabilitiesoftheA-Train,GPM,orEarthCareandwouldallowforsignificantadvancesinourunderstandingofcloudandprecipitationprocesses.

Thebaselinesetofinstrumentsincludesvariousoptionseachofwhichwillincrementallyeitherenhancetheaccuracyofretrievedgeophysicalparametersorbroadenofthescopeofthecloudtypeswecanaddress.(Table2describeswhatmeasurementsconstrainspecificaspectsofcloudandprecipitationmicrophysics)Forinstance,addingathirdfrequencytotheACEradarallowsforprobingdeeperprecipitatingsystemssuchasheavilyrainingconvectionandfrontalsystems.AddingapolarimeterandanHSRLlidarwillenhancesomecloudretrievalsbutwillprimarilybenefitourunderstandingofthenear-cloudaerosolpropertiesthatareacriticalaspectofmanyofoursciencequestions.Additionalpassiveconstraintsprovidedbyhighermicrowavefrequenciesorsub-millimeterradiometerswouldenhanceice-phaseprecipitationretrievalsindeeperconvectivesystemsandallowformoreaccuratecharacterizationofhighlatitudesnowfall.

FurtherDiscussionandJustificationoftheACECloudsScienceTraceabilityMatrixRapidimprovementsincomputingpowerisallowingglobalmodelstoapproachthecloudresolvingscale(Myamotoetal.,2013;Satohetal.,2008)whereconvectiveprocessescanberesolvedandtheneedforconvectiveparameterizationsarediminished(Larsonetal.,2012).WhileitwillbesomeyearsbeforeglobalCloudResolvingModels(CRMs)canbeusedastrueclimatemodels,CRMsarestillconsideredthetoolbywhichtraditionalcoarseresolutionclimatemodelscanbeimprovedandthisimprovementwillcomethroughstatisticalrepresentationsofcloudmicrophysicsontheGCMgridscale.Therefore,understandingmicrophysicalprocessesgloballyisrelevantnowandwillbeincreasinglyimportantaswemovethroughthe2020’s.TheACEquestions,therefore,focusedonsmallspatialscales(~100’sofm)andfinelyresolvedverticalscales(~10’sofm)thataretypicalofCRMorLargeEddySimulations(LES).Inshort,high-resolution(~100to500mscale)observationsofmicrophysicalprocesseswereconsideredtobecritical.Likewise,sinceourtargettheoreticalaudienceistheCRM/LEScommunitieswherehigh-resolutioncloudmeasurementscanbeassimilateddirectly,wesoughtmeasurementsthatwouldcoveraswaththatisseveral10'sto100kmwidealongasuborbitaltrack.

Intermsofpassivemicrowavemeasurements,itwasthoughtthatmostoftheACEcloudrequirementscouldhavebeenprovidedbyincludingradiometerchannelsontheradarssothatmicrowavebrightnesstemperature(Tb)attheradarfrequencieswerecollectedonlyalongtheswathsampledbytheradar.Ifthiswerethecase,stand-alonemicrowaveimagerswouldnothavebeenrequiredtoaddressACE-cloudsmeasurementneeds.Thetradespacebetweenthecoarsespatialresolutionbuthighaccuracyprovidedbytraditionalmicrowavesensorsandafootprintthatis,bydefinition,perfectlymatchedtothatoftheradarmeasurementsshouldbecarefullyexaminedsincenotrequiringstandalonemicrowaveimagerswouldhavesignificantlyreducedthecomplexityandcostofACE.Itispossiblethattheradiometerchannelsontheradarwouldhavebeenpreferabletothemoreaccuratebutlargerfootprintsfromtraditionalradiometermeasurements-especiallyinbrokencloudfields.Higherfrequencymicrowave(i.e.beyondthehighestfrequencyoftheACEradar-94GHz)andsub-millimeterradiometermeasurementswouldbeamajorbenefittomanyofthesciencequestionsbutwerenotconsideredaspartofthebaselinemission.Suchhighfrequencyandsub-millimetermeasurementscouldbeprovidedbyinternationalpartnersorotherwiselaunchedindependentlyofACEandmanagedaspartofasatelliteformationorconstellation.TheA-Trainisanexcellentexampleofsuchaconstellationthatcoalescedopportunisticallyovertime.

Asmentionedearlier,ourgoalwastoformulateanACEmissionthataddressedthescienceneedsoftheAerosol-Cloud-Precipitation-OceanEcosystemcommunitiesinthecomingdecadebutthatwasalsoachievableintermsofcostandcomplexity.Oneapproachthatcouldhavebeenconsideredwastoseeknaturaldivisionsinthescienceapplicationsthatcould,forinstance,haveallowedforexploitationofnaturalsynergiesamongmeasurements.AnotherapproachtoanimplementationofACE

wastoexploitopportunitiesforcollaborationandsynergy.ThePACEmission,forinstance,affordedonesuchopportunity.UsingPACEasafoundation,anHSRLandDopplercloudradarflyinginformationwithPACEwouldallowACEtoaddressmost,ifnotall,ofthesciencequestionsoriginallyposedinthe2010whitepapersinadditiontoaddressingemergingsciencesuchasocean-lidar.ExploitingthissynergywouldrequiresomecompromiseamongthevariousdisciplinesbuttheendresultwouldbeaconstellationthatistrulyanadvanceovertheA-TrainthatwouldpushNASAEarthobservationalscienceintothe2020’sandbeyond.

Aerosol-Cloud Questions Consensusemergedwithinthebroadercommunity(e.g.,IPCC2013,Chapter7)that1)differencesinclimatesensitivityamongmodelsareduetodifferencesintheirsimulationofshallowmarineboundarylayercloudsand2)theprimarymechanismsbywhichaerosolimpactsclimateisviatheprocesslevelperturbationswithintheseshallowconvectiveclouds.Theaerosolindirecteffectsareknownbyvariousnomenclature.Thefirst(Twomey,1974)andsecond(Albrechtetal,1989)aerosolindirecteffectsareconceptuallysimplebutverydifficulttodocumentobservationally.Thisisbecausetheprocessesthatresultintheindirecteffectsaremicrophysicalinnaturetakingplaceatthescaleswhereaerosolevolvesintoclouddropsandclouddropsintoprecipitation.Theseprocessestypicallyoccurwithinopticallythickhydrometeorcolumnsinoftenbrokencloudfields,andvaryrapidlywithheightoverdepthscalesofafewhundredmeters.Assuch,theseeffectsarelargelybeyondthereachoftraditionalpassiveremotesensing.Allstudiesclaimingobservationalevidenceoftheseeffectshavenecessarilydiagnosedthembyhowtheeffectsarehypothesizedtochangethebroadercloudfield.

Theindirecteffectsofaerosoloncloudsareexpectedtobeparticularlylargeinboundarylayercloudssuchasshallowstratocumulusandcumulusthatareubiquitousacrosstheglobaloceansfromthetropicstothehighmiddlelatitudesofbothhemispheres(Rosenfeldetal.,2014).UncertaintiesinthefeedbacksofthesecloudsastheyinteractwithaerosolandchangingcirculationunderclimatechangearetheprimarycontributorstouncertaintyinpredictionsoftheclimateresponsetodoubledCO2(SodenandVechi,2011).ProgressinthelasteightyearsinthisareahasbeenrealizedprimarilythroughmodelingworkandanalysisofdatafromtheA-Train.ThepaperbyStevensandFeingold(2009)andreferencesthereindemonstrate,usingbothmodelingandA-Trainmeasurements,theimportanceofdynamicalfeedbacks(buffering)thatexistbetweenaerosol,shallowclouds,andprecipitationthatmodulatethe1stand2ndaerosolindirecteffectsinacloudfield.Forexample,LESstudieshaveshownthatcloudsthatareperturbedbyhigheraerosolconcentrations,becomedeeper,precipitatemoreintensely,andresultinstrongerdowndraftswithhigherwindatthesurface(MaceandAbernathy,2016;Korenetal.,2014;Xueetal.,2008).

Thelessonstobelearnedarethatacloudfieldobservedinnatureataparticularinstanthasahistorythatincludesrepeatedprocessingofaerosolthroughcloud

elementswithinlarge-scaledynamicalenvironments.Afieldofcumulusorstratocumulusobservedbyorbitingsatellitesisasnapshotofachangingsystemthatisrespondingattheinstantofmeasurementtoaperturbedandbufferedenvironmentthathasbeenandisundergoingmodificationbythecloudprocessesandlarge-scalemotions.

Thiscomplexityisastrongargumentforglobalsatellitemeasurementssinceitwilltaketimetobuildstatisticalportraitsofcloudfieldsinvariousstatesofevolutionespeciallyoverremoteregionsoftheglobaloceans(MaceandAvey,2016).Whilefieldprogramcasestudiesareusefulandnecessary,theyarenotsufficientintermsofeitherdurationornumberofcasesneededtoproviderobuststatistics.Suchlimitationisevidencedbythediverserangeofcontradictoryfindingsthathasemergedfromrecentfieldexperimentsseekingtoquantifyaerosolindirecteffects.Asatellite-basedmeasurementstrategy,therefore,mustincludedatarelevanttothechangingsystemthatcanbeassimilatedbymodelsthatresolvethemotionsandprocesseswithincloudelements.Forshallowconvectiveandstratiformcloudsrelevanttothisclassofquestions,dualfrequencyDopplerradar(KaandWbands)isdesirable.HoweveronlysinglefrequencyWbandDopplerradarwouldlikelybeconsideredrequired(seetheaerosolSTM)becausethedualfrequencyinformationinshallow,weaklyprecipitatingcloudsisminimalandKa-bandradarwilloftennotprovidetherequiredsensitivitytosamplethesecloudseffectively.Inaddition,highspatialresolutionmicrowave(perhapsprovidedbytheradar),andvisiblereflectancesinafewbandsthatcontainindependentinformation(NakajimaandKing,1990)arecriticallyimportantforretrievalofcloudpropertieshere,asisinformationregardingtheregionalaerosolbackgroundthatrequiresomecombinationoflidar(ideallyHSRL)andpolarimetricvisiblereflectancesasdiscussedintheaerosolsection.KnowledgeofthechemicalcompositionandCCNdistributionthatHSRLandpolarimetrycouldprovide(i.e.ultimatelycompositionoftheaerosolthatactascloudcondensationnuclei)islikelynecessarytofullyaddressthe1stand2ndaerosolindirecteffectsinshallowcumulusandstratocumulus.

Cloud-Precipitation Questions TheCloud-Precipitationquestionstendtofocusondeeperclouds(e.g.frontallayerclouds,moderatelydeeptodeepconvection)whereicephaseprocessesthatresultinprecipitationareimportanttotheevolutionofthecloudsystem.EventhemostadvancedCRMscontainmanyprocessesthatrequireobservationalconstraints.Forinstance,changesindropletbreakupparameterizations,icecrystalcollectionandrimingefficiencies(amongothers)andtheirdependenceonverticalmotioncancausedrastic(manyhundredsofpercent)differencesinsurfacerain/snowaccumulationsandresultinfeedbacksonthedynamicalenvironmentvialatentheatreleasethattotallychangethepredictedevolutionofthecloudfield(VanDenHeeveretal.,2011).Thesesensitivitiesextendacrossthesynopticspectrumfromtropicalconvectivecloudstostratiformrain,tofrontalsystemsandstratiformcloudsinthemiddleandhighlatitudes(e.g.Adams-Selinetal.,2013;Igeletal.,2013;SaleebyandvandenHeever,2013).

MultifrequencyDopplerradarwithcollocatedmicrowaveTbarefundamentaltothemeasurementstrategyneededtoaddresscloud-precipitationcentricsciencequestions(MaceandBenson,2016;PosseltandMace,2014).WhileourearlierthinkingfocusedondualfrequencyKa/WbandDopplerradar,theadditionofKubandgreatlyextendsthereachofoursciencefocusintocloudsystemsthataremuchdeeperandmoreheavilyprecipitating.WenowincludeKubandasanoptioninthebaselineSTM.Similarly,higherfrequencymicrowavemeasurements(>89GHz)willprovideimportantconstraintsontheicemicrophysics.However,thequantitativebenefitofsuchmeasurementswhencombinedwithmulti-frequencyradarhasnotyetbeendemonstrated.Wetherefore,listhighfrequencymicrowaveandsub-millimetermeasurementsaspartofathresholdmissionthatcouldbeprovidedbyinternationalpartnersorothersources.Formostofthecloud-precipitationobjectivesofACEClouds,lidarmeasurementsarenotasrelevantbecausealidarattenuatesinthefirstfewopticaldepthsofcloudsthatareveryopticallythick.Furthermore,thesecloudsystemstendtobemuchlargerinscale(extendingto1000’sofkm)makingitimpossibletoconstrainthelocalaerosolenvironmentsinwhichtheydevelopwithlidarorpolarimetermeasurements.Alternativemeanssuchasdataassimilationwillthereforebeneededtoprovideaerosolinformationinsuchsystemsshoulditbedesirabletoexaminethesecond-ordereffectsinducedbyaerosolsonthesemoreenergeticsystems.

Cirrus Weaddresscirrusasaseparatecategory.Cirrustendtobeopticallythin,horizontallyextensive,andtheroleofaerosolisuncertainbutlikelysecondorder.Cirruswithopticaldepthslessthantwodrivetheradiativeheatinginthetropics(BerryandMace,2014)anditiswidelyacceptedthattropicalcirrusimposeapositivefeedbackonawarmingclimatebecausetropicalcirruswilldetrainfromdeepconvectionataconstanttemperaturewhilethesurfacewarms(ZelinkaandHartmann,2012).Whileallmodelstendtogeneratethispositivefeedback,thereasonforthisagreementisnotclearanditisnotknownifthisresultisfortuitousoriftheglobalmagnitudeofthisfeedbackisphysicallyreasonable.Improvedunderstandingofdeepconvectiveprocessesthatresultindetrainmentoficetothetropicaluppertropospherewilllikelyimproveourunderstandingoftheroleoftropicalcirrusinclimatechange.

Lidar-radarsynergyismaximizedinthincirrusnearopticaldepthone(BerryandMace,2014)sothatbothradarandlidarareneededtodescribethem.However,thespecificroleofHSRLmeasurementsinaddressingcirrusprocessesremainstobedetermined.Thekeysciencequestionsherearewhatcontrolstheamountoficedetrainedfromdeepconvectionandwhatprocessescauseanvilstoevolveintoselfmaintainingcirruslayers.Mostcirrusquestionscouldbeaddressedwitheitherthebaselineorthresholdmeasurementstrategieslistedabove.Forinstance,singleordualfrequencyDopplerradaratWandKabandscombinedwithlidarthatisconsideredcriticaltotheaerosol-cloudquestionswouldprovidesignificantand

uniqueinformationregardingthincirruswhilethickercirrusbeyondopticaldepth10orsowouldbeinformedbytheclouds-precipitationmeasurementobjectives.2.3 Summary Thesetofimportantquestionsthatunderpintheuncertaintyinclimatechangeprojectionsduetoaerosols,cloudsandprecipitation,andthefundamentalapproachoftheACECloudWorkingGrouptoaddressingthosequestionsmaturedsignificantlyinthefinalyearsoftheACEprogram.

Werecognizethataerosol-cloud-precipitationprocessesremainoneoftheprincipalunderlyingcausesforuncertaintiesinclimatepredictions. Tomakefurtherprogress,theseprocessesrequiresynergistic,verticallyresolved,activelidarandradarcombinedwithpassivemicrowaveandsolarreflectancemeasurements.Thegoalforthosemeasurementsistoprovidebetterconstrainsonatmospheric,hydrological,andrelatedprocessestoultimatelyimprovefuturegenerationsofclimatemodels.Unfortunately,nosinglemeasurementcanthoroughlyaddressprocess-orientedquestions.Thoseprocessesarecomplexandspatiallyheterogeneousandrelatetomultiplerelatedprocesses.Asanexample,someprocessesdependoncloudandaerosolmicrophysicalproperties,aswellastheirinteractionswiththethermodynamicenvironmentacrossarangeofscales. TheA-Trainsatelliteconstellationhasdemonstratedthatthecombinationofmultipledisparatemeasurementsprovidessignificantmeasurementsynergiesandhelptoadvanceourunderstandingwellbeyondtheoriginalscopeofanyofthesinglemissions.

Instrument Measurement Cloud Microphysical Constraint

Additional Information and Comments

Backscatter Lidar High Spectral Resolution Lidar (HSRL)

Extinction, Single Scatter Albedo

• Attenuated Backscatter profiles in thin clouds

• Aerosol properties in vertical profiles

• Aerosol Composition

• Produces direct evaluation of optically thin cloud and aerosol extinction and aerosol single scattering properties

• Provides information on cloud-top-height and more generally insight into vertical structure of thin cloud and aerosol.

Multi Frequency Doppler Radar W/Ka Bands With Ku band

Radar Reflectivity • Vertically resolved 6th moment of cloud drop size distribution for particles less than 0.1 of the radar wavelength

• Differential response to large hydrometeors

• Ku provides additional information on heavy precipitation

• Differential frequency radar reflectivity and Doppler velocity for larger particles (> ~0.3 mm) can be used to identify the presence of such particles and help characterize the microphysics of this part of the distribution.

• Differential attenuation with respect to 94 GHz is likely to prove useful in identification of cloud and precipitation type (phase) and retrieval of precipitation water content.

• Dual-wavelength ratios at Ka-W and Ku-W bands can further discriminate ice species: snow, graupel, and hail

improving ice water content retrieval accuracy

Doppler Velocity

• Vertically resolved 2nd/3rd moment of drop size distribution (reflectivity weighted)

• Differential response in presence of large hydrometeors.

• Doppler velocity is a measure of total velocity of the cloud particles. In convective cores, the velocity is dominated by cloud vertical motion. In other conditions, the velocity can be separated into contributions from particle fall velocity and air motion (Dynamics).

• Cloud liquid water drops generally fall too slowly to be measured via this technique but it is very useful for identification, and characterization of ice clouds, snow, drizzle, and rain.

• Ku Band desired to characterize heavy precipitation

Differential Attenuation, Path Integrated and Vertical Profile

• Profile of Condensed Water • Total column liquid water

• One can use surface reflectance to estimate total attenuation in the radar in the column, when the radar is not totally attenuated. The attenuation is determined largely by the amount of liquid water (cloud and precipitation) in the column.

Radiometer Channels

• Passive microwave Tb • Constrains integrated liquid water and ice scattering.

Narrow Swath Vis-IR Imager High-Resolution VIS-SWIR Polarimeter

UV, Visible and shortwave infrared radiances at multiple view angles. Polarized reflectances at some visible wavelengths.

• Cloud phase near “cloud top” (in region of cloud where bulk of visible light is reflected)

• Radiative-effective ice cloud-habit (constrains possible/likely cloud habit mixtures) near “cloud top”.

• 2nd moment of drop size distribution near cloud top)

• Effective radius near cloud top.

• Multi-view-angle imagery can be used with stereo-imaging technique to derive cloud top height. This approach is insensitive to calibration and does not rely on any assumptions regarding atmospheric temperature lapse rate. The approach works well except for exceptionally diffuse high clouds, representing a failure rate of only a few percent. 50 m resolution images can be used to determine cloud-top-height with precision of about 50 m assuming view angles at +/- 45 degrees from nadir.

• Important for defining aerosol type in broken cloud fields

• Reflectances constrain column optical depth and effective radius.

Passive Low and High Frequency Microwave Radiometer Channels at: 10.65, 18.7, 23.8, 36.5, 89, 166.5, 183±3, 183±9 GHz

Brightness temperature

• Column liquid water path • Column water vapor path • Surface precipitation rate in

wide swath • Ice cloud and ice

precipitation • Important wide swath • Significant constraints for

nadir viewing

• Column constraint • Will provide wide-swath / cloud

system context to narrow-swath observations and in particular information on precipitation.

• With radiometer channels on radar, these instruments are considered to be not required.

Passive Sub-mm Radiometer Channels at high frequency: 325.15, 448.00, 642.90, 874.40 GHz

Brightness temperature

• Column ice and particle size constraint for ice clouds;

• Proportional to the 3rd moment of particle size distribution

• Column constraint • Will provide wide-swath / cloud

system context to narrow-swath observations.

• These measurements are not required. Could be provided by partnership.

Table 2.3. Potential ACE Instruments and Measurements and their contribution to Level 1 Geophysical Parameters. The instruments that we consider required are denoted in bold font. Italicized font indicates goals or non-required instruments for ACE Clouds.

2.3 Ocean Biology and Biogeochemistry TheOceanEcosystemSTMsynthesizestheend-to-endrequirementsassociatedwithaddressing6groupsofoverarchingFocusedQuestions:

1. Whatarethestandingstocks,composition,&productivityofoceanecosystems?Howandwhyaretheychanging?[OBB1]

2. Howandwhyareoceanbiogeochemicalcycleschanging?HowdotheyinfluencetheEarthsystem?[OBB2]

3. Whatarethematerialexchangesbetweenland&ocean?Howdotheyinfluencecoastalecosystems,biogeochemistry&habitats?Howaretheychanging?[OBB1,2,3]

4. Howdoaerosols&cloudsinfluenceoceanecosystems&biogeochemicalcycles?Howdooceanbiological&photochemicalprocessesaffecttheatmosphereandEarthsystem?[OBB2]

5. Howdophysicaloceanprocessesaffectoceanecosystems&biogeochemistry?Howdooceanbiologicalprocessesinfluenceoceanphysics?[OBB1,2]

6. Whatisthedistributionofalgalbloomsandtheirrelationtoharmfulalgalandeutrophicationevents?Howaretheseeventschanging?[OBB1,4]

EachofthesesciencequestionstracesdirectlytooneormoreofthefourbroaderscienceobjectivesofNASA’sOceanBiologyandBiogeochemistry(OBB)program,asdefinedinthedocument,Earth’sLivingOcean:AStrategicVisionfortheNASAOceanBiologicalandBiogeochemistryProgram,andindicatedabovebythebracketedOBBxdesignations.

ToanswertheFocusedQuestions,theACEOceanEcosystemteamdefinedamulti-tieredapproachinvolvingremotesensingobservations,supportingfieldandlaboratorymeasurements,andoceanbiogeochemical-ecosystemmodeling,with9groupsofspecificobjectives:

1. Quantifyphytoplanktonbiomass,pigments,opticalproperties,keygroups,andproductivityusingbio-opticalmodelsandchlorophyllfluorescence

2. Measureparticulateanddissolvedcarbonpools,theircharacteristicsandopticalproperties

3. Quantifyoceanphotobiochemicalandphotobiologicalprocesses

4. Estimateparticleabundance,sizedistributions(PSD),andcharacteristics

5. AssimilateACEobservationsinoceanbiogeochemicalmodelfieldsofkeyproperties(air-seaCO2fluxes,export,pH,etc.)

6. CompareACEobservationswithground-basedandmodeldataofbiologicalproperties,land-oceanexchangeinthecoastalzone,physicalproperties(e.g.,winds,SST,SSH,etc),andcirculation(MLdynamics,horizontaldivergence,etc)

7. CombineACEocean&atmosphereobservationswithmodelstoevaluate(1)air-seaexchangeofparticulates,dissolvedmaterials,andgasesand(2)impactsonaerosol&cloudproperties

8. Assessoceanradiantheatingandfeedbacks

9. Conductfieldsea-truthmeasurementsandmodelingtovalidateretrievalsfromthepelagictonear-shoreenvironments

Thesespecificobjectiveswerethentracedtothemeasurement/instrumentrequirementsfortherelevantACEsatellitesensors,supportingfieldandlaboratoryactivities,andmodeling.AlsoidentifiedwerespecificACEplatformrequirementsandancillarysupportingglobaldataproductsfromothermissions,models,andfieldstudies(seerightcolumnsinOceanSTMbelow).

ThethreeprimaryinstrumentsontheACEplatform(s)relevanttothemission’soceanecosystemscienceobjectivesaretheadvancedoceanradiometer,lidar,andpolarimeter.InadditiontotheACEscienceteammeetings,theACEOceanEcosystemteamconductedroughlyweeklyteleconferencestodefinethespecificmeasurementandinstrumentrequirements,withoutcomesfromthesedeliberationsrecordedinaseriesofdocumentsandpublications.Theteamassumeda‘grassroots’approach,beginningwiththeproductionofindividualProductAssessmentReportsforeachoceangeophysicalpropertytargetedbytheACEinstruments.Thesereportsprovideddetaileddescriptionsofthederivedparameters,theirfieldmeasurementmethodologies,producterroranalyses,andaccuracyassessments.

TheACEOceanEcosystemteamnextconstructedasummarytableoftargetedocean-relevantproperties.Thesepropertiesinclude(1)spectralremotesensingreflectance,(2)inherentopticalproperties(totalabsorption,phytoplanktonabsorption,detritalabsorption,coloreddissolvedorganicmaterialabsorption,backscattercoefficient,beamattenuation),(3)diffuseattenuationcoefficientfordownwellingplaneirradianceat490nm,(4)24-hrfluxandinstantaneousincidentphotosyntheticallyavailableradiation,(5)surfaceoceaneuphoticlayerdepth,(6)particulateinorganiccarbonconcentration,(7)particulateorganiccarbonconcentration,(8)dissolvedorganiccarbonconcentration,(9)suspendedparticulatematterconcentration,(10)particlesizecharacteristics,(11)totalchlorophyll-aconcentration,(12)phytoplanktoncarbonconcentration,(13)normalizedfluorescenceline-height,(14)fluorescencequantumyield,(15)netprimaryproduction,(16)phytoplanktonchlorophyll:carbonratiosandgrowthrate,and(17)phytoplanktonfunctional/taxonomicgroups.Foreachofthese14

properties,thesummarytabledefinedthebaselineandthresholdvaluerangesforACEretrievals,alongwithdocumentingthebasisfortheserangeestimates(Cetinicetal2018).

Inadditiontotheaboveactivities,theACEOceanEcosystemteamconductedmodelsimulationstudiestoidentifymeasurementrequirementsfortheACEoceanradiometernear-infrared(NIR)andshort-waveinfrared(SWIR)bandsand,usingastate-of-the-artspectralinversionalgorithm,todefinespectralsignal-to-noiserequirements.ResultsfromalloftheseactivitiesweresynthesizedinanACEOceanEcosystemwhitepaper,summarizedastheOceanEcosystemSTM(copiedbelow),andrecentlypublishedinCetinicetal(2018).

TheACEOceanEcosystemwhitepaperandSTMprovidedtheneededframeworkforconductingaverythoroughevaluationofinstrumentrequirementsforanadvancedoceanradiometer,whichwaspublishedin2011(Meister,etal.2011).Thetimingofthispublicationwasideal,asitappearedinparallelwithearlydeliberationsofthePre-AerosolCloudEcosystem(PACE)ScienceDefinitionTeam(SDT).MultiplemembersoftheACEOceanEcosystemteamwerealsomembersofthePACESDTandtheMeisteretal(2011)documentservedasakeyreferenceindefiningbaselineandthresholdrequirementsforthePACEinstrument/mission.Thefinal,274pagePACESDTrecommendationdocumentprovidesthemostthoroughrecommendationguidelinesforanadvancedoceanradiometersuitableforthePACEandtheACEmissions.

WithrespecttolidarandpolarimetermeasurementsforACEoceanscienceobjectives,significantadvanceshavebeenrealized.In2013,thefirstglobalassessmentofoceanplanktonstocksusingtheCALIOPlidarwaspublished(Behrenfeldetal.2013).Subsequently,CALIOPoceanretrievalswereusedtostudyannualcyclesofphytoplanktonbiomassinthepolarregions,wheretraditionalsatelliteoceancolormeasurementsareextremelychallengingandforsomemonthsimpossible(Behrenfeldetal.2013).Followingtheseandotherstudies,arecentreviewofoceanremotesensingwithsatellitelidarwaspublishedandprovidesadetailedevaluationofmeasurementrequirementsforanACElidar(Hostetleretal.2018).Withrespecttopolarimetrymeasurements,Remeretal(2015)providedadetailedsummaryofmeasurementrequirementsandscienceadvantages.

Insummary,theACEOceanEcosystemteamconductedend-to-endevaluationsofmissionmeasurementrequirementsnecessarytoaddressthe6groupsofoverarchingFocusedScienceQuestions.Theseevaluationsbeganwithanbasicassessmentofstate-of-the-artaccuraciesanduncertaintiesinfieldmeasurementsoftargetedkeyecosystempropertiesandthenstep-wiseextendedtoaverydetailedevaluationofsatelliteinstrumentrequirementsandrequirementsforsupportingfield,laboratory,andmodelingwork.BenefittingfromtheparallelassessmentsofthePACESDT,theOceanEcosystemteamconcludedthatoverallunderstandingofobservationalrequirementsfortheEcosystemaspectsoftheACEmissionishighlymature.

2.4 Aerosol-Ocean ProjectionsoffutureclimateremainanimportantscientificgoalformuchoftheEarthsciencecommunity.Alargefractionoftheuncertaintyinpredicting21st-centuryclimatechangeliesintheuncertaintiesassociatedwithanthropogenicaerosolforcingandfeedbacksthatresultfromland-atmosphere-oceaninteractionsandinteractionsbetweennaturalandanthropogenicemissions(IPCC,2013).Asaerosoleffectsonclimateareestimatedfromthedifferencesbetweenmodelsimulationswithpresent-dayandwithpreindustrialaerosolandprecursoremissions,accuraterepresentationofmarineaerosolsiscriticalforassessmentofanthropogenicaerosoleffectsinEarthSystemSciencemodels(Ramanathan,2001;Andreae,2007;Hooseetal.,2009;Ghanetal.,2013;Carslawetal.,2013).Changesinmarineecosystemsinresponsetoawiderangeofstressfactorscausedbyhumanactivitiescanfurtherincitecomplexfeedbacksbetweenoceanandatmosphere.Reductioninseaicecoverandchangesinphysical(temperature,salinity,circulation),chemical(nutrientavailability,pH)andbiological(bacterialandphytoplanktonabundance)propertiesofseawatercanstronglyinfluenceproductionrateandphysicochemicalpropertiesofmarineaerosols.Thesechangesinseawaterpropertiescan,inturn,affectthesources,sinks,andpropertiesofmarineaerosol,influenceconcentrationsofcloudcondensationnuclei(CCN)andicenucleatingparticles(INP)intheatmosphere.Todaythereisagreatneedforcomprehensiveobservationaldataonmarineaerosolsthatcanbeusedforimprovement/evaluationsofclimatemodels(Meskhidzeetal.,2013).Thecollectionofsuchdatarequiresmultiscalemeasurements(fromin-situtoremotesensing)throughacoordinatedandmultidisciplinaryresponse,withinvolvementandexpertisefromabroadrangeofscientificcommunities(includingatmosphericsciences,physicalandbiologicaloceanography).

Aerosol-Ocean Questions CurrentEarthSystemSciencemodelsexhibitalargediversityintheirrepresentationsofmarineaerosolsourcesandsinks,aswellastheprocessesbywhichtheseaerosolsimpactcloudwaterandiceformation.Thisdiversityisdueinparttothelackofmeasurementstoconstrainthemodels.Measurementsofmarineaerosolsarechallengingbecauseoftheirvastspatiotemporalvariabilityandlowconcentration.Keyquestionsremainunansweredregardingtheimpactsofmarineaerosolsoncloudsandclimate,limitingourabilitytoquantitativelypredicthowthefutureclimatewillrespondtocontinuedandincreasinggreenhouse-gasandfine-particleemissions.

1.HowmuchdomajorclassesofmarineparticlescontributetotheCCNandINPnumberofthemarineboundarylayerindifferentregionsandseasons?2.Howdoenvironmentalparameters(surfacewindspeed(U10),atmosphericstability),oceanphysicochemicalproperties(seasurfacetemperature(SST),salinity,whitecapfraction,Chlorophylla(Chla),dissolvedandparticulateorganiccarbonconcentration,surfacefilmcoverage),biologicalindicators(organismtype

andabundance,physiologicalstress),andseaiceextentmodifytheCCNandINPnumberovertheocean?3.Howdochangesinocean-derivedCCNandINPabundancesaffectcloudmicrophysicalpropertiesandphase?4.Whatarethefeedbacksbetweenoceanicemissions,marineaerosolsandclouds,aerosoldeposition,andoceanecosystems?Howishumankindchangingthesefeedbacks?

Thesequestionscanbeaddressedbythedevelopmentofimprovedremotesensingproductsincombinationwithrecentadvancesinmodeling,remotesurfacemonitoringandinsitufieldandlaboratorymeasurements.

Space-based Observations are Essential for Addressing Aerosol-Ocean Challenges and Questions Satellitesare,andwilllikelyremain,thedominantmeansforimprovedcharacterizationofmarineaerosolsandaerosol-cloud-climateinteractionsinachangingclimatebecausetheyprovideglobal,long-terminformationonthespatiotemporalvariabilityofmanypropertiesaffectingmarineaerosolproduction(i.e.,surfacewindspeed,waveparameters,surfaceChlorophylla(Chl-a),dissolvedandparticulateorganiccarbonconcentration,whitecapfraction,SST,andsalinity)andremoval(i.e.,wetanddrydeposition).Thereisanumberofpast,existingandplannedremotesensinginstrumentssupportedthroughU.S.andinternationalprogramsthatcanbeusedforcharacterizationofmarineaerosols,aswellasground-basedsystemsincludingtheMAN,aship-bornedataacquisitioninitiative(Smirnovetal.,2011)complementingisland-basedAERONET(Holbenetal.,1998)measurements,andsatellitessuchasMODIS,MISR,AATSR,PARASOL,MERIS,SeaWiFS,CALIPSO,GPM,SAGE-III/ISS,CATS,andPACE.However,noneofthesesensorscanachievecoincident(intimeandspace)retrievalsofvertically-resolvedaerosolinformation,oceansub-surfaceproperties,andoceanbiologicalparameters,i.e.,parametersessentialforquantitativecharacterizationofmarineaerosol-cloud-climateinteractions.Moreover,currentsatellitesensorseitherdonotprovidethedataorprovideatsignal-to-noiseratiothatisnothighenoughforretrievalofmanyoceanecosystemprocessesandaerosolspeciationandloadingsovertheoceans.ExistingsatellitesalsoprovidelimiteddataintheArcticandSouthernOceanregionscharacterizedbyhighcloudinessandlowsolarzenithangles.Therefore,onlythecombinationofinstrumentationplannedforfutureACEmissioncanprovidethedataonglobaloceanecology,biogeochemistry,aerosolsandcloudswiththeaccuracyneededtosignificantlyadvanceourunderstandingofthecoupledocean-aerosol-cloudsystem.

Additionalinvestmentsareneededtolinkspace-basedobservationswithotherobservations.ThesupportingsatellitemeasurementsareneededtoassessenvironmentalconditionsaffectingmarineaerosolsincludingSST,U10,icecover,humidityandtemperatureprofilesandprecipitationrates.Inparticular,measurementsofdrizzleandprecipitationratescoincidentwithlidarand

polarimeterobservationsarerequiredtobetterconstrainaerosolsinksovertheoceans.Inadditiontotheocean’sphysicalstate,thechemicalcompositionoftheoceanandtheseasurfacecaninfluenceseasprayproduction,sodirectmeasurementsareneededofsurfacefilmcoverage(e.g.,viasyntheticapertureradar)andbiogeochemicalvariablesthathavecausallinkstoseasprayproduction.Improvementsinsensortechnologycanadvancethefieldpastusingproxieslike[Chl-a]toderivemarinechemicalstateanditsimpactonaerosolcomposition.

ThedetailedmechanismsandtheradiativeimpactofmarineaerosolsintheEarth'sclimatesystemarebestunderstoodthroughthecombinationofsatelliteremotesensing,insituobservations,andmodeling.Forexample,controlledlabworkcanprovidedetailedinsightforexploringtherelevantparameterspacewithgreaterclarityandspecificity.Suchlabexperimentscanbeusedasatooltoilluminatecausalrelationshipsthatleadtobetterfieldobservations.Dedicatedfieldmeasurementscanrangefromin-waterphysical,chemical,biological,andopticalproperties,tonumbersizedistribution,chemicalcharacterization,hygroscopicityandCCNandINPpropertiesofaerosolsandprecursortracegasconcentrationmeasurements.Inaddition,fieldcampaignswillcontributevaluabledataforcalibrationandvalidationofsatellitesensors,aswellasproviderequireddatatoanswerthekeychallengesandquestionsraisedinthisdocument.

The ACE Aerosol-Ocean Science Traceability Matrix TheAerosol-OceanSTMprovidesaroadmapfromsciencequestionstosensorandmissionrequirements(i.e.,fromwishfulthinkingtoconcretemeasurements)forexploringthecomplexinterplaybetweenaerosols,clouds,andglobaloceanecosystems.TheAerosol-OceanSTMissummarizedin5groupsofoverarchingFocusedQuestions:

1.Whatisthefluxofaerosolstotheoceanandtheirtemporalandspatialdistribution?

2.Whatarethephysicalandchemicalcharacteristics,sources,andstrengthsofaerosolsdepositedintotheoceans?

3.Howarethephysicalandchemicalcharacteristicsofdepositedaerosolstransformedintheatmosphere?Howdooceanecosystemsrespondtoaerosoldeposition?

4.Whatisthespatialandtemporaldistributionofaerosolsandgasesemittedfromtheoceanandhowarethesefluxesregulatedbyoceanecosystems?

5.Whatarethefeedbacksamongoceanemissionsofaerosolsandgases,microphysicalandradiativepropertiesoftheoverlyingaerosolsandclouds,aerosoldeposition,oceanecosystemsandtheEarth'sclimate,andhowishumankindchangingthesefeedbacks?

ToanswertheFocusedQuestions,theACEAerosol-Oceanteamdefinedacoordinatedapproachthroughcombinationofinsitudata,satelliteremotesensingandmodels,with8groupsofSpecificObjectives:

1.Identifymicrophysicalandopticalpropertiesofaerosols,partitionnaturalandanthropogenicsources,andcharacterizespectralcomplexindexofrefractionandparticlesizedistribution

2.Characterizedustaerosols,theircolumnmass,ironcontentandothertraceelements,andtheirregional-to-globalscaletransportandfluxfromeventstotheannualcycle

3.Conductappropriatefieldobservationstovalidatesatelliteretrievalsofaerosolsandoceanecosystemfeatures

4UseACEspaceandfieldobservationstoconstrainmodelstoevaluate(1)aerosolchemicaltransformationsandlong-rangetransport,(2)air-to-seaandsea-to-airexchangeand(3)impactsonoceanbiology

5.Characterizeaerosolchemicalcompositionandtransformationduringtransport(includinginfluencesofverticallydistributedNO2,SO2,formaldehyde,glyoxal,IO,BrO)andpartitiongas-derivedandmechanically-derivedcontributionstototalaerosolcolumn

6.Monitorglobalphytoplanktonbiomass,pigments,taxonomicgroups,productivity,Chl:C,andfluorescence;measureanddistinguishoceanparticlepoolsandcoloreddissolvedorganicmaterial;quantifyaerosol-relevantsurfaceoceanphotobiologicalandphotobiochemicalprocesses

7.Relatechangesinoceanbiology/emissionstoaerosoldepositionpatternsandevents

8.Demonstrateinfluencesofoceantaxonomy,physiologicalstress,andphotochemistryoncloud/aerosolproperties,includingorganicaerosoltransfer

TheseSpecificObjectiveswerethentracedtotheMeasurementRequirementsfortherelevantACEsatellitesensors,supportingfieldandlaboratoryactivities,andmodeling.AlsoidentifiedwerespecificACEplatformrequirementstoprovidetheincreasednumberofparametersandimprovedsignalresolutionnecessaryforadvancingourunderstandingoftheseimportantprocessesandtoimprovefutureprojectionsofclimate.

TheprimaryinstrumentsontheACEplatform(s)relevanttothemission’sAerosol-OceanscienceobjectivesaretheSpectrometer,Polarimeter,HighSpectralResolutionLidar,andDualfrequencyDopplerradar.InadditiontotheACEscienceteammeetings,theACEAerosol-Oceanteamconductedweeklyteleconferencestodefinethespecificmeasurementandinstrumentrequirements,withoutcomesfromthesedeliberationsrecordedinawhitepaper.

TheACEAerosol-Oceanteamidentifiedbothaerosolandocean-relevantproperties.Foraerosolpropertiestheseincludeaerosoltype(dust,smoke,etc.),opticalthickness,complexindexofrefraction,andheightandsizedistributionswitha2-dayglobalcoveragetoresolvethetemporalevolutionofplumes.Althoughoceanicaerosolsourcesappeartoproduceaerosolandgasconcentrationsinthenearnoise

levelofexistingsatelliteplatforms,estimatesofnaturalbiogenicconcentrationsovertheoceanweredeemedtobeessential.Foroceanecosystemproperties,keypropertiesincludephytoplanktonfunctionaltypeandpigmentabsorptionspectra,coloreddissolvedorganicmatter(CDOM)absorption,totalandphytoplanktoncarbonconcentration,oceanparticlesizedistribution,phytoplanktonandCDOMfluorescence,phytoplanktongrowthratesandratesofnetprimaryproduction.ManyofthesedeterminationscanbemadebysamplingthetopoftheatmosphereradiancespectraandpolarizedradiancespectraforselectedUV,visibleandSWIRbands.Active(lidar)measurementsofaerosolpropertiesalongtheorbittrackarethoughttobeneededtorefineheightdistributionandcompositionandtoprovideindependentmeasurementsofoceanparticlescatteringanditsverticaldistributionwithinthewatercolumn.Manysupportingsatellitemeasurementsareneededtoassessenvironmentalconditionsaffectingaerosolsandorganichydrosolsincludingseasurfacetemperature,windspeedanddirection,chlorophyllconcentration,icecover,humidityandtemperatureprofilesandprecipitationrates.Inparticular,measurementsofdrizzledetectionandprecipitationratescoincidentwiththeACElidarandpolarimeterobservationswereidentifiedasrequiredparameters.ItwasenvisionedthatmanyoftheothersupportingglobalproductswouldcomefromoperationalsatelliteassetssuchasNPOESSorDecadalSurveymissions.

TheACEAerosol-Oceanteamalsoproposedthatsimultaneousdeterminationsoftroposphericconcentrationsofseveraltracegasspeciesmightbeimportantforlinkingocean–aerosolprocesses.Thesespeciesincludebutarenotlimitedtoformaldehyde(CH2O),glyoxal(C2H2O2),IO,BrO,NO2,andSO2.ThesedeterminationscouldcomefromfuturesatellitesystemssuchastheGeoCAPEmission,whichisplannedtohaveageostationaryorbit.

Inadditiontotheaboveactivities,theACEAerosol-OceanteamrecognizedfieldobservationstobeanintegralpartoftheACEmissionfromthepre-launchperiodonward.Insitumeasurementsareessentialforcalibratingandvalidatingsatellitesensorsandproductretrievals.Theyalsoprovideobservationsthatarenotpossiblefromsatelliteinstrumentsyetneverthelesscriticalforachievingscienceobjectives.Suggestedfieldmeasurementswouldrangefromsolarradiationobservationstoin-waterchemical,biological,andopticalpropertiesandtochemicalcharacterizationofaerosols.Twotypesoffieldcampaignsareenvisioned:sustainedtime-seriesobservationsfromfixedlocations(e.g.theBATSandHOToceanographictime-seriessites,andtheAERONETsunphotometernetwork)andmobilesites(MarineAerosolNetwork),andintensivefieldcampaignstoaddressparticularsciencequestions.Bothtypesoffieldcampaignsareseenascontributingvaluabledataforcalibrationandvalidation,aswellasdatarequiredtoanswerthefocusedquestionsraisedintheScienceTraceabilityMatrix.SomepossibletopicsoffieldcampaignstudiesthataddressquestionsoftheAerosol-OceanSTMinclude:

•NorthAtlanticAerosolsandMarineEcosystemsStudy–Astudyfocusingoncomparing/contrastingtheatmosphericimprintofcoccolithophoreand/or

Phaeocystisblooms,andexaminingthehypothesisthattheNorthAtlanticbloomisamajorsourceoffineparticleorganicaerosols

•SouthernOceanandDMS–ASouthernOcean(SO)studywouldbeonthedimethylsulfide-cloudconnection.GiventhatoceanicgasesareprobablythedominantCCNprecursorsovertheSO,thisstudycouldbeofgreatclimaticsignificance

•NorthPacificAsianOutflowImpact–AnexaminationoftheimpactofAsiandustandpollutantoutflowonoceanicproductivity,tracegasemissions,andaerosol/cloudproperties.

Summary and Recommendations Insummary,theACEAerosol-Oceanteamhasconductedacomprehensiveevaluationofmissionmeasurementrequirementsnecessaryfornarrowingthegapinthecurrentunderstandingofanthropogenicandnaturalcontributionstoachangingclimate.Improvingclimatepredictionswillrequiredevelopmentofnewspace-based,field,laboratoryinstruments,andmodelingcapabilities.Byexpandingavailablesatellite-bornesensorstoencompassaerosolforcingofoceanbiologicalsystemsandcloudprocesses,itwillbepossibletocapturepotentiallyimportantfeedbackswithimplicationsonatmosphericradiativeeffectsandclimate.Models,inadditiontorepresentingcurrentclimate,willbeabletobettercapturethechangesthathaveoccurredoverthepastcenturyandpredicttheclimatechangesthatwouldresultfromdifferentfutureemissionstrategies.Achievingsuchconfidencecriticallydependsuponmorerealisticsimulationsoftheaerosol-oceanecosystems-cloudsystemwithforcingsandfeedbacksoperatingonmultiplespatiotemporalscales.

3 Assessment and Instrument Concept Development ThissectiondescribesthetechnologicalaccomplishmentstowardtheACEmission,includingaircraftinstrumentdevelopmentandutilization,originofsupportandTRLstatus.Foreachtypeofinstrument(radar,polarimeter,lidar,andoceancolorsensor)wesummarizetheroadmapadopted,accomplishmentsthusfarandon-goingefforts.

3.1 Radar SignificantradaradvancementsrelevanttoACEhavebeenachievedinthe2007-2018period.TheyweremostlyfundedthroughESTO’sIIP,AITT,ACTandInVESTprograms,withimportantcontributionsalsobyJPLandGSFCinternalresearchanddevelopmentfunding,theSBIRprogram,andseveralairbornefieldcampaignactivitiesfundedbyACE,GPM,theEarthVentureprogramorotherNASAprograms.Overall,theACEmissionconceptprovidedthenecessaryfocusfortechnologicaladvancesspecificallytargetingtheobservationofclouds,convectionandprecipitation.ACEenabledthefullvisionexpressedbythesciencecommunityintheESDS2007:instrumentcapabilitiesexpressedasstrongdesirementsintheearlyACEworkshops(i.e.,2007-2009)thatwereassessedaseitherimpossibleornotaffordableatthattime,arenowfeasible.WithoutACE’sstrategicfocus,itishardtoenvisionhowmanyoftheseadvanceswouldhavetakenplaceinthesametimeperiod.

RadardevelopmentsforACEfollowedthefourmaindirectionspresentedintheNovember2010reportasdetailedbelow.

Extension of CloudSat-class technology to meet the ACE threshold requirementsCompletionoftheACERADconcept(PI:S.Durden,JPL)technologymaturationthroughtheIIP’08funding(seeFigure3.1).ThisdesignprovidesbothKa-bandandW-banddual-polarizedDopplerobservationsatnadir,withadditionalKa-bandmeasurementsoveralimitedswath(i.e.,~30km).ThekeytechnologydevelopmentsidentifiedtoenablethisconceptweretheDragonianantennadesign(toallowKa-bandscanning;scaledversionshowninnear-fieldtestchamber),theDual-FrequencyDual-PolarizationQuasi-Opticaltransmissionline,theKa-/W-bandfrequencyselectivesurface,andthesignalgenerationandprocessingstrategy.TheTRLofeachofthesewasraisedthroughprototypeimplementationandtestinginrelevantenvironmentsothattheACERADoverallTRLhasbeenraisedto5(withmanysubsystemsathigherTRLduetoheritagefromCloudSat’sCPRandairbornecloudandprecipitationradarsandIIP-fundedenvironmentaltestingofthefrequencyselectivesurfaceforseparatingandcombiningKa-andW-bands).Nofurthertechnologymaturationisdeemednecessarybeforeinstrumentselectionsincetheremainingstepsareonlyrelatedtoscalingandengineering.ThelevelofmaturityofACERADatthistimeishigherthanthelevelofmaturityofCloudSatCPRattheendofCloudSat’sPhaseA.Thisinstrumentconceptmeetstheminimumrequirementssetin2010bytheACEScienceWorkingGroup.Theprimary

limitationsofthistechnologyareinthemarginalpotentialforfurtherminiaturization(becausethespace-qualifiedHigh-VoltagePowerSupplyunitsfortheExtendedInteractionKlystronhighpoweramplifiersarealreadypresentingsignificantchallengesinbeingassmallastheyare;andbecauseofthesimplewaveformsadoptedwhichimposelargeantennasizes).InabroadanalogytotheTRMM/PRandGPM/DPRprecipitationradarsproducedbyJAXA/NICTinthefirstdecadeofthismillennium,thesearemature,provenandreliableradartechnologies,whichhoweverrequiresignificantallocationsinSize,WeightandPowerandarethereforedifficulttoscale-upwithoutsignificantlyimpactingothermissioncosts.

Figure 3.1: Top Left: ACERAD IIP’08 (PI Durden), subscale Dragonian antenna prototype in test chamber; Top Right: WiSCR IIP’10 (PI Racette) sub-scale antenna flight demonstration through IPHEX mission in May, 2014 (see also Figure 3.2) and Middle: WiSCR instrument concept and key technologies.

Maturation of new technologies necessary to meet the ACE goals.InordertoenableinstrumentperformanceclosertothescientificneedsexpressedduringthedefinitionofACE,twoadditionalinstrumentconceptsweredefined:WiSCRand3CPR.Bothincludeuseofactiveelectronicallyscanninglineararrays(AESLA)illuminatingasingly-curvedparabolicreflector(SCPR)toincreasetheradarcross-trackscanningcapabilitieswhilenotincurringintheadditionalchallengesandoftenprohibitivecostsassociatedwithlarge2-Dactivearrays.Both

conceptsadoptadvancedsignalgenerationandprocessingschemestoachievethedesiredradarsensitivities,resolutionsandDoppleraccuracies.ThetechnologicaldevelopmentofWiSCRconceptinitiatedunderGSFCinternalfundingandperformedjointlywithNorthropGrumman,receivedcriticalfundingthroughIIP’10(PI:PaulRacette)andIIP’13(PI:LihuaLi)andhingesuponAESLAforKa-bandandinnovativeW-/Ka-bandreflectarraymainreflectortoenableuseofCloudSatheritagetechnologyatW-band.Thereflectarraytechnologyenablesco-locatedbeamsforallfrequencybandswithcapabilitytosupporteitherfixednadirorscanningW-bandbeams.TheantennaWiSCRconceptprovideswideswathimagingatKa-band.UndertheIIP’10,thereflectarrayantennaachievedaTRL5throughairbornedemonstrationofasubscaleantenna.TheIIP’13focusedonadvancingtheTRLoftheKa-bandradarAESAlinefeedandT/Rmoduledevelopment.TheT/RmoduledevelopedforspaceisalmostatTRL5by2018.Thetechnologicaldevelopmentof3CPRconcept,initiatedunderJPLinternalfundingandSBIRreceivedcriticalfundingthroughACT’11(PI:A.Fung)andIIP’13(PI:GregSadowy)anditisperformedjointlywithRaytheonandNuvotronicsaskeypartners.IthingesuponmatureKu-andKa-bandlinefeedarraytechnologiesandandmaturesaninnovativeW-bandactivefeedarraytechnologytoenablescanningatallfrequencies.ThekeytothistechnologyliesinaninterleavedpatternoftransmitandreceiveradiativesurfacesthatallowtoavoiduseofanyT/Rswitches,andonthemodulardevelopmentinunitsof16MMICTandRelementsinonesocalledScanningArrayTile(SAT)whichfacilitatesdesignandimplementationofActiveLineFeedArrays(ALAF)ofarbitrarylengthbymatingthemalongsideinthescanningplane.ThisinstrumentconceptisatTRL4havingdemonstratedthekeyfunctionality,includingactivescanning,oftheW-bandSATinalaboratoryenvironment.ItisexpectedtoachieveTRL6bytheendof2018throughThermal/VacuumtestingofascaledW-bandarray.TheroadmapbeyondthatpointincludesdemonstrationofintegrationofKu-andKa-bandALAFwithW-bandALAFintheairborneairMASTRprototype(describedlaterinthissection)andriskreductionstudyforlargersizes,upto3mx5m,cylindricalparabolicreflectorantennas.Development of alternative instrument concepts and architectures to achieve subsets of the same ACE goals with radically new and more affordable solutions.OnefirstconceptforapossiblepartialtechdemoofselectedsubsystemsofallofthethreeinstrumentconceptsdiscussedabovewasjointlydevelopedbyJPLandGSFCin2013inresponsetoarequestbyNASAHQ.ThisinstrumentconceptwasdefinedfordeploymentontheISS,andadoptsCOTSpartsandType-IIstandards.ItfocusesonthedemonstrationinorbitofsomeoftheKa-andW-bandcomponents,andofavarietyofdigitalprocessingschemesadoptedintheACEradarconcepts.Thisconceptwasnotimplemented,butitservedasreferencepointforthreeinstrumentconceptsdevelopedindependentlyatJPLandGSFCforsubmissiontotheEarthVentureMissionandInstrumentprograms.Ingeneral,theseeffortsweredrivenbytheneedtoachievesomeofthemostchallengingscientificgoalssetbytheACESWGwitharchitecturesthatrequirelessresourcesthanthelargeantennaversionsof3CPRandWiSCR.Thedevelopmentoftherespectiveproposalshasenabledfocusing

onseveralkeytechnologiestoexploreimplementationoflow-costcloudandprecipitationradars;however,giventheconstraintsandprioritiesoftheEVprogramultimatelynoneofthesehasmovedbeyondconceptformulation.

Figure 3.2a: The RainCube progression from concept development and laboratory demonstration to the subsystems, to airbone demonstration of the measurement, to instrument implementation and integration in a 6U cubesat (Tyvak), to launch under InVEST. Ongoing efforts under SBIR aim at bringing a ultra-lightweight deployable 1-m antenna at TRL 6 by 2019 (scalable up to to 2 m).

Duringthesameportionofthelastdecade,anewdisruptivemeasurementconceptwasdevelopedtoaddressthescientificneedtoobservestormdynamicsandenergetics(seee.g.,ACEtargetedGeophysicalParametersGP1,GP7andGP9intheScienceTraceabilityMatrix):theconceptistoenableglobalobservationofthetemporalevolutionoftheverticalstructureofstormsatatime-scalethatisrelevanttotheprocessofinterest(thatis,minutes),ratherthan,orinconjunctionwith,instantaneoussnaphotsofradarreflectivity(thatis,snapshotsofthestructureintheclassicalfashionofTRMM,CloudSatandGPM)andDopplervelocity(forinstantaneousshapshotsofthestormdynamics).ThisconceptenvisionsanumberofsmallplatformsinLowEarthOrbitwithdownwardlookingradars,complementingasimilarconstellationofsmallmicrowaveradiometersinafashionsimilartoGPM.Thesesmallplatformscanbearrangedintrainsalongoneorbitalplane(tocapturetheshorttimescaleevolution)and/orondifferentorbitalplanes(toimprovethesamplingofthediurnalcyclewithinasufficientlysmalltemporal

window,suchasamonth).Thisconceptfillsagapleftopenbygroundbasedradarnetworks(limitedinglobalcoveragesincetheymostlyoverland,andoverdevelopedcountries),andGeostationaryEarthOrbitradarconcepts(whichdemandextremelylargeantennasandarelimitedintheirzonalcoverage).Implementationofthisconcepthadtobesetasideuntillastdecade,becauseofthesignificantcostofimplementationandaccesstospaceforasinglecloudorprecipitationradarinstrument.Fourkeyfactorsenabledthisconceptintheearly2010’s:a)maturationoftechnologiesthatallowtominiaturizetheradarantennaandtheelectronicsubsystems,b)arrivalofthesmall-satelliteandlow-costlaunchoptions,c)definitionofanewsimplifiedradararchitectureandwaveformgenerationschemethatreducethenumberofpartsbytwoordersofmagnitudewithrespecttopredecessorspacebornecloudandprecipitationradarsand,d)theoccurrenceoftheACEworkshopsanddefinitionofitsScienceTraceabilityMatrixthatprovidedclearscientificobjectivestomotivateengineerstoevenlookintowhatappearedinitiallytobeadauntingchallenge.Initialstudiesonexpectedperformanceofaggressivesolutionsinthearenaofhigh-puritysignalprocessingweredirectlysupportedbyACE(Beauchampetal.2017).Theresultofthischallenge,todate,isthattheRainCubetechnologydemonstration(6-UcubesatwithaKa-bandnadirpointingprecipitationprofilingradarimplementedundertheInVESTprogram,seeFigure3.2a)wasdeployedfromtheInternationalSpaceStationinJuly2018andattheendofAugust2018itachieveditsprimaryobjectivebydemonstratingsuccessfullyprofilingofthunderstormsovertheSierraMadreOrientalinMexico.RainCubeenteredinextendedmissionandiscontinuingtoacquiredata.RainCubedemonstratedafewitemsdirectlyrelevanttoACE:a)theultra-compactbackendarchitecture(whichincludesthedigitalsystemandtheup-/down-conversionunits,performingrealtimeultra-lowrangesidelobepulsecompression,withdirectmodulationanddemodulationbetweenbasebandandKa-band)whichcouldbeinheritedbyanyfuturecloudandprecipitationradars,hencereducingthesize,weightandpowerofthedigitalsubsystemandup/downconversionassemblies),b)thespecificwaveformandfilteringforpulsecompression(designedkeepinginmindtheACEradarrequirementofconfiningthegroundcluttertoonly500mabovethesurface),andc)onefirstversionofanultra-compactlightweightdeployable0.5mKa-bandantennaforradarapplications(atechnologycurrentlyunderfurtherdevelopmentunderESTOandSBIRfundingtoachievelargerapertureswhichwouldbedirectlyapplicabletotheACErequirements).TheTRLoftheseelementsis6atthetimeofwriting(havinggonethroughfullhardwarequalificationforflightandairborneflightdemonstrationofthesignalandprocessingschemeduringthePECAN’15fieldexperiment)andisexpectedtobecome9beforetheendofSummer2018.

Figure 3.2b: Multi-Application Small-satellite Tri-band Radar (MASTR and airMASTR) instrument concept. It enables measurements of cloud properties and precipitation, sea ice and snowpack thickness, as well as ocean surface winds if used on a spinning scanning platform.

PortionofthetechnologydevelopedforRainCube(thatis,theback-endarchitecture)hasalreadybeenreusedtowardsthespecificneedsofACE(orCCP)bybeingintegratedinaselectedIIP’17project:theMulti-ApplicationSmall-satelliteTri-bandRadar(MASTR,PI:MauricioSanchez-Barbetty,seeFigure3.2b).Inessence,thisinstrumentconceptunifiesthe3CPRfrontendtechnologiesdescribedearlier(i.e.,Ku-,Ka-andW-bandActiveLineArrayFeedsandsinglycurvedparabolicreflector)withtheRainCubesignalarchitecturetodeliveraninstrumentthatcanaddressboththecloudandprecipitationmeasurementsaswellasinnovativealtimetricmeasurementsfocusingonseaicefreeboardandthethicknessofthesnowpackaboveit,snowpackoverground,or,ifinstalledinaspinningscanningplatform,scatterometricmeasurementsforoceansurfacewinds.Becauseoftheminiaturizednatureofeachsubsystem,andthemodularscalabilityoftheALAF,thisinstrumentcanbescaledtoantennasizesrangingfrom0.3mto3m,makingitsuitableforavarietyofaccommodationsaccordingtospecificinstrumentperformanceandSWAPallocations(rangingfrom6Ucubesats,tobusescapabletoaccommodateinstrumentsofafew100kgmassandrequiringintheorderof1000W),andincludinganyorallofthe3bands.Theairborneprototype(airMASTR)isscheduledforcompletionunderthisIIPin2019andfirsttestflightsareplanned

forNovember2019.ThiswilldemonstratethemeasurementcapabilityofthefullinstrumentandelevatingtheinstrumentTRLto5.Thekeysubsystemsarealsoplannedtobetestedinarelevantenvironmentunderseparateeffortsin2018-2019(i.e.,thermal-vacuumtestingfortheALAFunderthe3CPRIIP,andLowEarthOrbitenvironmentfortheRainCubebackenditself).Oncethesetasksarecompleted,nofurthertechnologicaldevelopmentisnecessarytomovetoengineeringandfull-scaleinstrumentimplementation.TheonlyadditionaltechnologicaldevelopmentsthatarecurrentlyplannedorenvisionedaretofurtherimprovethecurrentMASTRperformanceorfurtherreducemissioncost.Theseincludesolidstatetechnologywithimprovedpowerefficiencytoreducepowerconsumption,andultra-lightweightdeployablesinglyreflectorsuitableforW-bandoperationtoreducemassandvolumeat-launch.SpecifictailoringoffutureengineeringeffortsdependsentirelyontheguidelinesandrequirementsthatwillbeproducedbytheACCPstudies.

Use Airborne campaigns to demonstrate some of the innovative solutions, develop algorithms, and refine science requirements vs goals. InordertoenabletheacquisitionofobservationaldatasetsspecificallytailoredtoadvanceACEsciencedefinitionaswellasalgorithmdevelopment,NASA’sairbornecloudandprecipitationradarcapabilitieshavebeenaugmentedasfollows.Forthehigh-altitudeplatforms(ER-2andGH)theexistingGSFCradars(PI:G.Heymsfield)havebeenupgradedandre-engineeredtoenablesimultaneousobservationsattheACEfrequencies(i.e.,Ka-andW-band)plusothersupportingfrequencies(i.e.,X-andKu-band)toprovideacompleteviewofcloudandprecipitationsystems.Mostnotably,theCRS(W-bandnadirDoppler),HIWRAP(Ku-andKa-bandnadirDoppler)andEXRAD(X-bandscanningDoppler)haveflownintheRADEX-14/IPHExexperimenttoprovidethefirst-ever4-frequencyairborneradardatasetofcloudsandprecipitation(seeoneexampleinFigure3.3aand3.3.b).Forthemid-altitudeplatforms(DC-8andP-3)theexistingJPLradarsAPR-2(Ku-andKa-band)andACR(W-band),PI:S.Durden,S.TanelliandS.Dinardo,werereengineeredundertheAITT’14programtoradiatethroughasingleantennatoenablecollocatedscanningacquisitionatKu-,Ka-andW-bandfortheviewbelowtheaircraft,andfixedzenithacquisitionatKa-andW-band.Inthisarchitecturethepre-existinghardwareofthetwoinstrumentswasinterfacedandthecontrolsoftwareinACRmodifiedsothatAPR-3couldoperateasmasterandACRasslaveinordertoenablebothindependentandmaster-slaveoperation.TheresultingAPR-33-frequencyDopplerscanningpolarimetriccloudandprecipitationradarsystemwasscheduledtobecompletedbyApril2016undertheAITTprogram.WhileAPR-2(Ku-andKa-band)wasbaselinedtoparticipateonboardtheNASADC-8intheOLYMPEXGVexperimentinNovember/December2015,whenACEidentifiedaugmentationofthatexperimenttoachievethegoalsofRADEX-15,anewprioritywassettoacceleratecompletionofAPR-3toenableacquisitionofalsoW-bandmeasurementsinthatparticularexperiment.ThisacceleratedschedulewasmetthankstoACEsupport,andAPR-2successfullyacquiredthefirstevertriplefrequencyscanningradardatasetofcloudsandprecipitation.TheRADEX/OLYMPEXcombinedradar

datasetsfromtheER-2andDC-8platforms,jointlywiththesignificantamountofinsitu(airborneandgroundbased)andgroundbasedremotesensingdatahavebeenalreadyrecognizedbythescientificcommunityasanunparalleledtroveofofinformationoncold-seasonandorographicprecipitatingevents,withseveralpapersalreadypublishedandmorethanadozengroupsactivelyworkingonthematpresent(e.g.,Chaseetal.2018,Houzeatal.2017,Heymsfieldetal.2017).

Figure 3.3a: ACE augmented two GPM Validation field experiments (IPHEX'14 and OLYMPEX'15) specifically to acquire remote sensing datasets focused primarily on multi-frequency radar. These were to be used to demonstrate the capabilities and limitations, and to support algorithm development for multi-frequency radar observations of clouds and precipitation. The two ACE contributions were named RADEX'14 and RADEX'15, respectively, and brought to the field X-, Ku-, Ka- and W-band Doppler radar on the NASA ER-2 (in RADEX'14) and on both the NASA DC-8 and ER-2 (in RADEX'15), which delivered the most comprehensive dataset of airborne multi-frequency radar data on cloud and precipitation to date.

AnotherairbornedatasetofinterestforACEwasacquiredduringtheStudiesofEmissionsandAtmosphericComposition,CloudsandClimateCouplingbyRegionalSurveysSEAC4RSfieldexperiment(Aug/Sept2013)bytheAPR-2instrument(seeFigure3.3c).Thatdatasetsfocusesoncumuluscongestusobservationsoverland,anddeepconvectivestormsovertheGulfofMexico.ItincludedonlytheAPR-2Ku-/Ka-bandradar,primarilybecausetherewasnopossibilitytoaccommodateanadditionalW-bandradarsuchasACR(thisbeingakeyfactormotivatingthecreationofAPR-3)andthereforecouldaddressonlytheprecipitationaspectsofthe

aerosol-cloudinteraction(seee.g.,Heathetal.2017).AnaturalupgradeandextensionofthisresearchisplannedfortheCAMP2Exfielddeployment(currentlyplannedforthesummerof2019)whereAPR-3isselectedtooperatefromtheNASAP-3.FurtherairborneradardatasetsacquiredinthelastdecadethatareadvancingthescienceofACEarethoseacquiredinthecontextoftheORACLESEarthVentureSuborbitalmission(PI:J.Redemann).

Figure 3.3b: Example of data collected in one of the ACE-specific flights during IPHEX/RADEX (NC, May-June 2014): May 28, Oceanic Cumulus Congestus. Top: 3 of the radar channels from the ER-2; lower left: view from NEXRAD coastal weather radar; lower right: 4 of the radiometric channels from ER-2. (N.B. all ER-2 data are preliminary uncalibrated quicklooks). UND citation performed several penetrations of the cloud imaged here at various altitudes to capture the evolving microphysics.

Figure 3.3c: Example of data collected in one of the Convection-focused flights during SEAC4RS (TX, Aug-Sept 2013): Aug 23, Cumulus Congestus over Alabama. Top left: DC-8 forward camera view of the cloud of interest, Lower left: example of in situ particle probe imagery for the upper portion of the congestus cloud; right: APR-2 scans of the convective cloud (3 of the 6 calibrated L1 products shown here). Similar datasets are expected from RADEX-15/OLYMPEX with the addition of the W-band channel.

3.2 Polarimeters

AirMSPI/MSPI

ThemostsignificantMultiangleSpectroPolarimetricImager(MSPI)/AirborneMSPI(AirMSPI)advancementsrelevanttoACEhavebeenachievedunderESTO’sInstrumentIncubatorProgram(IIP)andtheAirborneInstrumentTechnologyTransition(AITT)program.Specifically,IIP-04,IIP-07,andIIP-10supportfromESTOhasbeenusedtoadvancethetechnologyreadinesslevel(TRL)ofthekeyMSPIsubsystems,andAITThassupportedairborneflighttesting.

ThekeytoaccuratepolarimetryintheMSPImeasurementapproachisrapidrotationoftheplaneoflinearpolarization(withouttheuseofmovingparts)coupledwithsynchronousdemodulationoftheresultingsignals.Utilizationofpolarizationmodulationasahighlysensitivemeasurementmethodologyhasbeenpioneeredbythesolarandstellarastronomycommunities(e.g.,Poveletal.,1990;Tinbergen,1996),andtheMSPItechnologydevelopmentefforthasadaptedthisapproachtomeetACEsciencerequirements.Therearetwocriticaltechnologycomponentstothisscheme:(1)aretardancemodulatortorapidlyrotatetheplaneofpolarization,comprisedofapairofphotoelasticmodulators(PEMs)andachromatic,athermalizedquarter-waveplates(QWPs),and(2)aspecializedfocalplaneconsistingofstripefilterswithpatternedwiregridpolarizerstoprovidespectralandpolarizationselectionforthedetectorlinearrays,anddetectorreadoutintegratedcircuitsthatsamplethemodulatedsignalswithhighspeedandlownoise

(Dineretal.,2007,2010).BecausethePEMsaremadeoffusedsilica,theyefficientlytransmitlightfromtheultraviolet(UV)throughthevisible/near-infrared(VNIR)andshortwaveinfrared(SWIR).Areflectivetelescopedesignenablesopticalimagingthroughoutthisspectralrangeandminimizesinstrumentalpolarization.

SupportunderIIP-04ledtotheconstructionofaground-basedcamera,GroundMSPI,whichdemonstratedthebasicmeasurementconcept.GroundMSPIhasbeenusedtoexplorethepolarimetricandangularreflectancepropertiesofterrestrialsurfacestohelpconstrainthelowerboundaryconditionforaerosolretrievals(Dineretal.,2012).UnderAITT,asecondcamerawasassembledandintegratedintotheNASAER-2high-altitudeaircraft,usingthehousingandelectronicsrackoriginallybuiltforAirMISR.Theresultinginstrument,namedAirMSPI(Dineretal.,2013a),hasbeenflyingontheER-2since2010,andparticipatedinseveralfieldcampaigns,includingPODEX(2013)(Dineretal.,2013b;VanHartenetal.,2018;Knobelspiesseetal.,2019),SEAC4RS(2013)(VanHartenetal.,2018),pre-HyspIRI(2014),pre-PACE(2014),CalWater-2(2015),RADEX/OLYMPEX(2015),SPEX-PR(2016),ImPACT-PM(2016)(Kalashnikovaetal.,2018),ORACLES(2016)(Xuetal.,2018),andACEPOL(2017).AirMSPILevel1dataproductshavebeendeliveredtotheNASALangleyAtmosphericScienceDataCenterforpublicdistribution,alongwithsupportingUserGuide,QualityStatement,andDataProductSpecificationdocuments,seehttps://eosweb.larc.nasa.gov/project/airmspi/airmspi_table.

Thesecond-generationAirMSPI-2instrument,developedunderIIP-07andIIP-10,extendsthemeasurementsintotheSWIRandaddsbandcenterandwingchannelsfortheO2A-band.ThesuiteofcurrentlyoperationalMSPIinstrumentsisshowninFigure3.4.TheAirMSPI-2instrument,currentlyundergoingimprovementstothevacuumsystemwithAITTsupport,isshowninFigure3.5.

Figure 3.4: LabMSPI, GroundMSPI Camera GroundMSPI on Tripod AirMSPI Camera & Housing AirMSPI mounted in the nose of the NASA ER-2.

Figure 3.5: Two views of the JPL AirMSPI-2 instrument. AirMSPI-2 was integrated, calibrated, and tested in flight on the ER-2 in 2015. AirMSPI-2 is currently undergoing improvements to the vacuum system at JPL.

TherearethreemainstepsinvolvedinmaintainingpolarimetricaccuracyoftheMSPIinstruments.Thefirststepisalaboratorycalibrationtoaccountforopticalpolarizationaberrationswithinthecamera.Anexampleofthisismirrordiattenuation(differentreflectanceforp-ands-polarization).TheseaberrationsleadtocrosstalkbetweentheintensityandlinearStokesparametersI,Q,andU.ThenecessarycalibrationcoefficientsaredeterminedbyconstructingaPolarizationStateGenerator(PSG),alaboratoryinstrumentcapableofgeneratingaccuratelycalibratedlinearpolarizationinanyorientation.InIIP-10,anearlierversionofthePSG(MahlerandChipman,2011)wasupgradedtoachieveanuncertaintyinthedegreeoflinearpolarization(DOLP)outputof<2x10-4,i.e.,morethananorderofmagnitudebetterthantheACErequirement.ThishighaccuracyisnecessaryinordertoaccuratelyassessthecapabilitiesoftheMSPIimagingpolarimeter.Fullypolarized(DOLP=1.0),partiallypolarized(DOLP=0.01,0.05,0.10,0.20),orunpolarized(DOLP=0.0)lightgeneratedbytheupgradedPSGwasviewedbyAirMSPItogenerateasetofpolarimetriccalibrationcoefficientsthatcompensateforinstrumentalpolarizationaberrations(Dineretal.,2010;VanHartenetal.,2018).AsshowninFigure3.6,systematicerrorsinDOLPdeterminedfromAirMSPIare~0.001,implyingthatrandommeasurementnoise(primarilyduetophotonshotnoise)dominatesthetotalDOLPuncertainty.AirMSPIsignal-to-noiseratiosaresufficientlyhightoenablemeetingtheACErequirementonDOLPerror(i.e.,within±0.005)ona20mx20mspatialscale.

Thesecondstepinvolvesin-flightmaintenanceofthePEMoperatingparameters.ThisisaccomplishedusinganopticalprobebuiltintotheAirMSPIandAirMSPI-2cameras.AbeamoflightfromanLEDispolarizedandsentthroughthedualPEMsatalocationnotusedforimaging,andthemodulationsaresensedwithahigh-speedphotodiode.AnalysisofthesignalsallowsdeterminationoftheretardancesofthetwoPEMsandthePEMoscillationphase.Thesearecontrolledtothedesiredvalues(thesamevaluesasusedforlaboratorycalibration)usingafeedbackcontrolloop.Theopticalprobeisinconjunctionwiththethirdstep,whichisperiodicin-flightverificationofPEMretardancesandphasebyviewinganon-boardpolarizationvalidator,consistingofasetofLEDsthatilluminateadiffusepanelandpolarizersindifferentorientations.ByviewingthevalidatorwiththeAirMSPIcameraduringflight,themodulationfunctionsusedtoanalyzepolarizationdatacanbedetermined

andverifiedtobegovernedbythepropervaluesofthePEMoperatingparameters.Deviationsfromthedesiredvaluescanbecorrectedingrounddataprocessing.

Figure 3.6: Laboratory polarization calibration of AirMSPI using the PSG keeps systematic errors in DOLP well below the ACE requirement.

Acustomdual-PEMretardancemodulatorpackagewasengineeredandbuilttowithstandlaunchloads,andwasvibratedinallthreeaxesat15gRMS.PEMfunctionalitywasretestedtoverifythattherehadbeennodamagetothebondlineholdingthePEMheadtothepiezoelectrictransducer.PEMretardancestabilitywastestedinthelaboratoryatanumberoffixedsetpointtemperaturesfrom-30°Cto+50°C.Inspace,thePEMswillbethermallystabilized.Inaddition,adualPEMoperatedinthelabnearlycontinuouslyformorethan8years.TheachromaticQWPsarecompoundretarderscomprisedofthreematerials(quartz,sapphire,andMgF2)thatareoftenusedinspaceapplications.IIP-07workextendedtheperformanceoftheQWPintotheSWIR.AsimilarcompoundQWPforOCO-3demonstratedsurvivabilityofthebondsthroughthermalcyclinginvacuumbetween-20°Cand35°C.VibrationtestingoftheOCO-3articleshowednovibration-inducedstructuraldefects.

TheMSPIspectropolarimetricfiltersarebutcherblockassembliesofpatternedwiregridpolarizationanalyzersandminiaturizedstripefilters.StructuralreplicatesoftheMSPIfilterswererunthroughthermalstresstestsinvacuum,consistingof123thermalcyclesbetween220Kand313Kand108additionalcyclesbetween180Kand313K.Thetestedfilterssurvivedthermalcyclingandmetbondlineintegrityrequirementswithsubstantialmargin.Theotherelementofthefocalplaneisthesensorchipassembly(SCA),consistingofthereadoutintegratedcircuit(ROIC)andhybridizedHgCdTedetectorfortheSWIR.AseparateROIConthesamechipprovidesUV/VNIRsensingusingembeddedSi-CMOSphotodiodes.TheROICsenablesamplingofthePEMmodulationpatternsattherequiredreadoutspeeds(~25Mpix/sec),leadingtophotonshot-noiselimitedsensingoverawidedynamicrange.SingleEventLatchup(SEL)testingusingheavyionbombardmentindicatesalatchupprobabilityofonceper5000years.Latchupwasalsodeterminedtobe

nondestructive,meaningthatintheunlikelyeventofoccurrence,aresetrestoresnormaloperation.TotalionizingdoseexposureoftheROICwasalsocompleted,usingtheJPLcobalt-60sourcein5kradstepsupto25krad.Alltestedpartsremainedfullyfunctional,anddarkcurrentremainedwithinspecificationsatdosescorrespondingtolowEarthorbit.Finally,characterizationofthehybridizedROIC/detectorsatoperatingtemperatureandfollowingthermalcyclingwasperformed.AnSCAunderwent100thermalcyclesbetweenroomtemperatureand235K,andwassubjectedtoanadditional30cyclesbetweenroomtemperatureand180K.Thepartremainedfunctionalfollowingtheseenvironmentalstresses.

Theaboveenvironmentalstressesrepresent“relevantenvironment”qualificationtestingofallkeyMSPItechnologies,includingtheretardancemodulatorandspecializedfocalplane.Asaconsequence,eachofthesesubassembliesiscurrentlyatTRL5.Inaddition,theMSPIonboardprocessingalgorithmthatconvertsthesampledmodulationsignalstolinearStokespolarizationparameterswastestedaboardtheCubeSatOn-boardprocessingValidationExperiment-2(COVE-2),providingthefirstspaceborneapplicationofanewradiation-hardenedVirtex-5QVfieldprogrammablegatearray(FPGA).COVE-2waslaunchedinDecember2013.Telemetrydemonstratedsuccessfulprocessing,bringingthematurityofthiskeycomponenttoTRL7(Pingree,2014).

Figure 3.7: Top left: retardance modulator. Top right: filter assembly. Bottom left: ROICs and detectors. Bottom right: COVE payload with Virtex-5QV FPGA.

KeytechnologycomponentsoftheMSPIsystemareshowninFigure3.7.Atupperleftisthedual-PEMretardancemodulator(includingQWPs)inaspace-qualifiedpackage.Thegreenassemblyattopistheopticalprobe.Atupperrightisafront-andback-litphotographoftheAirMSPI-2spectropolarimetricfiltershowingthe

stripespectralfiltersandpatternedpolarizers.LowerleftshowstheUV/VNIR/SWIRdetectorsandROICsbuiltforAirMSPI-2.LowerrightshowstheJPLCOVEpayloadfeaturingtheXilinxVirtex-5QVFPGA.

TheAirMSPI-2instrumenthasbeenintegrated;systemcharacterizationhasbeenperformed;andtheinstrumenthasbeenflighttestedontheNASAER-2inOctober2015.Theseflightsdemonstratedthefunctionalityoftheend-to-endUV/VNIR/SWIRcamerasystem,raisingtheTRLto6.

RegardingtheLevel0toLevel1processingapproachforMSPI(Jovanovicetal.,2012),ageneralizedphotogrammetrysoftwarelibrarydevelopedfortheTerraMulti-angleImagingSpectroRadiometer(MISR;Jovanovicetal.,2002)servesasthebasisforthis.Criticalfunctionalityincludescollinearity,whichmakesuseofthecamera/orbitgeometricmodeltoestablishtheviewvectorsforeachlineandpixelinthefocalplane.Itisexpandedtoincludesimultaneousbundleadjustment,whichemploysgroundcontrolpointsandadigitalelevationmodeltosolveforstaticand/ordynamicchangesincertainparametersdescribingtheinstrumentpointinggeometry.Thisfunctionality,alongwithpixel-by-pixelapplicationofradiometricandpolarimetriccalibrationcoefficients,isusedtoconvertrawinstrument(Level0)datatocalibrated,georectified,andco-registeredradianceandpolarizationimageryatLevel1,andhasbeenprototypedforACEusingAirMSPI.InadditiontoMISR-likeLevel0toLevel1processingthatgeneratesellipsoid-projectedimagery,georectifiedimagerymap-projectedtothesurfaceterrainisusedasinputtoaerosolretrievals(Figure3.8a).AsimilarapproachisenvisionedforMSPI,andhasbeenprototypedusingAirMSPIdata.

Figure 3.8a: Example AirMSPI imagery over Leland, MS, acquired on 9 September 2013 during SEAC4RS. Left: Intensity imagery at 445, 555, and 660 nm. Middle: False color intensity imagery at 470, 660, and 865 nm. Right: DOLP image at 470, 660, and 865 nm. Georectification provides subpixel registration of the different instrument channels as well as registration of images acquired at different angles of view.

TheResearchScanningPolarimeter(RSP)isafunctionalprototypeoftheAerosolPolarimetrySensorthatflewontheNASAGlorymission,whichfailedtoreachorbit.ThemeasurementconceptusedinthissensorhasalongheritagestartingwiththeImagingPhotoPolarimeteronPioneer10and11thentheCloudPhotoPolarimeteronPioneerVenusandmorerecentlythePhotoPolarimeterRadiometeronGalileo.ThedevelopmentoftheRSPhasbeenachievedwithsupportfromtheNASARadiationScienceProgram,ESTO'sAITTprogram,theGlorymissionandcontributionsfromSpecTIRLLC,thecompanythatbuilttheRSP.

ThemajordifferencebetweenRSPandprecedingplanetaryinstrumentsistheimplementationofarotatingpairofmirrorsinfrontofthetelescopesthatprovidescenedefinitionandspectralandpolarimetricanalysis.Thisallowsthefieldofviewoftheinstrumenttobescannedwhileintroducingnegligiblysmallamountsofinstrumentalpolarizationintotheobservedscene.Thescanningsystemalsoallowswellcharacterizedscenesofbothlow(usingapseudo-depolarizer)andhigh(usingpolarizers)polarizationtobeobservedoneveryscanprovidingcontinuouspolarimetriccalibrationandguaranteedpolarimetricaccuracyovertheentirerangeofpossiblepolarizationstates,inadditiontocontinuousradiometriccalibration/stabilitymonitoring.Thisensuresthatmeasurementsofthedegreeoflinearpolarizationaremadewithanabsoluteuncertaintyoflessthan0.2%absoluteaccuracywhenthedegreeofpolarizationislessthan20%andlessthan0.5%whenthedegreeofpolarizationisgreaterthan20%.

Thepolarizationcompensatedscanmirrorassemblyscansthefieldsofviewofsixbore-sighted,refractivetelescopes,withaninstantaneousfieldofviewof14mrad,toobtainscenedataoverarangeof+/-60°fromthenormalwithrespecttotheinstrumentbaseplate.Therefractivetelescopesarepaired,witheachpairmakingmeasurementsinthreespectralbands.Onetelescopeineachpairmakessimultaneousmeasurementsofthelinearpolarizationcomponentsoftheintensityinorthogonalplanesat0°and90°tothemeridionalplaneoftheinstrument,whiletheothertelescopesimultaneouslymeasuresequivalentintensitiesinorthogonalplanesat45°and135°.Thisapproachensuresthatthepolarizationsignalisnotcontaminatedbysceneintensityvariationsduringthecourseofthepolarizationmeasurements,whichcouldcreatefalsepolarization.Thesemeasurementsineachinstantaneousfieldofviewinascanprovidethesimultaneousdeterminationoftheintensity,andthedegreeandazimuthoflinearpolarizationinallninespectralbands.

Theinstrumenthasninespectralchannelsthataredividedintotwogroupsbasedonthetypeofdetectorused:visible/nearinfrared(VNIR)bandsat410(30),470(20),550(20),670(20),865(20)and960(20)nmandshortwaveinfrared(SWIR)bandsat1590(60),1880(90),and2260(120)nm.Theparentheticfiguresarethefullwidthathalfmaximum(FWHM)bandwidthsofthespectralbands.Thesespectralbandssamplethespectrumofreflectedsolarradiationovermostofthe

radiativelysignificantrange,withmeasurementsundertypicalclearskyconditionsrangingfromsignificantRayleighscattering(410nm)tosinglescatteringbyaerosol(2260nm)withinasinglemeasurementset.

Thedesiredpolarization-insensitivescanningfunctionoftheRSPisachievedbytheuseofatwo-mirrorsystemwiththemirrorsorientedsuchthatanypolarizationintroducedatthefirstreflectioniscompensatedforbythesecondreflection.Bore-sightedrefractivetelescopesdefinethe14mradfieldofviewoftheRSP.Dichroicbeamsplittersareusedforspectralselection,interferencefiltersdefinethespectralband-passesandWollastonprismsspatiallyseparatetheorthogonalpolarizationsontothepairsofdetectors.ThedetectorsfortheVNIRwavelengthsarepairsofUV-enhancedsiliconphotodiodes.ThedetectorsfortheSWIRwavelengthsarepairsofHgCdTephotodiodeswitha2500nmcutoffthatarecooledto163Ktooptimizeperformance.Theaveragedatarateof110kbpsprovidesreadoutofthe36signalchannelstogetherwithinstrumentstatusdataatascanrateof71.3rpmandissimilartothedataratefromAPS.Ascanrateof~70rpmiscompatiblewithgettingcontiguous(nadirviewtonadirview)coveragewithaircraftrangingfromaCessna210totheNASAER-2.Itisalsocompatiblewiththevelocityandaltitudeofatypicallowearthorbitforthe8mradIFOVofaninstrumentsuchasAPS.

TheRSPinstrumentwasdesignedtomeetthescientificrequirementsforhighqualitypolarimetricdata,byhavinghighaccuracy,simultaneouscollectionofallpolarizationcomponentsandspectralbandswithinaninstantaneousfieldofview,theabilitytoobserveascenefrommultipleanglesandabroadspectralrange.TheRSPinstrumentmeetsthepolarimetricaccuracyrequirements(lessthanabsolute0.2%error)andhasbeenusedtoobtainmorethanathousandhoursofmulti-angle,multi-spectraldatasince2000.InstrumentperformancehasbeenflawlessandithasbeenoperatedonawiderangeofaircraftmostrecentlytheNASALangleyResearchCenterB200since2008andtheNASAER-2since2012.Allradianceandpolarizationdataarepubliclyavailableandisgenerallycalibratedandmadepublicwithin2-3daysofacquisition.FundingforflightsoftheRSPcamefromtheGloryandCALIPSOmissionsandtheResearchandAnalysisprograms,primarilytheRadiationScienceProgramthroughsupportofRSPdeploymentforSEAC4RSandtheOceanBiologyandBiogeochemistryprogramthroughsupportoftheRSPdeploymentforSABORandonHySPIRIairbornepreparatoryprogramflights.

TheRSPgroupparticipatedintheEarthSystematicMissionDirectorateprogramoffice’sSystemsEngineeringWorkingGroup(SEWG)assessmentofTechnologyReadinessLevel(TRL)foranAerosolPolarimetrySensor(APS)rebuild.TheassessmentwasthatthesensorhadaTRLof7andwhiletherearealwaysdisagreementsabouttheexactTRLofacompletesystemandthedefinitionofTRLoccasionallychangesitisclearthattheAPSisamaturedesignwithsubstantialdesignheritage.InparticulartheAPSsuccessfullycompletedbothsensorlevelandobservatorylevelEMI/EMC,vibration,thermal/vacuumandshocktestingwithatotalofmorethan1200operationalhoursinthermal/vacuumtesting.ThesuccessfulperformanceduringsensorleveltestingisdocumentedintheRaytheon

RequirementsVerificationMatrixandsupportingdocumentation,togetherwiththeConsenttoShipReviewpackage.ThesuccessfulperformanceduringobservatoryleveltestingisdocumentedintheobservatoryPre-ShipReviewpackageandsupportingOrbitalScienceCorporationrequirementsverificationdocumentation.

TheonlylikelychangetotheAPSdesignfortheACEmissionwouldbeifacleanviewtodeepspacewerenottobeavailableforcooling.InthatcasetwoThermoElectricCoolers,acoldplate,redundantethaneheatpipes,andaradiatorcanbeusedtomaintaintheSWIRdetectorsatatemperatureof183K±2K.AsimilarthermalsystemwasflownonSwiftandmorerecentlyontheLandsatDataContinuityMission(LDCM)ThermalInfraredSensor(TIRS),providingdesignheritage.AmodelofthisprovendesignwasassembledandsuccessfullytestedunderGSFCInternalResearchandDevelopment(IRAD)fundingtodemonstratefeasibility.Inadditiontoprovidingproof-of-conceptforthisspecificapplication,theprototypeprovidedrealisticmassandpowerestimatesandwillallowforthesizingoftheSWIRHeatRejectionRadiatorearlyinACEmissiondevelopment.

TheLevel0toLevel1processingofRSPdatafollowsthesameflowintermsofrequiredcalibrationcoefficientsandtheiron-boardcalibrationsources,aspresentedintheGloryAPSL1BAlgorithmTheoreticalBasisDocument(http://glory.giss.nasa.gov).ThesecoefficientsareusedtogeneratethecalibratedStokesparametersI,QandUandthecodedevelopedfortheGloryprojectisusedforgeolocationandgeo-rectification.Inaddition,theL1BdataproductincludesindexarraysthatcanbeusedtoremaptheRSPdatatoanyaltitude,simplifyingtheimplementationofcloudretrievals.AL1Cproductisalsoprovidedforwhichthisremappingtocloudtop,ortothesurfaceincloud-freecases,isalreadyapplied.

PACS/HARP

ThePassiveAerosolandCloudSuite(PACS)isamodularconceptwithmultiplepassiveimagerssidebysiderangingfromUVtoTIRwavelengths.Aspartofthisconcept,theHARP(Hyper-AngularRainbowPolarimeter)isacompactandrobustimagingpolarimeterwithnooperationalmovingparts(exceptforinternalcalibrators).HARPisdesignedforthreewavelengthrangescoveringfrom350to2250nm:HARPUV,HARPVNIR,andHARPSWIR.

AHARPVNIRpolarimetermodulewasbuiltforspaceapplicationsundertheHARPCubesatprojectfundedundertheNASAESTOInVestprogram.HARPiscurrentlyslatedtobethefirsthyper-angularpolarizationimagerflyinginspace(Martinsetal.2018;Duboviketal.2018).

IntheHARPdesign,eachtelescopehasatelecentricbackendandaPhilipsprismthatsplitsthesignalintothreeidenticalimagesoverthreeindependentdetectorarrayscontrolledbyasingleFPGAelectronics.HARP’sFPGAhasbeenfullydevelopedtoperformalldataacquisitionandrequiredprocessinginordertoeliminatetheneedforanonboardinstrumentcomputer,substantiallyreducingcost,mass,powerconsumptionandrisk.Wavelengthsandanglesaredefined(and

softwareselectable)byastripedfiltermountedonthesurfaceofthedetector.Foreachviewingangleandwavelength,theHARPpolarimeterprovides3intensityimagesacquiredthroughpolarizersalignedat0,45and90ºrelativetoeachother.These3imagesarerelatedtotheStokesvectoroftheincominglightbya3X3characteristicmatrixthatfullyrepresentsalltheelementsoftheopticalsystem(Fernandez-Bordaetal.,2009).Thesimultaneousmeasurementsofthe3polarizationorientationsassuresthehighaccuracyofthemeasurements.ThisapproachhasbeendemonstratedandvalidatedintheUMBClabandonthreeaircraftcampaigns(PODEX,LMOSandACEPOL)confirmingapolarizationaccuracybetterthan0.5%.Thismeasurementaccuracyhasbeenvalidatedwithapolarizationgeneratorthatcanmodulatethedegreeoflinearpolarizationofthelightgeneratedbyanunpolarizedintegratingsphereintherangeof0to60%.

DesignstudiesandTRLassessmentswerecompletedfortheHARPUVandSWIRmodulesbutnohardwareassemblythesemoduleshasbeenfundedtocompletion.TheHARPVNIRmodulehasbeenfullydevelopedinthethreeconfigurationsshowninFigure3.8b.Inallthreecases,HARPVNIRhasa94°crosstrackswathand113°alongtrackcoverageinviewingangle,fourwavelengths(440,550,670and870nm),60alongtrackviewinganglesat670nm,and20alongtrackviewinganglesfortheotherthreewavelengths.Thedetectorarrayshave2048pixelscrosstrackwhichcanbebinnedonboardforreducingthedataraterequirementsasneeded.

AirHARP

AnairborneversionoftheHARPVNIRsystemhasbeenfullyassembledandtestedontheNASALangleyUC12aircraftduringtheLMOS(LakeMichiganOzoneStudy)campaigninJune2017,andontheNASAER2aircraftduringtheACEPOLcampaigninOctober-November2017.

HARP CubeSat

HARPCubeSatwasdevelopedwithfundingfromtheNASAESTOInVESTprogramandcarriesafullversionoftheHARPVNIRtelescope,whichwasspeciallyminiaturizedforthisapplication.LimitationsontheCubeSatformfactor,mass,thermalsystem,anddataratesposeamajorchallengeforaninstrumentlikeHARPbut,nevertheless,HARPCubeSatwillprovideimportantdemonstrationofthistechnologyfromspace.Duetothedataratelimitations,onlyafewregionscanbetargetedbyHARPCubeSatinadailybasis.HARPCubeSatiscurrentlyscheduledforlaunchintheSpring2019intheISSorbit.

HARP2 for PACE

AcontributedversionofHARPisunderconstructiontojointhePACEmissionandsupplementthemeasurementsbythemainpayload,theOceanColorInstrument(OCI).HARP2isacopyoftheHARPVNIRpolarimeterfullyadaptedtoworkinalargespacecraft,withthecapabilitytoprovideglobalcoveragein2days,bettersignaltonoiseratios,andmultiplefeaturestosupportonboardcalibrationincludinganinternalflatfieldcalibrator.

Figure 3.8b: Three current configurations for the HARP VNIR polarimeter including the AirHARP instrument that has flown in the Langley UC12 and the NASA ER2 aircrafts, the HARP Cubesat scheduled for launching in the Spring 2019, and the HARP-2 instrument under construction as an add on to the PACE mission. The figure also shows a photograph of the core of the HARP VNIR telescope illustrating the small size of the telescope assembly.

3.3 Lidar Lidarverticalprofilemeasurementsofbackscatter,depolarization,andextinction,indayandnightconditions,providethesciencecommunitywiththeaerosolpropertiesthatarenecessarytocomplementpassiveaerosolretrievalsandexamineaerosolimpactsonclimateandairquality.Current/pastspace-basedlidarssuchasCloud-AerosolLidarwithOrthogonalPolarization(CALIOP)ontheCloud-AerosolLidarandInfraredPathfinderSatelliteObservations(CALIPSO)satelliteandCloud-AerosolTransportSystem(CATS)ontheInternationalSpaceStation(ISS)havebeenprovidingessentialmeasurementsofaerosolverticaldistribution(Winkeretal.,2009;McGilletal.,2015).However,intheforeseeablefuturetheavailabilityofthiscriticaldataisendangeredduetolimitedlaserlifetime.TocontinueandadvanceCALIPSOandCATSmeasurementsofaerosolverticaldistribution,alidarthatcandetectopticallythinlayerswithhighaccuracyandglobalcoverageisrequired.

Thereareseveraltypesofcloud-aerosollidars,suchassimpleelasticbackscatterlidarsandHighSpectralResolutionLidars(HSRLs),thatcanprovidethedesiredgeophysicalparameters.EarlyintheACEprogram,twolidarinstrumentconceptsweredevelopedforuseinACEmissiondesignstudies.Onewasamulti-beambackscatterlidarthatprovidedsomeinformationinthecross-trackdirectionviaapushbroom-likesamplingstrategy.Theotherwasasingle-beammulti-wavelengthHighSpectralResolutionLidar(HSRL)thatprovidedonlyanadircurtainoflidarmeasurementsbutwithhigherSNRandinformationcontentinthatcurtain.BothconceptswereanalyzedintheinitialGSFCIntegratedMissionDesignLab(IMDL)missionstudies.

InFebruary2009,theAerosolWorkingGroupmetatGSFCtorefinelidarrequirements.AgreementwasreachedthatACEaerosolrequirementscalledforimplementationofsingle-beammulti-wavelengthhighspectralresolutionlidar(HSRL)providingtheso-called“3β+2α+2δ”suiteofprofiles:3aerosolbackscatterwavelengths,2aerosolextinctionwavelengths,and2polarization-sensitivewavelengths.Cloudandoceanrequirementswerealsometbythisconcept,andthereforeitwasusedinsubsequentmissiondesignstudies(GSFCIMDLandJPLTeam-Xstudies).

OverthecourseoftheACEpre-formulationeffort,significantadvanceshavebeenmadeintechnologyreadiness,retrievaldevelopment,scientificdemonstration,andvalidation.ManyoftheadvancesfundedbyACEarebasedontheNASALaRCHSRL-2airborneprototypeinstrumentthatimplementsthefull3β+2α+2δACElidarconceptandwhichhasbeenflownoneightmajorfieldmissionsstartingin2012.Althoughmostlyfundedthroughothersources,excitingtechnologyandalgorithmadvancesforsomeoftheACEcapabilitieshavealsobeenachievedwiththeCALIOP,CATS,andtheAirborneCloud-AerosolTransportSystem(ACATS)instruments.

RequirementsontheACElidarstemfromallACESTMs(aerosols,clouds,ocean,andaerosol-oceanSTMs).ThediscussionoftheHSRLtechniqueandadvancesmadeunderACEisseparatedbelowintosectionsfocusedonatmosphere(aerosolsandclouds)andoceanrequirements.

HSRL for meeting atmospheric requirements Thewavelengthsrequiredforthe3β+2α+2δmeasurementsareUV,mid-visible,andnear-IR,whichcanbeachievedwithmaturelasertechnology(Nd:YAG,orNd:YVO4),usingthefundamental(~1064nm),doubled(~532nm),andtripled(~355nm)wavelengthsofasinglepulsedlasertransmitter.Unambiguousaerosolextinctionmeasurementsrequiredatthe355and532nmwavelengthsnecessitateuseoftheHSRLtechnique.Thiscombinationofthreebackscatterandtwoextinctionwavelengthsistheonlypublishedmethodforretrievingtherequiredverticallyresolvedaerosolopticalproperties(scatteringandabsorption)andmicrophysicalproperties(size,indexofrefraction,concentration)usingonlylidarmeasurements(Mülleretal.,2001,2002,2014;Veselovskiietal.,2002;Wandingeretal.,2002;Sawamuraetal.2017)calledforintheACESTM.Therequirementfordepolarizationmeasurementsattwowavelengthswasimposedtoprovideenhancedskillforaerosoltyping(Burtonetal.,2012,2013,2014)beyondthatusingonlybackscatterandextinctionmeasurements.Itremainstobedeterminedwhichtwoofthethreewavelengthsarerequiredfordepolarization,butheritagemeasurementswithairbornesystemshavebeenmadewiththe532and1064nmwavelengths.Studiesareunderwaytodeterminewhetherasimplerlidarcombinedwithapolarimetercansatisfytheaerosolrequirements(e.g.,Liuetal.,2017).Lidarmeasurementsrequiredforthecloudobjectivesincludecloudtopheightandprofilesofcloudphase,backscatter,andextinctionintenuousclouds.These

requirementscouldbemetwithfewerchannels(e.g.,a532nmHSRLwithpolarizationsensitivity).

AirborneprototypeshavedemonstratedtherequiredACElidartechnologiesandmeasurements.TheLaRCHSRL-2instrumentisafull-upprototypeforachievingtheACE3β+2α+2δatmosphericmeasurements.ItimplementstheHSRLtechniqueat355and532nmandthestandardbackscattertechniqueat1064nmandispolarizationsensitiveatall3wavelengths.ThedevelopmentofHSRL-2originatedwithESTOInstrumentIncubatorProgram(IIP)fundingin2004andcontinuedthroughanAirborneInstrumentTechnologyTransition(AITT)awardin2007,LaRCinternalfunding,andcurrentfundingtoextendthecapabilitytooceanprofilingunderanIIPaward.ThereceiveremploysaniodinevaporfiltertoimplementtheHSRLtechniqueat532nm(PiironenandEloranta,1994)andafield-widened,off-axisMichelsoninterferometerat355nm(Seamanetal.2015).FundingforadvancementoftheinterferometerimplementingtheHSRLtechniqueat355nmhasbeenprovidedviaanESTOQRSaward,ACEpre-formulationfunding,DirectedTechnologyandResearch(formerlyGOLD)laborsupport,andLaRCinternalfunding.HSRL-2buildsonalonghistoryoftechnologyandsciencedemonstrationofthetwo-wavelengthHSRL-1instrument(Hairetal.,2008),thedevelopmentofwhichwasinitiatedin2000andwhichhasflownon25airbornefieldmissionsstartingin2006.TheACE-prototypeHSRL-2instrumenthasbeendeployedoneightmajorairbornefieldmissionsstartingin2012.Operationalsoftwarecodeproducesfulllidar“curtains”ofACE-likeaerosolopticalandmicrophysicalpropertieswithinafewhoursaftereachflight(Mülleretal.,2014).SeveraloftheeightHSRL-2fieldmissionsinvolvedadditionalparticipatingaircraftmakinginsituaerosolmeasurementscoincidentwiththeHSRL-2measurements.Aerosolmeasurementsmadeontheparticipatingaircraft,alongwithcoincidentAERONETobservations,havebeenusedtoassessthemulti-wavelengthlidaraerosolretrievalsandthedevelopmentofnewalgorithmapproaches(e.g.,Sawamuraetal.2017).Since2015,HSRL-2iscapableofautonomousoperationandtwomajorsciencedeploymentshavebeenconductedfromtheER-2high-altitudeaircraft(seeFigure3.9).

Inaddition,theLaRCUltra-VioletDifferentialAbsorptionLidar(UVDIAL)instrument,aflagshipinstrumentflownsincethe1980sonover30chemistryfocusedfieldmissions,waswasupgradedunderanAITTawardtoincludeHSRLcapabilityat532nminboththenadirandzenithdirections.IthasflownonthreefieldmissionsinthatconfigurationandaerosoldataproductsareoperationallyproducedwithinafewhoursaftereachflightusingsoftwaremodulesfromtheHSRL-1and-2processingcode.Cirruscloudretrievalsofbackscatter,extinction,anddepolarizationhavealsobeendemonstratedwiththeUVDIAL/HSRLdataset.Also,ongoingistheHigh Altitude Lidar Observatory (HALO)program,anESTOfundedIIPbasedontheHSRL-2instrumentarchitecturetoadvanceTRLforDIALtechnologiesforwatervaporandmethanewhilesimultaneouslyprovidingHSRLcapabilityat532nm.TheHALOinstrumentdeployedontwosuccessfulseriesoftestflightssofarin2018.

ATRLassessmentofthe3β+2α+2δACElidarconceptwasconductedin2013.ThislidarconceptwasbasedsignificantlyonCALIOPheritage.TheTRLassessmentfocusedonelementsrequiringtechnologydevelopmentonlyandexcludedelementsthatcouldbedevelopedviastraightforwardengineering(e.g.,commonlydeployedelectronicsubsystems,thermalsubsystems,structures,etc.)orbasedonCALIOPdesigns.Consideringonlyatmosphericmeasurements,thereadinesslevelwasassessedatTRL-5.ThesubsystemslimitingtheTRLatthattimewerethelasertransmitterandtheinterferometricopticalfilterusedasanHSRLreceiver.Significanttechnologydevelopmenteffortshavebeenmadeinbothareassince2013.

Thelasertransmitterconsistsofaseedlasersubsystemandapulsedlaser.ThebaselinefortheseedlaseristheTRL-6TesatlaserthatisemployedontwoEuropeanSpaceAgencymissions,theALADINlidaronADM-Aeolus(launchedin2018)andtheATLIDlidaronEarthCARE(launchin2021).ThedevelopmentofaUSsourcefortheseedlaserhasbeingfosteredunderaseriesofSBIRawardstoAdvR,Inc.,thathaveincrementallyadvancedtheTRLofvariouscomponenttechnologies.

TheAdvRseedlasersubsystemconsistsofacompact,highly-efficientdirect-diodelaser,aplanarlightwavecircuitthatintegratesfrequencydoublingandphasemodulationinasinglefiber-coupledcomponent,acompactiodinecellthatprovidesanabsolutefrequencyreference,andcompactlow-noisecurrentandtemperaturecontrollersinafeedbackloopthatlocksthediodeseedlaserwavelength.Thedirect-diodelasersourceattheheartoftheseedlasersystemusesadistributed-feedbackarchitectureandisundergoingindependentspacequalificationatacomponentlevel.AdirectdiodesourcerequireslessthanhalftheelectricalpowerofthecompetingTesatsolid-statelaser(8Wvs20W),providessufficientshort-andlong-termstabilityforHSRL,andprovidesastabilizedopticaloutputpowerof10mW,sufficienttoseedaNd:YAGlaserwithouttheneedforadditionalamplification.TheAdvRseedlasersubsystemshouldreachTRL6byFY19(fundingdependent).Moreover,thistechnologyapproachisbeingfurtheradvancedbyrelateddevelopmentsundertheHALOprogram.TheperformanceoftheseedlaserdevelopedunderHALO,aswellassize,weightandpowerallmeetrequirementsforafuturespace-basedimplementation.RadiationstudiesonthehardwaredevelopedunderHALOwillbecarriedoutstartinginFY19.

ThepulsedlaserisbeingadvancedthroughtheESTOfundedHighEnergyUVDemonstrationProject(HEUVD)withFibertek,Inc.Underthisprogram,FibertekhasbuiltanEngineeringDevelopmentUnit(EDU)witharchitecturesuitableforboththeACEanddirectdetection3-DWindsDecadalSurveymissions.ThisEDUdesignwasbasedonlessonslearnedfromtheCALIPSO,CATS,andICESat-2space

laserdevelopmentprogramsandincorporatesdesignprinciplesthatinsureitwillmeetenvironmentaltestingrequirementsandlong-termoperationinspace.

AmajorconcernforthepulsedlaseristhelifetimeofthelaserintheUVwavelengths.AnassessmentofthisriskbyFibertekconcludedthatthehigh-riskcomponentwasthethirdharmonicgeneratorcrystal,specificallythecoatingontheexitfaceofthatcrystal,whichisanareaofhighUVfluenceandonwhichsmallamountsofcontaminationcanleadtodamage.AnACE-fundedprogramconductedoverseveralyearsledtothedevelopmentofcontaminationcontrolproceduresandidentificationofsuitablecoatingvendors.Usinga20kHzlasersource,acceleratedlifetestsinFY15oncrystalswiththesenewcoatingsandpreparedunderthenewcontaminationcontrolprocedureshavedemonstratedsignificantimprovementsinlifetimes:resultsshowednegligibleoutputpowerdegradationat50billionlasershots(Figure3.10),whichwouldbeequivalentto16yearsofon-orbitoperationsoftheACElidarassuminga100Hzpulserate.Theseresultsareextremelyencouragingbutalonearenotconclusive.Thisisduetothefactthatdamagemechanismsareassociatedwithdefectsinthecoatings,andthesmallbeamdiameteroftheirradiationsourceusedinthesetestsresultedinsamplingasmallareathatmayhavebeenserendipitouslyfreeofdefects.Toaddressthis,lifetestsatACE-likebeamsizeshavebeenconductedwiththeHEUVDlaseritselftobetterevaluatethehigherenergyUVlifetime.A1.5billionshottestwassuccessfullycompletedwithnodamagetothetriplingcrystalcoatingsatthe355-nmenergiesconsistentwithACErequirements(50mJ/pulse).AfinaltestattwicetheACEenergies(100mJ/pulse)isunderwayandhasachievedover1billionshotswithnodamagetotheLBOanddownstreamopticsasofthetimeofthiswriting.UVlaserlifetimeissueshavebeenaddressedbyESAforthelidarsonADM-Aeolus(launchin2018)andEarthCARE(launchin2019)andinformationfromthoseprogramsaswellasfutureon-orbitdatawillprovideadditionalinformationonUVlaserlifetime.

TheonlyremainingsteprequiredtoadvancethereadinessleveloftheHEUVDlaserheadtoTRL6isenvironmenttesting.ESTOiscurrentlyconsideringwhethertofundtherequiredenvironmentaltestsinthefinalmonthsofFY18.

ToelevatetheTRLoftheHSRLinterferometricreceiver,anadvancedinterferometerwasdevelopedforspaceapplication(Figure3.11).Thisinterferometerisbasedonaquasi-monolithicdesignwhichhasflownonthreeairborneHSRL-2fieldmissionsismorestableinfrequencyandmoremechanicallyrobustthanthepiezoelectricallycontrolledversioncurrentlyflownonearlierHSRL-2missions.AspaceflightinterferometerEDUhasbeendeveloped,thedesignofwhichisbasedonanextensivestructural-thermal-optical-performance(STOP)modelingeffortfocusedonmaximizingmechanicalrobustness,minimizingwavefronterror(insuringoptimalopticalperformanceasanHSRLopticalfilter),andenablingreliablethermaltuningoftheopticalpassbandtothelaserfrequency(insuringrobuston-orbitoperationandcontrol).LaboratorytestingshowedexcellentopticalperformanceasanHSRLopticalfilter,andvibrationtestinginFY18elevatedthereadinessleveltoTRL-6.Thisunitwasbuiltforthe355-nmwavelength,andminormodificationstothedesignarebeingimplementedina532-nmunit,whichwillbedeliveredandtestedinfallof2018.Basedonsimilarity,theHSRLinterferometricreceiverisatTRL6,regardlessofwavelength.OperationalalgorithmsforcalibrationandproductionofLevel-2aerosolextinctionandbackscatterproductsfromtheinterferometricHSRLdatahavebeendemonstratedonairbornefieldmissionssince2014.

Table 3.1.: TRL summary assuming CALIOP (TRL-9) as a basis and examining only major technology deltas from the CALIOP design and excluding subsystems falling into the category of straightforward engineering.

Subsystem Baseline Current TRL

Effort remaining to achieve TRL-6

Seed Laser Tesat 6 None: identical lasers launching on ADM Aeolus in 2018 and EarthCARE in 2019

Pulsed Laser HEUVD 5 Vibration and TVAC testing; may occur in 2018

HSRL interferometer

Quasi-monolithic Michelson

6 None: 355 nm unit passed environmental testing; 532 nm unit based on similar design

Overall CALIOP and HSRL-2 5 HEUVD environmental testing

AdvancingtheTRLcomprehensivelybydevelopingaspace-likeversionoftheentireACElidarremainsimpracticalduetocost.EliminatingfromTRLdemonstrationthoseelementswhichcandevelopedviastraightforwardengineeringandfocusinginsteadonthoseelementswhichrequiretechnologydevelopmentremainsthemostpracticalapproachforaninstrumentlikethelidar.Followingthisapproach,theTRLoftheACElidarremainsat5withtheprospectofincreasingtoTRL-6withinlessthanayear.ThehighTRLisbasedonspaceborneandairbornelidardemonstrationsandtechnologydevelopmentdoneunderACEandotherNASAfunding.Forinstance,thedeploymentofCALIOPonCALIPSOandCATSonISSdemonstratedsomeofthe

capabilitiesrequiredfromtheACElidarandshowedthatlong-termoperationinspaceisfeasible(CALIOPhasbeenoperatingfor12yearsonorbit).Full-upairborneprototypesfeaturingallofthecapabilitiesrequiredfortheACElidarhavebeendevelopedandtheirmeasurementsvalidatedonnumerousairbornefieldmissions.TheTRLsummaryinTable3.1isbasedonthosetechnologieswhichrepresentdeltasfromtheCALIOPdesignandexcludeselementswhichcanbeaccomplishedviastraightforwardengineering.ThelimitingsteptoTRL6isfundingtheenvironmentaltestingoftheHEUVDlaserhead.

ThealgorithmsforprocessingACEmultiwavelengthHSRLdataareconsideredhighlymature.TheywillbebasedonalgorithmsdevelopedandemployedforCALIOPandtheairborneHSRLdataforoveradecade.TheLevel-1algorithmsforproducingattenuatedbackscatterandvolumedepolarizationwillfollowthosedevelopedforCALIOP(Winkeretal.2009).ThealgorithmsforretrievingLevel-2aerosol/cloudbackscatterandextinction,particulatedepolarization,andaerosoltypewillfollowthosedevelopedfortheairborneHSRLprogram(Hairetal.2008;Burtonetal.2012,2013,2018);however,toproduceadatarecordconsistentwiththe12-yearCALIOPrecordforpurposesoftrendstudies,CALIOP-likeLevel-2productswillalsobeproducedusingalgorithmsdevelopedforCALIOP(Winkeretal.2009).ThemoreadvancedalgorithmsforproducingtheLevel-2aerosolmicrophysicalproducts(effectiveradius,concentration,refractiveindex,andsinglescatteralbedo)werelargelyfundedunderACEandhavebeendemonstratedoperationallyusingairborneHSRL-2fielddatasince2012asdescribedinsection4.Extensivevalidationstudieshavebeenconductedthatshowthelidarretrievalsofconcentrationsandeffectiveradiicomparewellwithcorrespondingvaluesderivedfromairborneinsitumeasurements(Mülleretal.,2014;Sawamuraetal.,2017).

TheGSFCAirborneCloud-AerosolTransportSystem(ACATS)hasHSRL,standardbackscatter,andDopplerwindcapabilitiesat532nm(Yorksetal.,2014).TheACATStelescoperotatestofourdifferentlookanglesandissetatanoff-nadirviewangleof45degrees. Afterundergoingmodificationstoimproveperformanceofthetelescope,ACATSwastestedontheER-2aircraftduringAugust2015.Performancewassatisfactory,andadditionalfutureflightsareplanned.ACATSemploysaninterferometricHSRLtechniquethatisdifferentthantheNASALaRCHSRLtechnique.CATSwasdesignedtoimplementaninterferometricreceiverat532nmusingamulti-channeldetectortechniquesimilartoACATS,butissueswiththelaserstabilityprohibitedsciencequalitydata.ThehardwareforthissubsystemisatTRL6butdataproductsproducedfromthisACATSapproachrequirefurtherassessment.

HSRL for meeting ocean requirements Theoceanobjectivescallforocean-profilingHSRLmeasurementsnecessarytoretrievediffuseattenuationandparticulatebackscattercoefficients.Oceanobjectiveswouldbesatisfiedwithmeasurementsat532nm,butmeasurementsatboth355and532nmwouldbepreferred,astogethertheyallowtheseparationof

pigmentabsorptionfromabsorptionbycoloreddissolvedorganicmatter(Hostetleretal.2018).Hugeadvancesinoceanprofilingtechnologydevelopment,measurementdemonstration,algorithmdevelopment,andmeasurementvalidationweremadeunderACE,AirborneInstrumentTechnologyTransition(AITT),ESTOIIP,OceanBiologyandBiogeochemistry(OBB),andEarthVentureSuborbitalprogramsoverthecourseoftheACEpre-formulationperiod.

TheairborneHSRL-1instrumentwasfirstupgradedtoenableoceanprofilingcapabilityat532nmin2012underanAITTaward.Sincethen,ithasflownfiveocean-focusedfieldmissionsonwhichtherequiredACEoceanmeasurementsweredemonstrated.ThesedeploymentsincludeadeploymenttotheAzoresin2012(Behrenfeldetal.,2013),theOBB-sponsoredShip-AircraftBio-OpticalResearch(SABOR)mission(Hairetal.2016;Schulienetal.2017)conductedin2014,andthreedeploymentsfortheNorthAtlanticAerosolsandMarineEcosystems(NAAMES)EarthVentureSuborbitalmissionin2015,2016,and2017.

TheACEHSRLdesignconceptfortheatmosphericrequirementsrequiresverylittlemodificationtomeettheoceanrequirements(Hostetleretal.2018).Theonlymodificationinvolvesthedetectorsanddetectionelectronicstomeettherequired2-mverticalresolutionandreducepotentialsusceptibilitytoartifactscausedbystrongspecularreflectionofthelaserpulsefromtheoceansurface.Thelatterissueisthetechnologydriver,asthespecularreflectioncreatesastrongsignalpulsethatcancreateartifactsinthesubsurfaceprofile,whichinturncouldlimitthedepthtowhichtheoceanmeasurementscanbeaccuratelymade.TheairborneHSRL-1instrumentistypicallyoperatedinanoff-nadirconfigurationtoavoidreceivingthesespecularreflections,and,whileperfectlyacceptableforairbornemeasurements,operatingsignificantlyoff-nadirissuboptimalforthespaceapplication.Toaddressthisissue,considerableefforttodesignanddevelopdetectionsubsystemsimmunetotheseartifactshasbeenconductedsince2014.Onemethodusingmicrochannelplatephoto-multipliertubes(MCP-PMTs)andananalogdetectionschemeisbeingimplementedinHSRL-2aspartofthe2014IIPprojectandwillbefieldtestedin2019.Anothermethodusingmulti-anodeMCP-PMTsandaphoton-countingdetectionschemeisbeingdevelopedundera2018ESTOAdvancedComponentTechnologyaward.Labtestingofearlyprototypehardwareindicatesthatthisapproachisextremelyeffectiveandpreferredovertheanalogdetectionscheme.

TheTRLforoceanprofilingisidenticaltothatfortheatmosphericmeasurementswiththepossibleexceptionofthemodificationsrequiredforthedetectionsubsystem.TheTRLforthatsubsystemissomewhatfluid,dependingontherequiredfidelityanddepthofthemeasurement.CALIOPdatafromCALIPSOhavebeenusedinnumerousstudies(e.g.,Behrenfeldetal.2013and2017)todemonstratethescientificvalueofnear-surfaceoceanmeasurementsmadebyspacebornelidar.PMTdetectorssimilartothoseusedonCALIOPorICESat-2couldbecoupledtomaturedetectionelectronicsdesignstoprofileto1-2opticaldepths.Thisapproachfallsintothecategoryofstraightforwardengineeringratherthan

technologydevelopment.Higherfidelitymeasurementsto3opticaldepthsarelikelytorequirethemoreadvancedphoton-countingMCP-PMTapproachbeingdevelopedundertheACTproject.ThisapproachshouldreachTRL-6by2021.Notably,photon-countingMCP-PMTwillalsoprovideadvancesintheaccuracyandprecisionofatmosphericmeasurements.Itwillimprovetheprecisionofsignalsfromweakertargets(e.g.,tenuousaerosolsandmolecularcalibrationtargetsinthestratosphere)andenabletheabilitytomeasuretheprofileofextinctioninthetopsofopticallythickwaterclouds,which,whencombinedwithclouddropletsizedistributionsretrievedfromthepolarimetercloudbowdata,willprovideascientificallyimportantbenefitforcloudsciencebyenablingaccurateestimatesclouddropletnumberconcentration.

AlgorithmsforproducingtheACEoceanproducts,diffuseattenuationandparticulatebackscatterbackscattercoefficients(Hostetleretal.2018),aresimilartothosedevelopedforHSRLaerosolretrievalsofextinctionandbackscatter.Thesealgorithmsarematureandhavebeenvalidatedagainstinsitumeasurementsmadefromshipsandsatelliteoceancolorretrievals(Hairetal.2016;Schulienetal.2017).

Elastic Backscatter Lidar

Whileasimpleelasticbackscatterlidarcannotprovidehigher-orderdataproducts(i.e.,cloud/aerosolmicrophysicalproperties),itdoesprovidemostrequiredobservables(i.e.,layertop/baseheight,backscatter,depolarization).OverthecourseoftheACEpre-formulationeffort,thereweretwoelasticbackscatterlidarsoperatingfromspacethatwerenotdirectlyfundedthroughACEbutarerelevanttotheACElidarconcept.TheCALIOPlidar,managedbyNASALaRC,waslaunchedinApril2006.Foroveradecade,CALIOPhasprovidedverticalprofilesofcloudandaerosolpropertiesessentialtostudiesoftheEarth’sclimatesystem,asdemonstratedbyover2000publications.TheNASAGSFCCATSisanelasticbackscatterlidarthatoperatedfor33monthson-orbit(12Feb.2015to29Oct.2017)fromtheISS,firingover200billionlaserpulses.TheCATSinstrumentwasdesignedtodemonstratenewin-spacetechnologiesforfutureEarthSciencemissionswhilealsoprovidingpropertiesofcloudsandaerosols.TheCATSinstrumentprovidedspacebornedemonstrationofahighrepetitionratephoton-countingapproachtoelasticbackscatterlidar(Yorksetal.,2016).CATSoperatedthefirst6weeksinamodethatprovideddualwavelengthbackscatteranddepolarizationmeasurements(532and1064nm)using2beams.Afterthefirstlaserfailed,thelast31monthsofoperationwerelimitedtosinglewavelengthbackscatteranddepolarizationmeasurementsusingonebeam.

SeveraladvancesinelasticbackscatterlidaralgorithmandtechnologydevelopmentwereachievedinparallelwiththeACEpre-formulationeffort(mostlyfundedbysourcesotherthanACE).CALIPSOprocessingalgorithmsbasedonthe2b+1ddesignadvancedfromVersion1toVersion4,producingrobustdataproducts(Figure3.12).CATSalgorithmsleveragedtheheritageoftheCALIPSOalgorithmsandimplementedlessonslearnedtocreatealgorithmsforthemulti-beam2b+2ddesign

(Mode7.1)andsinglebeam1b+1doperations(Mode7.2).Thus,elasticbackscatterlidaralgorithmsareverymatureforseveraldifferentcombinationsofmeasurementcapabilities(singleormultiplebackscatteranddepolarizationwavelengths).Theelasticbackscatterlidarsubsystemcomponentsareallveryreliable,withmostreachingTRL9overthelastdecade.CALIOPhasoperatedforoverelevenyears,wellpastitsthree-yearproposedlifetime.TheCALIOPlaser,telescope,analogdetectors,andothersubsystemcomponentsareallTRL9.CATSwasdesignedtooperatefor6monthsbutprovidedsciencequalitydatafor33months.ManyoftheCATSsubsystemcomponents,especiallythephoton-countingdetectors,arealsoTRL9.Additionally,ESTOhasfundedthetechnologyreadinessadvancementofacompacthigh-rep-ratelasercapableoffittingaSmallSatarchitecture.

Figure 3.12. CATS data products including many of the observables the scientific community desires, such as backscatter coefficient, depolarization ratio, layer boundaries, and estimates of aerosol type, cloud phase, optical depth, and extinction.

CATShasprovidedvaluableinsighttoinformapathforward(sciencegoals,instrumentdesign,implementationstrategy)thatoptimizessciencereturnversuscost.Examplesare:

• DiurnalCycle:GiventheorbitoftheISS,athree-dayrepeatcyclethatpassesoverthesamelocationsbutatdifferentlocaltimes,CATShasshownthatsensorsinsun-synchronousorbits(passingoverthesamelocationatthesamelocaltimeeveryoverpass)areonlycapturinga“snapshot”oftheclouddiurnalcycle(Noeletal.,2018).Futuremissionsshouldconsideranorbitand/ormultipleSmallSatimplementationthatcancomplementGOES/ABIandcapturethediurnalvariabilityofcloudsandaerosols.

• SingleWavelength:Manyofthepopularlidardataproducts(layerheights,backscatter,depolarization,cloudphase,aerosoltype)canbeaccuratelyproducedusingasinglewavelength(1b+1d)lidarlikeCATSMode7.2(Yorksetal.,2016;Emmanouiletal.,2017;McGilletal.,2018).AmultipleSmallSatimplementationofa1b+1dlidarwouldprovidethesedataproductswithhighertemporal/spatialcoverage.

• Near-RealTime(NRT)Data:SimpleNRT(datalatencyof<6hours)CATSdataproducts(backscatterprofile,layerheights,etc.)demonstrateimprovementsinaerosolandvolcanicplumetransportforecasts(Hughesetal.,2017).Afuturemission’sabilitytoproduceNRTdataiscriticalforairqualityandaerosolforecastmodels,butmodelsarestillevolvingtoincorporatesimplelidarproducts.

• 1064nmSignal:TheCATS1064nmsignalisrobustandcalibrateddirectlybynormalizingtotheRayleighprofile.Italsoprovidesspectaculardetail(duetohighreprate-photon,countingtechnique)andhasprovencriticaltoaccuratedetectionofabovecloudaerosols(ACA;Rajapaksheetal.,2017).Afuturespace-basedlidarneedstohavesimilarorbettersignalstrengthat1064nmforaccurateACAdetectionand1064nmopticalproperties.

TheseresultsfromCATSsuggestanelasticbackscatterlidarisaviablepotentialpathforward,asalow-costalternativetothe3β+2α+2δHSRL,forafuturespace-basedlidarmission,evenasasinglewavelength(1b+1d)instrumentimplementedasmultipleSmallSats.InformationonCATSandaccesstotheCATSdatacanbefoundathttp://cats.gsfc.nasa.gov.

Lidar Summary and Recommendations

TheprogressmadeinadvancingtheACElidarconceptshasputNASAinanexcellentpositionforanear-termimplementationoftheAerosolsmissionrecommendedinthe2017DecadalSurvey.AnelasticbackscatterlidarcanleveragetheheritagefromCATSandCALIPSOtominimizeriskandofferaffordability.Suchalidarisareliable(TRL9)andcost-effectiveinstrumentthatcanbeadaptedtodifferentorbitaltitudesandseveraltypesofspacebornearchitecture,suchasafree-flyermissionlikeACEoraspartofaSmallSatconstellationconcept.TheACEpre-formulationeffortfocusedprimarilyonsignificantadvancestowardsanHSRLthatmeetsthe3β+2α+2δmeasurementrequirementsforhigher-ordercloud/aerosolmicrophysicalproperties.ThecostandscheduleforadvancingHSRLsubsystemcomponentstoTRL6isachievableforaneartermmission(e.g.,KDPAin2020orafter).

Werecommendthateffortsinthenear-termfocusonthefollowingactivities:

• AdvancingtheTRLofkeylidarsubsystems,includingthelasertransmitteranddetectionelectronics

• Advancingandvalidatingretrievalalgorithms,especiallycombinedretrievalsusinglidar+polarimeterdataandlidar+radardata.

• Improvingsimulationcapabilitiesforconductingsensitivitystudies,retrievalstudies,andOSSEsusingvariouslidarandspacecraftconfigurations.

• Refininglidarinstrumentdesigns,developmentschedules,partneringapproaches,andcostestimates.

3.4 Ocean Color Sensor TechnologyassessmentandinstrumentconceptdevelopmentforanadvancedoceancolorsensorcapableofsatisfyingallmeasurementrequirementsfortheACEradiometerbeganwellbeforereleaseofthe2007DecadalSurveyReport.ThishistoryhasbeendocumentedindetailinMcClainetal.(2012)andisbrieflysummarizedhere.

During2000-2001,astudywasconductedtoassesssatellite,field,andmodelingrequirementsforaNASAcarbonprogram(McClainetal.,2002,Gervinetal.,2002).OneoftheresultantrecommendationswasforanadvancedoceanbiologysatellitesensorthatexpandeduponheritagesensormeasurementsbyincludingUVbandsformoreaccurateretrievalofcoloreddissolvedorganicmatter(CDOM).Thisrecommendationwasmergedwithparallelworkbeingconductedonanoceanlidarsystemformeasuringphytoplanktonbiomass,yieldinganewmissionconceptcallthePhysiologyLidarMultispectralMission(PhyLM).ThePhyLMmissionwasfocusedonimprovingthecharacterizationofoceancarbonstocksandflowsthroughbotharefinedseparationofopticallyactivein-waterconstituentsandimprovedatmosphericcorrections.Atthispoint,theadvancedoceancolorsensorwasenvisionedashavingonly3UVbands,11visiblebands,twoNIRbands,andtwoSWIRbands.Importantly,theconceptgarneredenoughinteresttobegrantedfundingin2003fromNASAGoddardtoconducttwoInstrumentDesignLaboratory(IDL)studies,largelyfocusedontheoceanradiometer.Thus,technologyandinstrumentdevelopmentwork,ultimatelyinsupportofACE,beganmorethan15yearsago.

FollowingtheIDLstudies,anexternalscienceteamwasassembledforPhyLMtodefinethescienceobjectivesanddevelopaninitialScienceTraceabilityMatrix(STM).TheACEOceanEcosystemSTM(seeSection2above)bearsmanysimilaritiestothisearlydraft.ContinueddevelopmentstothePhyLMconceptyielded,by2005,anexpandedmissionincludingapolarimeterandlidarforcharacterizingaerosolsandimprovingatmosphericcorrectionsandanoceanradiometerwith5nmresolutionretrievalsfromthenearUVintotheNIR.Atthispoint,theconceptwascalledtheOceanCarbon,Ecosystem,andNear-Shore(OCEaNS)missionanditwassubmittedasawhitepaperforconsiderationduringtheNRCDecadalSurveystudy.

In2006,NASAHQrequestedformulationstudiesforseveralmissionconceptsinpreparationfortheDecadalSurveyresults,oneofwhichwascalledtheGlobalOceanCarbon,Ecosystems,andCoastalProcesses(GOCECP)mission.ThisformulationstudyprovidedfundingforathirdIDLassessment,yieldingfurtherdesignchangesandrefinementsforanadvancedoceanradiometer.TheDecadalSurveyresultswerereleasedinlate2007andincludedtheinterdisciplinaryAerosol,Cloud,andEcosystems(ACE)mission,equivalenttotheOCEaNSmissionconceptsubmittedin2006,butwiththeadditionofacloudradar.

InJune2008,theACEscienceteamwasformedandbeganthedevelopmentofmissionSTMsforeachofthesciencedisciplines(seeSection2above).Deliberations

bytheACEscienceteamresultedinsevenadditionalrequiredspecificbandsontheoceanradiometer(plus5nmhyperspectralUVtoNIRresolution),bringingtheminimumnumberof‘aggregate’bandsto26andincludingthreebandsintheSWIR.Inthespringof2009,aspartofanACEMissionDesignLaboratorystudyofthebaselineACEmission,afourthIDLstudywasconducted.

In2010,PresidentObamareleasedtheNASAPlanforEarthObservations(NPEO2010),announcingthePACEmissionwithanoceanradiometerastheprimaryinstrumentanddedicatedtomakingadvancedoceanmeasurementsinpreparationfortheACEmission.Soonthereafter,thePACEScienceDefinitionTeam(SDT)wasformedand,aspartoftheSDTactivities,afifthIDLstudywasconducted,largelyfocusedonassessingcostsforanadvancedradiometer.

Inparallelwiththesemissionconceptdevelopments,workwasalsoundertakentobuildaprototype‘proof-of-concept’advancedoceancolorinstrumentnamedtheOceanRadiometerforCarbonAssessment(ORCA).InitialdevelopmentoftheORCAprototypewassupportedbyGSFCInternalResearchandDevelopment(IRAD)fundsandfocusedonallopticscomponentsfromaprimarytelescopetoa‘bluechannel’detectorarray.ThisworkwasfurthersupportedthroughanInstrumentIncubatorProgram(IIP)grantandexpandedtoincludeafullyfunctioningprototypewithbothblueandredchannels,alongwithsystemleveltestingattheNationalInstituteofStandardsandTechnology.InstrumentperformancegoalsweresignificantlyguidedbyanoceanradiometerspecificationsdocumentdevelopedbytheACEoceanscienceteam(Meisteretal.,2011).In2010,asecondIIPgrantprovidessupportforthedesign,fabrication,testing,andintegrationofflight-likefocalplanesandelectronicsintheORCAprototype.AlloftheseactivitiessignificantlyadvancedthetechnologicalreadinessofanoceanradiometermeetingACEandPACErequirements.

3.5 Ocean Color Validation Sensors

Optical Sensors for Planetary Radiance Energy (OSPREy)

ACEoceancolorscienceobjectivesincludegeophysicalpropertyretrievalsinthecoastaloceanandcontemporaneousobservationsoftheoceanandatmosphere.TheOSPREyprojecthasbeenfocusedondevelopinganddeployinganewsuiteofradiometerstosupporttheincreasingdemandsofNASA’soceancolorresearch(Figure3.13),withanemphasisonthedataqualitychallengesassociatedwithvicariouscalibrationandalgorithmvalidation.OSPREyinstrumentsarethermallyregulated,ruggedized,anddesignedtooperateautonomously(Hookeretal.2012).AnOSPREysystemmakesobservationsoftheseasurfacepluscelestialtargets(Sun,sky,andMoon)acrosstheUV–SWIRdomain(305–1,670nm)toderiveanunprecedentednumberofnear-simultaneousatmosphericandoceanicparameters.OSPREycanalsobeusedforland,snow,andicetargets,buthasnotbeendeployedforthoseobservations.Theradianceandirradiancesensorshavehighlyaccuratemicroradiometers(19and18,respectively),whichcanbeusedtocontinuously

calibratethetemperature-stabilizedspectrograph.Thistypeofmeasurementapproachisreferredtoashybridspectral,becauseitusestwotypesofdetectortechnologiestoimprovethequalityofthecollecteddata.ThespectrographsprovidehighresolutionUV-NIRdata,andthemicroradiometersextendthespectraldomaintotheSWIR.ACEpre-formulationfundingforOSPREydevelopmentallowedfortheadditionofa9-positionfilterwheelforthree-axispolarimetryandimproveddarkcorrectionforthespectrograph,plusnovelperformancecharacterizationmeasurementsusingdiversecelestialtargets.Thelatterincludedthefollowingduring2012:thePerigee(orSuper)Moonon6May;thesolareclipseon20May;theVenustransiton5June;andtheBlue(full)Moonon31August.Celestialobservationsprovideautonomousabove-watersystemsuniquemonitoringsources(asisdonewiththespacebornesensor)withrespecttoin-watermethods.OSPREyhasaTRLof9.

Compact-Optical Profiling System (C-OPS)

Toensureastate-of-the-artin-watervalidationdatasetforOSPREydataproductsoftheseasurface,theCompact-OpticalProfilingSystem(C-OPS)instrument(Morrowetal.2010)wasfittedwithtwodigitalthrustersaspartoftheCompact-PropulsionOptionforProfilingSystems(C-PrOPS)accessory(Hooker2014),whichalsoaddedaconductivityprobe.TheprogrammablethrustersallowtheC-OPS,whichisbuiltwiththesamemicroradiometersasOPSREy,tobemaneuveredhorizontallybeforeanear-simultaneousprofileofthewatermassismadeincloseproximitytotheOSPREyinstrumentsystem.TheC-PrOPSprototype(Figure3.14)wasfieldcommissionedwithACEsupportandsignificantlyimprovedthedataqualityforin-watervalidationexercisesbyreducingtheamountoftimeneededtoacquiretheopticaldata,becausenovesselmaneuveringisneededtopositiontheprofilerandthethrusterscanbeusedtobringtheprofilerrapidlytothesurfaceinbetweenopticalcasts.Inaddition,thesmallthrustersorienttheprofilerverticallyandproducenegligibleturbulencethatisdirectedbelowtheupwardpointingirradiancesensor,sowatercolumnopticalproperties(nowspanning312–875nm)areonlyminimallyinfluencedbythemotionoftheprofiler.TheC-OPSinstrumenthasaTRLof9.

Figure 3.13: An OSPREy radiance &irradiance dyad deployed at a lake in 2013.

Figure 3.14: C-PrOPS thrusters (one on back) with conductivity probe mounted on a C-OPS instrument. The ydrobaric buoyancy permits descent rates as small as 5 cm/s with stable, ±5°, vertical tilts.

4 Measurement Algorithms ThissectionpresentsanoverviewoftheLevel2(L2)algorithmsbeingdevelopedfortheACEinstruments.Level0(L0)andLevel1(L1)algorithmsaregenerallyinstrumentspecificandrepresentthestepsneededtotransformvoltagescapturedbyaninstrumenttogeo-located,calibratedsetofgeophysicallymeaningfulparameters.TheyarethereforedescribedinSection3underTechnologyAssessmentandInstrumentConceptDevelopmentseparatelyforeachinstrument.

WhilethereisanexpectationthatL1orL2ACEmeasurementswillbeassimilatedintocomprehensiveearthsystemmodelscapableofrepresentingcloudandaerosolmicrophysics,thedetailsofsuchmodelsandL4algorithmsarenotdescribedhere.

4.1 Aerosol TheACErequirementsonretrievingthesizedistribution,complexrefractiveindexandnon-sphericityofaerosolsmeanthataretrievalapproachisrequiredthatmakesfulluseoftheinformationcontentofthemeasurements.

ThebasisofL2aerosolretrievalalgorithmsforbothpassivepolarimetricobservationsandmulti-spectralhighspectralresolutionlidarisnecessarilytheinversionoftheobservationstoretrieveamicrophysicalmodel(sizeandcomplexrefractiveindex)andamount(numberconcentration,surfaceareaconcentration,volumeconcentration)ofaerosolthatisconsistentwiththeobservations,withsomeformofregularizationtosuppressunphysical,orunlikelysolutions.Theregularizationgenerallyhastheeffectofforcingtheretrievedaerosolproperties(e.g.sizedistribution,spectralrefractiveindex)tobesmooth(Duboviketal.2011)orimposeconstraintsonretrievedvalues(Hasekampetal.2011).Thepassivepolarimetricobservationsdependnon-linearlyontherequiredaerosolpropertiesandtheinversionisthereforeiterativeinnatureandtheapplicationoftheseschemestothetypeofglobaldatathatisexpectedfromafutureACEmissionwillbechallenging,butcurrentlybothstandardparallelizationtechniques(Wuetal.,2015),implementationsusingGraphicsProcessingUnits(GPUs)andanalyticalsimplificationsofradiativetransfer(Chaikovskayaetal.,2014)andneuralnetworks(DiNoiaetal.2017)arebeingappliedsuccessfullytoprocessingofglobalpolarimetricdatafromPOLDER.

Whiledifferentgroupswilladaptspecificimplementationsofoptimalestimationtechniquestothemeasurementsetprovidedbytheirsensor,therearetwoaspectsofaerosolremotesensingfrompassivepolarimetricobservationsthataregeneraltoanyapproach.Thefirstisanadequatemodeloftheunderlyingsurfaceandthesecondisafastandaccurateradiativetransfermodelfortheatmospherethatideallyprovidesanalyticdeterminationoffunctionalderivativesoftheradiationfieldwithrespecttotheaerosolparametersbeingretrieved,commonlyknownasJacobians.Wenotethatarecentreviewpaper(Duboviketal.2019)providesanoverviewofavailablepolarimetricobservations,theirhistoryandexpecteddevelopments,andthestateatthetimeofwritingofresultingaerosolproducts.

Surface Characterization

Surfacemodelscanbedividedbetweenwaterandlandsurfaces,withtheprimarywatersurfaceofinterestbeingtheocean.ForremotesensingofaerosolovertheoceanthespecularreflectionoflightfromthesurfaceiswellrepresentedbythemodelofCoxandMunk(1954).Wenotethatwhilethismodelalwaysprovidesareasonablerepresentationofthesunlightscatteredofftheoceansurface,ifitisestimatedfrommulti-angleobservationsaspartofanaerosolretrieval,thewindspeedanddirectionretrievedwillnotnecessarilycorrespondtotheactualwindspeedanddirection(Suetal.2002,Chowdharyetal.2005).Inadditiontosurfacescatteringthereisalsoacontributionfromlightscatteredunderwaterthatisnotnegligibleinthevisiblepartofthespectrum.Thebrightnessandspectrumofthislightdependsonthebiomasscontentoftheocean,suchthatvariationsinthecoloroftheoceancanbeobservedevenfromspace.Rayleighscatteringbypureseawater,andRayleigh-Ganstypescatteringbyplankton,causesthislighttobepolarizedwithadistinctiveangulardistribution.Chowdharyetal.(2012)reviewahydrosolmodelanddiscussitssensitivitytovariationsincoloreddissolvedorganicmatter(CDOM)andthescatteringfunctionofmarineparticulates.TheyshowthattheimpactofvariationsinCDOMonthepolarizedreflectanceiscomparabletoorlessthanthestandarderrorofthisreflectancewhereastheireffectsontotalreflectancemaybesubstantial(i.e.upto>30%).Thisemphasizesthevalueofmultiplepolarizationmeasurementsthroughthevisiblepartofthespectrumwhenperformingaerosolremotesensingovertheocean.ThemodelforoceanbodyscatteringdevelopedbytheRSPgrouphasrecentlybeenincorporatedintotheGeneralizedRetrievalofAerosolandSurfaceProperties(GRASP)algorithm(Duboviketal.2011)incollaborationwiththeUniversityofLille.InGenerallandsurfacemodelsaresomewhatadhocwiththeparametersthatcontrolthetotalbidirectionalreflectancefactorofthesurfacebeingunrelatedtothosecontrollingthepolarizedreflectanceofthesurface(Cairnsetal.2009a).TheRSPgrouphasworkedwiththegroupsatSRONandtheUniversityofLilletodevelopamoreadvancedphysicallybasedsurfacemodelwherethetotalandpolarizedreflectancearecontrolledbythesameparameters,whichdescribetheunderlyingphysicalscatteringprocesses,thatgeneratethereflectionoflightatasurface(Litvinovetal.2012).TheobservationsobtainedpriortoPODEXduringatestflightoftheRSPontheNASAER-2andsomeearlierdatafromtheCarbonaceousAerosolsandRadiativeEffectsStudy(CARES)(Zaverietal.2012)havebeenusedtoestablishthepolarizationpropertiesofsnow(Ottavianietal.2012,2015).Thesmallmagnitudeofthepolarizedreflectanceofsnowanditsweakspectralvariationover400to2300nmholdthepromiseofrobustaerosolsretrievalsoversnowfromsensorsthathaveasufficientspectralrangeofpolarizedobservations.

RSP Aerosol Algorithms

Onekeyaspectofaerosolretrievalsoveroceanusingpolarizationobservationsistohaveaphysicallybasedmodelofoceanbodyscatteringtoprovidealowerboundarycondition.TheoceanbodyscatteringmodelthatRSPaerosolretrievalalgorithmsuse(Chowdharyetal.2012)hasthereforebeingupdatedinlinewithcurrenttrends

inoceancolorremotesensing(Maritorenaetal.2010)toallowscatteringbyparticulatematter,absorptionbycoloreddissolvedorganicmatterandChlorophyllconcentrationtoallvaryindependently.

AerosolretrievalsusingRSPobservationsoverlandforthePODEXandSEAC4RSfieldcampaignswereevaluatedagainstcollocatedAERONETmeasurements(Wuetal.2015)andfoundtoshowgoodagreementforaerosolopticaldepth(AOD),sizedistribution,singlescatteringalbedo(SSA)andrefractiveindex.Thecriticalimportanceofmulti-anglepolarizationmeasurementsinthenear-UVandbluepartofthespectrumforpassiveremotesensingofaerosollayerheightwasidentifiedandgoodagreementoftheretrievedaerosollayerheightfromRSPwithmeasurementsfromtheCloudPhysicsLidar(CPL)showingameanabsolutedifferenceoflessthan1kmwasfound(Wuetal.2016).ThePhillips-Tikhonovalgorithmusedintheseretrievalswasthencoupledwithaneural-networkthatwasusedtoprovideaninitialguessfortheiterativescheme(DiNoiaetal.2017).Theresultingalgorithmappearscapableofaccuratelyretrievingaerosolopticalthickness,fine-modeeffectiveradiusandaerosollayerheightfromRSPdata.Amongtheadvantagesofusinganeuralnetworkasinitialguessforaniterativealgorithmareadecreaseinprocessingtimeandanincreaseinthenumberofconvergingretrievals.

Analternativeoptimalestimationretrievalframework,theMicrophysicalAerosolPropertiesfromPolarimetry(MAPP)algorithmwasdevelopedusingtheGISSvectorradiativetransfercode(Stamnesetal.2018).Thisiterativeschemeisparticularlyfocusedonsimultaneousretrievalofaerosolmicrophysicalpropertiesandoceancolorbio-opticalparametersusingmulti-angulartotalandpolarizedradiances.aerosolretrievalsoverocean.Measurementscollectedduringthe2012Two-ColumnAerosolProject(TCAP)campaignandthe2014Ship-AircraftBioOpticalResearch(SABOR)campaignwereanalyzedandgoodagreementbetweentheRSPretrievalsandco-incidentlidarmeasurementsmadebyNASAHighSpectralResolutionLidar1and2systemswasfound.Thecompatibilityofthepassive(RSP)andactive(HSRL)sensorsisakeymilestoneonthepathtoacombinedlidar+polarimeterretrievalusingbothHSRLandRSPmeasurements.

MSPI Aerosol Algorithms

OptimizationbasedalgorithmshavebeendevelopedtoretrieveaerosolloadingandaerosolopticalandmicrophysicalpropertiesfromAirMSPIobservationsoverthreedifferenttypesoflowerboundaries:ocean(Xuetal.,2016),land(Xuetal.,2017),andstratocumuluscloud(Xuetal.,2018).Boundarypropertiesarecoupledintoaerosolretrievals,whichincludewater-leavingradianceforwater,bidirectionalsurfacereflectancefactorsforland,andclouddropletsizedistribution,cloudtopheight,andcloudopticaldepthforstratocumulusclouds.Water-vaporabundanceisalsoretrievedfromAirMSPI’swatervaporband.TheretrievalsimposevarioustypesofconstraintsonhorizontalvariationsofaerosolmicrophysicalpropertiesfollowingDuboviketal.(2011),spectralinvarianceconstraintsontheangularshape

ofsurfacebidirectionalreflectancefactorandpolarizedsurfacereflectance(Dineretal.,2005,2012),andrelationsbetweenunder-wateropticalpropertieswiththewater-leavingradiance(Zhaietal.,2010).Ahybridradiativetransfer(RT)codethatcombinesthestrengthofMarkovchain(Xuetal.,2012)andadding-doubling(HansenandTravis,1974)RTmethodshasbeendevelopedtoimprovethemodelingefficiency(Xuetal.,2017).

Inadditiontothevariousalgorithmsfortheretrievalofaerosolproperties,analgorithmwasdevelopedfortheretrievalofliquidwatercloudproperties,includingthedropletsizedistributionandcloudopticaldepth(Dineretal.,2013b;Xuetal.,2018).ThisalgorithmutilizesthepolarizedprimaryandsupernumerarycloudbowsinAirMSPI’scontinuoussweepimagery,basedonBréonandGoloub(1998).AirMSPILevel2liquidwatercloudproductshavebeendeliveredtotheNASALangleyAtmosphericScienceDataCenterforpublicdistribution,alongwithsupportingQualityStatementandDataProductSpecificationdocuments,seehttps://eosweb.larc.nasa.gov/project/airmspi/airmspi_table.

TheMSPIretrievalalgorithmshavebeentestedusingGroundMSPIobservations,andAirMSPIobservationsoverocean,landandstratocumuluscloudsacquiredinmultiplefieldcampaignssuchasPODEX,SEAC4RS,CalWater,ImPACT-PM,ORACLES,andACEPOL.ExamplesareshowninFigures4.1,4.2,4.3a,and4.3b.Initialresultsshowthatspectralopticaldepths,aerosolmicrophysicalproperties,andnormalizedwater-leavingradiancecomparefavorablytoindependentreferencedataderivedfromAERONET,NASAHSRL-2(Hairetal.,2008),andRSP(Cairnsetal.,1999).Toaddressthesensitivityofthecoupledaerosol-surfaceretrievaltoinitialguesses,multipletypesofconstraintshavebeenimposedonretrievals.Forimage-basedremotesensingtechnologies,dataprocessingefficiencywithoutlosingmodelingaccuracyisamajorconcernforACE.SeveralspeedenhancementstotheJPLMSPIalgorithmarebeinginvestigated,includingtradeoffofspeedandaccuracyintheforwardradiativetransfermodule,combinationoftheoptimizationalgorithmwithlookuptables,anduseofaGraphicalProcessingUnit(GPU).

Figure 4.1: Left: GroundMSPI data collected over surface targets as the scattering angle changed due to motion of the Sun across the sky. Scaled bidirectional reflectance factors (BRF) at 470 and 865 relate linearly to the BRF at 865 nm, showing spectral invariance in the angular BRF shape. Right: Relationship between polarized BRF calculated using Q and U at 470 and 865 nm to 660 nm, showing spectral invariance in both the magnitude and angular shape.

Figure 4.2: Example aerosol aerosol optical depth (AOD), single scattering albedo (SSA), surface albedo (A) retrieval using MSPI over-land retrieval algorithm applied to AirMSPI data over Fresno, CA, 6 January 2012 during an

Wavelength (nm)400 600 800 1000

0.8AERONET 19:07AERONET 20:07AERONET 21:07AirMSPI 20:23

Wavelength (nm)400 600 800 1000

AERONET 19:07AERONET 20:07AERONET 21:07AirMSPI 20:23

Radius (µm)0.01 1 100

3 /µm

0.07AERONET 19:07AERONET 20:07AERONET 21:07AirMSPI 20:23

engineering flight of AirMSPI. Comparisons in the three bottom panels were made to spatiotemporally collocated AERONET reference data. Figures were adapted from Xu et al. (2017).

Figure 4.3a: Example aerosol AOD, single scattering albedo (SSA), volume-weighted aerosol size distribution, and normalized water-leaving radiance (nLw) retrieval using the MSPI over-ocean retrieval algorithm applied to AirMSPI data over the USC SeaPRISM AERONET site off the coast of southern CA, 6 February 2013 during PODEX. Figures adapted from Xu et al. (2016).

Wavelength (nm)400 600 800 1000

0.4 AERONET 19:08AERONET 20:08AirMSPI 19:44 (GRASP)AirMSPI 19:44 (Retrieved)

Wavelength (nm)400 600 800 1000

AERONET 19:08AERONET 20:08AirMSPI 19:44 (GRASP)AirMSPI 19:44 (Retrieved)

Radius (µm)0.01 0.1 1 10 100

AERONET 19:08AERONET 20:08AirMSPI 19:44 (GRASP)AirMSPI 19:44 (Retrieved)

Wavelength (nm)300 450 600 750 900

2 -sr-u

1 AERONET 19:40AERONET interpolatedAirMSPI 19:44 (Retrieved)

Figure 4.3b: Example above-cloud aerosol optical depth (AOD, upper left), cloud-top height (upper right), cloud optical depth (lower left), and cloud-top droplet effective radius (lower right) retrieved using MSPI aerosol and cloud coupled retrieval algorithm. As the input data, AirMSPI cloud imageries were acquired during ORACLES campaign over South Atlantic Ocean (off the coast of Namibia), which took place in August and September 2016. Retrieval comparison was made to the reference data of above-cloud aerosol optical depth and cloud-top height from NASA HSRL-2 measurements, and to reference data of cloud optical depth and effective radius of cloud-top droplets from NASA RSP measurements. Figures adapted from Xu et al. (2018).

PACS/HARP Aerosol Algorithms

TheHARPgrouphasworkedwithDr.OlegDubovik;’sgrouponaversionoftheGRASPalgorithmthatisoptimizedforHARPretrievalsusingitsuniqueangularsamplingandwavelengthcombination(Duboviketal.2011;Duboviketal.2014).TheGRASPalgorithmhasbeenfullyintegratedtotheUMBCserversandisnowoperationallyavailableforthefitandretrievalofaerosolmicrophysicaldatafromtheHARPpolarimeter.

Figure4.3cshowsanexampleofinversionoftheaerosolmicrophysicalpropertiesusingGRASPoverHARPdatacollectedinprescribedfiresinArizonaduringtheACEPOLexperiment.InthisexampleGRASPprovidedaverygoodfitoverthischallengingcase,allowingfortheretrievaloftheparticlesizedistribution,totalaerosolopticaldepth,singlescatteringalbedo,particleasymmetry,etc.

Figure 4.3c: The top figure on the right-hand side shows a single angle RGB image of a prescribed fire collected by AirHARP in Arizona during the ACEPOL flight campaign. The bottom figure shows an image of the degree of linear polarization at 440nm wavelength emphasizing the break of the typical Rayleigh scattering pattern by the mostly non-spherical fresh smoke particles. Both plots on the left-hand side show results of retrievals by the GRASP algorithm over this smoke.

HSRL Aerosol Algorithms

OperationalcodeforlidarretrievalsofACEaerosolproductshasbeendevelopedandusedtoproduceACE-likeLevel-2dataproductsfromtheeightfieldmissionsflownwiththeLaRC3β+2α+2δACEprototypeHSRLlidar.Theseproductsfallintothreecategories.FirstarethebasicLevel-2opticalproductsretrievedfromthelidarsignals(aerosolbackscatter,extinction,depolarization).Thealgorithmsfortheseproducts,includingprerequisiteinstrumentcalibrations,aredescribedinHairetal.(2008),Burtonetal.(2014),andBurtonetal.(2018).

Secondisaerosoltype,whichisaqualitativeratherthana

Figure 4.4: Comparison of AOT (355 and 532 nm) from HSRL-2 and DRAGON-AERONET measurements over Houston during the NASA DISCOVER-AQ mission.

quantitativeproduct.Aerosoltype(e.g.,marine,smoke,dust,urbanpollution,etc.)isinferredfromlidarintensiveparameters(e.g.,parameterslikeaerosolextinction-to-backscatterratio,depolarizationratio,andbackscattercolorratio,whichareindependentofaerosolloadinganddependonlyonparticleproperties).AerosoltypingalgorithmsandresultsfromairbornefieldmissionsaredescribedinBurtonetal.(2012,2013,and2018).Whilethereisnoagreed-uponuniversaldefinitionofaerosoltypeasageophysicalvariable,interestinaerosoltypefromHSRLmeasurementssteadilyincreasedovertheACEera,dueinlargeparttothepapersproducedfromthenumerousfieldcampaignsflownbytheLaRCairborneHSRLs.Infact,thetypingmethodologyhasbeenadopted,withmodification,byEuropeanlidargroupsfortheinterpretationoftheirairborne,spaceborne,andground-basedmeasurements(e.g.,Großetal.,2015,Papagiannopoulosetal.,2018).Significantly,progresshasalsobeenmadeconnectingHSRLaerosoltypestochemicalspeciationinchemicaltransportmodels(Dawsonetal.,2017),suggestingatleastonemethodtouseHSRLmeasurementstoassessandimprovemodelpredictions.

Thirdareadvancedaerosoloptical/microphysicalproductsderivedfromthebasicLevel-2aerosolopticalproducts.Theseincludeeffectiveradius,indexofrefraction,singlescatteralbedo,absorption,andconcentration,andarederivedusingadvancedinversiontechniques.Theaccuracyoftheseretrievedaerosolproductshasbeenextensivelyassessedusingdataacquiredonnumerousairbornefieldmissionsfromothersensorsflyingonparticipatingaircraftandretrievalsfromground-basedAERONETinstrumentsplacedalongtheflighttracks(e.g.,Sawamuraetal.,2014,2018;Mülleretal.,2014).Figure4.4showsacomparisonofaerosolopticalthicknessfromthebasicHSRL-2measurements(atboth355and532nm)withcoincidentAEROCOMmeasurementsacquiredduringtheNASADISCOVER-AQmissionoverHouston.Figure4.5showsacomparisonofaerosolconcentrationandeffectiveradiusprofilesderivedfromtheHSRL-23β+2αmeasurementswithcoincidentairborneinsitumeasurementsacquiredfromtheDOEG-1aircraftduringtheDOETwoColumnAerosolProject(TCAP).MoreextensivecomparisonswithbothairborneinsitudataandAERONETretrievalshavebeenconductedusingdatafromthreeNASADISCOVER-AQdeployments(twoin2013andonein2014)andthreeORACLESdeployments(2016,2017,and2018)(seeFigure4.6).Moreover,Burtonetal.(2016)conductedatheoreticalstudyoftheinformationcontentin3β+2αdatatodeterminethelimitsofwhatparameterscouldandcouldnotberetrieved.Thisstudyindicatedthatretrievalswouldhavethegreatestsensitivitytoaerosoleffectiveradiusandconcentrationandtheleastsensitivitytoaerosolabsorption.

Toprovidemoreaccurateaerosoloptical/microphysicalproducts,ajointlidar+polarimeteraerosolretrievalalgorithmwasdevelopedwithfundingfrombothACEandtheROSESRemoteSensingTheoryelement.Thealgorithmemploysandoptimalestimateframeworktoretrieveverticallyresolvedaerosolproperties.ThisretrievalhasbeenappliedtoHSRL-2andRSPdatacollectedfromthe2016ORACLESER-2deployment.Thejointlidar+polarimeteralgorithmsshowspromise

forimprovingtheaccuracyofthelidar-onlyretrievalsandprovidingverticallyresolvedproductsthatareotherwiseavailableonlyfromcolumn-wisepolarimeterretrievals.

Figure 4.5. (top) Curtains showing HSRL-2 retrievals of microphysical parameters and (bottom) comparisons of microphysical parameters retrieved from the HSRL-2 3b+2a inversion method (red) and from the G-1 in situ measurements (black) on 17 July 2012 (from Müller et al. 2014).

4.2 Clouds InthissubsectionwedescribethegeneralapproachforassessingtheimpactoftheACEobservingsystemontheretrievalofcloudandprecipitationgeophysicalparameters,andthesimplificationsnecessaryforoperationalimplementationofanalgorithmsuite.ThisisfollowedbythedescriptionofemergingL2algorithmsbeingdevelopedforgroundbasedandairbornesensorsthatwillinformtheoperationalACEcloudsprocessing.

General Approach

InthedevelopmentofL2algorithmsforACEClouds,wehavetwoveryspecificresearchobjectivesthataddressshortandlongtermsgoals.Ourmostimmediateneedistodeveloptoolsthatallowustorigorouslydefinethetradespacebetweenscienceobjectivesandinstrumentsuitecomplexity,andourmorelongtermgoalsaretodevelopL2algorithmsthatwouldbesuitableforoperationalimplementationpriortolaunchofACEassets.

Inourearlierworksummarizedinthe2010ACEReport,toolstorigorouslydefinethetradespacewerenotavailable.Whileweusedarigorousmethodtoestimaterequirementsongeophysicalparameters,itwasimpossibletocharacterizequantitativelyhowtherequirementsongeophysicalparametersmappedtoinstrumentrequirements.ThisisespeciallychallengingbecausetheL2algorithmsforACEcloudswillrelyonsynergisticcombinationsofactiveandpassivemeasurementsthathaveevolvedfromtheA-Trainera(i.e.,Maceetal.,2016).WhilewecouldtheorizewhatmeasurementswouldconstrainwhataspectsofthegeophysicalquantitiesofinterestappearingintheScienceTraceabilityMatricesof

Figure 4.6. Comparison of aerosol microphysical parameters derived from HSRL-2 3ß+2a inversion method and coincident airborne in situ measurements acquired during the NASA DISCOVER-AQ missions over the California central valley (top) and Houston (bottom).

thatearlierreport(Section2),wecouldnotsayrigorouslywhattheinstrumentrequirementswouldbewhencombinedinsynergisticalgorithms.AdvancedstatisticaltoolsforL2algorithmdevelopmentarenowbecomingavailablethatwillallowustoaddressthisissuerigorouslyasACEmovesforward(Posseltetal.,2016;PosseltandMace,2014).

Wetaketheapproachthatasetofmeasurements(y)havesomelevelofuncertaintyandrepresentanatmosphericstate(x)andthereexistsasetofforwardmodelsrelatingxtoythathaveassumptionswithquantifiableuncertainties.WecanthenutilizemethodologiesbasedinBayesianstatistics:

Then,theatmosphericstatethatweseektocharacterizeisrepresentedasaposteriorprobabilitydistribution,p(x|y),thatresultsfrommappingthemeasurementsthroughasetofforwardmodelsthatreplicatetheuncertainmeasurementsasafunctionoftheuncertainatmosphericstate.Intheshortterm,weseektoknowtheoptimalsetofmeasurementsthatproduceanatmosphericstateprobabilitythatsatisfiesourrequirementsongeophysicalquantitieswhileinthelongterm,weseekalgorithmsthatefficientlyprovidep(x|y)withreasonablecharacterizationsofuncertainty.

Ahierarchyoftechniquesexisttoaccomplishbothournear-andlonger-termgoals.Toaccomplishournear-termgoals,wemakecomputationalefficiencyasecondaryobjectiveandseekanapproachthatisleastconstrainedbyassumptionsinmappingtherelationshipsbetweenmeasurementsandtheposteriorprobabilitydistributionoftheatmosphericstate.Whatisneededisawaytogeneraterigorouslytheposteriorp.d.f.MarkovChainMonteCarlo(MCMC,Tamminen2004;Tarantola2005;Posseltetal.,2008;PosseltandVukicevic2010)methodsprovidesuchatool.MCMCalgorithmsconsistofaguidedrandomwalkthroughtheprobabilityspace.SeePosseltandMace(2014)foranexampleofMCMCappliedtoamixedphasesnowcloudusingground-basedcombinationsofradar,microwaveradiometer,andsurfacesolarfluxandPosseltetal.(2016)forapplicationofthisapproachtoshallowwarmcumulus.Weenvisionthatsuchanapproach,whencombinedwithactualmeasurementsandmodel-baseobservationsystemsimulationexperiments(OSSE),willrigorouslydefinethetradespacebetweeninstrumentrequirementsandgeophysicalparameterrequirements.

Ourlonger-termgoalsofdevelopingoperationalL2algorithmswillutilizemorecomputationallyefficientapproachestosolvingBayestheorembutatthecostofreducedaccuracyinproducingtheposteriorsolutionprobability.Optimalestimation(OE)hasemergedasapreferredapproachinthisregard.TomakeOEmorecomputationallyefficientthanMCMC,thePDF’sareassumedtobeadequatelydescribedbyGaussiansandtherelationshipsbetweenforwardmodelsandmeasurementsareassumedtobedescribedbythefirstderivativeofthe

p x y( ) = p x( ) p y x( )p y( )

measurementwithrespecttotheatmosphericstate–i.e.linearityintherelationshipisassumedsothatafirstorderTaylorexpansionissufficienttocharacterizetheserelationships.OEalgorithmsthatarenowunderdevelopment(Maceetal.2016,amongothers)willformthebasisoftheL2algorithmsuitethatwillultimatelybeimplementedonACEflightdata.WewillusethemorerigorousMCMCresults,fielddata,andOSSEstudiestodevelopandvalidatetheOEresults.

RSP Cloud Algorithms

Thepropertyofacloudthatisrequiredfirst,foramulti-anglesensor,isthecloudtopheightsothatviewsfromallanglescanbecollocatedtocloudtop.Operationally,cloudtopheightsretrievedfromRSPhyper-stereointensityobservationsareusedforthis.Thesecloudtopheightshavenbeenverifiedagainstlidarderivedcloudtopheights(Sinclairetal2017).Inaddition,cloudtoppressureisretrievedusingshortwavelength(410and470nm)polarizedreflectances(VanDiedenhovenet.al.2013).Cloudtopheightestimatesareusedtoremapthemulti-viewRSPdatasuchthattheyarecoincidentatthecloudtopaltitudeandprovidecontiguousangularsamplingoveraviewanglerangeof±60°fromnadirforeachspatialsampleofacloud.

ForasensorinlowEarthorbitthisviewanglerangewouldfrequentlyincludeascatteringanglerangefrom135°to165°,whichexhibitsasharplydefinedcloudbowstructureforwaterclouds.TheretrievalofdropletsizedistributionsfromcloudbowobservationswasoriginallyimplementedbyBréonandGoloub(1998)usingaparametricfitinwhichthesizedistributionisrepresentedbytheeffectiveradiusandvarianceofagammasizedistribution.Theaccuracyofthistypeofapproach,itsrangeofapplicabilityandrobustnessagainst3-Deffectswasevaluatedmorerecently(Alexandrovetal.2012a)usingMonte-Carlosimulationsofradiativetransferthroughamodeled(Ackermanetal.2004)cloudfield,andusinginsitumeasurementsobtainedduringtheNAAMEScampaign(Alexandrovetal.2018).Whileparametricfittingprovidesasimplemethodforestimatingclouddropletsizedistributions,itwasfoundthatcontiguoushigh(~1°)angularresolutionobservationsofthecloudbowcanusedinarainbowFouriertransform(RFT)thatprovidesanaccuratenon-parametricestimateoftheshapeofthedropletsizedistribution(Alexandrovetal.2012b).TheRFTisvaluableintheanalysisofcasessuchasfogs,ormulti-layerwatercloudswheretheassumptionthatthecloudbowisgeneratedbyasinglegammadistributeddropletsizedistributionisincorrect.ApplicationofthisapproachtowarmandsupercooledliquidcloudlayerswasdemonstratedusingdatafromthePODEXandSEAC4RScampaigns(Alexandrovetal.,2015,2016).Itshouldbenotedthatvariationsindropletsizedistributionmaybesubstantial,evenwithinaquitehomogeneousclouddeck,butcanberetrievedforeachpixelfromRSPobservations.ThehighangularresolutionoftheRSPpolarizedmeasurementsiscrucialfortheclouddropletsizeretrievalproducts.SincetheRSPmakesshortwaveinfrared(SWIR)radiancemeasurementsat1590and2260nm,opticaldepthandeffectiveradiusretrievalssimilartothose

developedforMODIS/VIIRS/SEVIRIetc.arealsoperformed(cf.NakajimaandKing1990).Thisallowsforthecomparisonandevaluationofthedifferentsizeretrievals.

RSPobservationsarealsousedtoprovideauniquemeasurement-basedestimateofcloudgeometricthickness.Thisisaccomplishedusingpolarimetricmeasurementsinthe960nmwatervaporabsorptionbandtoretrievetheamountofbothabove-cloudandin-cloudwatervaporandthenrelatein-cloudwatervaporamounttophysicalthicknessbasedontheassumptionthatthein-cloudwatervapormixingratioissaturated.Thecloudgeometricthickness,togetherwiththedropletsizeandopticaldepthretrievalsarethenusedtoestimatetheclouddropletnumberconcentration(Sinclairetal.2019),withoutmakinganyassumptionsabouttheadiabaticprofileofliquidwatercontentincloud(cf.Grosvenoretal.2018).

Presenceofthecloudbowstructureassociatedwithsphericalclouddropsalsoprovidesavirtuallyunambiguousindicationofaliquidcloudtopphase(Goloubetal.2000).Operationally,aliquidindex(vanDiedenhovenetal.2012a)iscalculatedthatquantifiesthestrengthoftherainbowsignalinRSPpolarizedreflectancesatscatteringanglesaround140°.Generally,aliquidindexvaluebelow0.3indicatesnoliquidispresentinthecloudtop,whichisthenclassifiedasice.Largerliquidindexvaluesindicateliquidormixed-phasecloudtops.

ForRSPmeasurementsovericeclouds,informationonicecrystalshape,crystaldistortion,scatteringasymmetryparameterandeffectiveradius,aswellascloudopticalthickness,areoperationallyretrieved(vanDiedenhovenetal.2012b,2013,2016a;vanDiedenhoven2018).Cloudopticalthicknessandeffectiveradiusareretrievedusingacombinationofvisibleandnadirshortwaveinfraredbands(cf.NakajimaandKing1990).Generally,suchretrievalsneedtheassumptionofaniceopticalmodel.However,themulti-anglepolarimetryallowstheicecrystalmodeltobetoconstrainedatanRSPpixellevel.Inadditiontoscatteringasymmetryparameter,themeanaspectratioofcrystalcomponentsandcrystaldistortionlevelisretrievedbythismethod.Forthisretrieval,singlehexagonalprismsareusedasproxiesforcomplexicecrystals.Thisapproachhasbeenevaluatedwithsimulatedmeasurementsusingopticalpropertiesofvariouscomplexicehabitsandtheirmixtures(vanDiedenhovenetal.2012b;2016b),aswellasusinginsitumeasurements(vanDiedenhovenetal.2013).Ingeneral,theasymmetryparameterwasfoundtoberetrievedwithin5%onapixellevel.Afterconstrainingtheasymmetryparameterusingthisapproach,theiceeffectiveradiusandcloudopticalthicknessisretrievedfromvisibleandtwoshortwavebandsusinganicemodelconsistentwiththeretrievedasymmetryparameter.ThisapproachisalsoappliedtoacombinationofMODISandPOLDERmeasurements(vanDiedenhovenetal.2014).Furthermore,vanDiedenhovenetal.(2016)demonstratedthattheconsiderabledifferenceinprobingdepthswithincloudtopassociatedwiththe1590and2260nmbandsofRSPyieldrelevantinformationabouttheverticalstructureoficesizeswithinconvectivecloudtops.

MSPI Algorithms

CloudretrievalsforMSPI,aswithMISR,useimagerymap-projectedtotheWGS84surfaceellipsoid.Algorithmsfallintotwoprincipalcategories:(a)stereophotogrammetricandradiometricretrievalsofcloud-topheightsandcloudfractionsasafunctionofaltitude,makinguseoffeatureandarea-basedpatternimagematchingandthresholding(bothleveragingheritagefromMISR),and(b)particlescatteringandradiativetransfer-basedretrievalsofcloudmicrophysicalproperties,whichcombinethenovelinformationcontentofpolarimetricdatawithmoreconventionalapproachesbasedonspectralradiances.

Figure4.7showsaretrievalofcloud-topheightsusingmulti-anglestereopatternmatchingappliedtoAirMSPIdatafrom31August2011,usingalgorithmssimilartothoseemployedoperationallywithMISR.Thestereoretrievalmakesuseof555nmimagesacquiredatviewanglesofnadirand26.5ºforwardandbackwardofnadir.UnlikeMISR,however,ataircraftaltitudesEarthcurvatureisinsufficienttoenableseparatingstereoparallaxfromtheeffectsofadvectionduetowind,hencetheheightsshowninFigure4.7arenotcorrectedforwind.ApplicationoftheMISRstereoalgorithmstoACEmultiangleimagerywillenablesimultaneousretrievalofcloud-topheightsandcloudmotionvectorwinds.

Figure 4.7: Stereoscopic retrieval of cloud-top heights using AirMSPI imagery at 3 view angles. Computational pattern matching is used to identify similar features in the different images and retrieve the cloud-top height field using the spatial disparities, or parallax, between the features in the imagery.

BuildinguponmethodologiesdescribedbyBréonandGoloub(1998)andAlexandrovetal.(2012a,b),AirMSPIdatahavebeenusedtoretrievecloud-topliquidwaterdropletsizedistributionsfornear-homogeneousmarinestratocumuluscloudsusingmeasurementsofthepolarizationofsupernumerarycloudbows(Dineretal.,2013a).Becausethepolarizationsignalsaredominatedbysinglescattering,theyarelesssusceptibleto3Dradiativetransfereffects,whichareaknownsourceofbiasforradiance-baseddropletsizeretrievals(e.g.,LiangandDiGirolamo,2013),hencehavethepotentialforretrievingspatialvariabilityincloud-topdropletsizein

brokencloudscenes.Figure4.8showsanexampleofcloudbowandgloryimageryfromAirMSPI,acquiredon31August2011.AtleftareintensityandDOLPimagesacquiredbysweepingtheinstrument’sgimbalalong-tracktoimageanareaapproximately110kminlengthx10kmatnadir.Atrightarefitstothesupernumerarybowsinthelowerportionoftheimage(southoftheglory)usingthesingle-scatteringmethodofBréonandGoloub(1998)overthescatteringanglerange140º-165º.Theparametricgammadistributionwasemployed,andthebest-fittingsolutionyieldsaneffectivedropletradiusof9.13µmandeffectivevarianceof0.006.Theregionabove165ºisusedhereasaconsistencycheck.Themodelcorrectlypredictsthelocationoftheinterferencefringesassociatedwiththehigher-ordersupernumerarybowsandglory,thoughsomedeviationinmagnitude,particularlyattheshorterwavelengths,isobserved.Thismaybeduetoadepartureofthedropletsizesfromapurelygammadistribution,spatialvariabilityinthedropletsizes,and/ormultiplescattering.MultipleAirMSPIimagesarebeingusedtoexamineeachofthesefactorsingreaterdetail.

Figure 4.8: Example of cloudbow and glory imagery from AirMSPI. At left are intensity and DOLP images. At right are fits to the supernumerary bows at 3 wavelengths in the lower portion of the image (south of the glory) using the method of Bréon and Goloub (1998). The dashed line indicates scattering angle of 165º.

Armedwithknowledgeofthedropletsizedistributionfrompolarizedlight,AirMSPIteammembersarealsoinvestigatingtheuseof1Dradiativetransfertheorytoestimatecloudopticalthicknessfromnaturallightinthepresenceof3Dadjacencyeffects.Specifically,applicationofastatistical-physicsanalysistechnique(Davisetal.,1997)toAirMSPIcloudimageryenablesanobjectivedeterminationoftheradiativesmoothingscale,beyondwhich3Dadjacencyeffectsbecomenegligible.

Invoking1Dradiativetransfertheoryatthisandlargerscalesminimizes3Dadjacencyeffects.Inthenearfuture,theAirMSPIteamplansto(1)extendpolarimetry-basedmicrophysicalretrievalstoheterogeneousclouds,and(2)refinenewradiance-basedretrievalsthatexploit3Dradiativetransfereffectsonmultiplescalesandyieldmacrophysicalcloudproperties,namely,opticaldepthandgeometricalthickness(henceavertically-averagedclouddropletnumberdensity).Althoughthelatertypeofretrievaldependscriticallyonthefine-scaleimagingachievablewithAirMSPIfromtheER-2(10–20mpixels),ananticipatedspin-offwillbecloudpropertyretrievalalgorithmsadaptedtoACE-typepixelscales(hundredsofmeters)thatwillberobustin3Dcloudstructures.Ultimately,thesystematicexploitationofpassivemulti-spectral/multi-angle/multi-pixeldatausingaccelerated3Dradiativetransferforwardmodelswillbenefitgreatlyfromobservationalconstraintsusingdatafromcollocatedactivesensors(namely,ACE’sradarandlidar).

Foricecloudsanewremotesensingtechniquetoinfertheaverageasymmetryparameteroficecrystalsnearcloudtopfrommulti-directionalpolarizationmeasurementshasbeendeveloped.Themethodisbasedonpreviousfindingsthat(a)complexaggregatesofhexagonalcrystalsgenerallyhavescatteringphasematricesresemblingthoseoftheircomponentsand(b)scatteringphasematricessystematicallyvarywithaspectratiosofcrystalsandtheirdegreeofmicroscalesurfaceroughness(VanDiedenhovenetal.2012).Icecloudasymmetryparametersareinferredfrommulti-directionalpolarizedreflectancemeasurementsbysearchingfortheclosestfitinalook-uptableofsimulatedpolarizedreflectancescomputedforcloudlayersthatcontainindividualhexagonalcolumnsandplateswithvaryingaspectratiosandroughnessvalues.Theasymmetryparameterofthehexagonalparticlethatleadstothebestfitwiththemeasurementsisconsideredtheretrievedvalue.Forcloudswithopticalthicknesslessthan5,thecloudopticalthicknessmustberetrievedsimultaneouslywiththeasymmetryparameter,whileforopticallythickercloudstheasymmetryparameterretrievalisindependentofcloudopticalthickness.Evaluationofthetechniqueusingsimulatedmeasurementsbasedontheopticalpropertiesofanumberofcomplexparticlesandtheirmixturesshowsthattheicecrystalasymmetryparametersaregenerallyretrievedtowithin5%,orabout0.04inabsoluteterms.Theretrievalschemeislargelyindependentofcalibrationerrors,rangeandsamplingdensityofscatteringanglesandrandomnoiseinthemeasurements.Theapproachcanbereadilyappliedtomeasurementsofpast,currentandfutureairborneandsatelliteinstrumentsthatmeasuremulti-directionalpolarizedreflectancesofice-toppedclouds.

PACS Cloud Retrievals

Onthecloudside,wehaveimplementedtheCloudProalgorithmthatoptimizestheusageoftheHARPhyperangularmeasurementsfortheretrievalofthethermodynamicalphaseandthedropletsizedistributionofwaterclouds.ThisalgorithmisbasedonaparametricfitandlookuptablesbasedonMiecalculationspreviouslyusedbyBreonandGoloub1998,andBreonandBoucher2005.

TheuniquehyperangularimagingcapabilityoftheHARPsensorallowforunprecedentedcoverageofthecloudbowsupernumeraryarcswithpixelresolution,producingdetailedcharacterizationoftheeffectiveradiusandeffectivevarianceoftheclouddropletsizes.Theseadditionalmeasurementsprovidemoredetailedcharacterizationoftheinteractionbetweenaerosolsandclouds.Particularly,theHARPdesignallowsforcontinuouscoverageofthecloudbowfeaturescoveringawideswathimagedarea.Figure4.9showsanexampleofcloudbowretrievalfromtheLMOSexperiment.HARPallowsfortwoapproachesinperformingretrievalswiththecloudbowtechnique:

1- Cloudblowprofilealongthecross-trackswath

2- SinglepixelretrievalwiththeHyperangularsampling

ThesinglepixelretrievalwiththeHyperangularsamplingisauniquecharacteristicoftheHARPsystemandcanprovidehighresolutioncloudmicrophysicalretrievalsinpatchyandheterogeneouscloudfields.ThesameHARPhyperangularfeaturealsoprovidesaclosemonitoringofthemicrophysicalpropertiesoficecrystalslinkedtotheicesurfaceroughness(VanDiedenhovenet.al.2012).

Figure 4.9: Example of cloud microphysical properties retrieved with the unique hyperangular imaging capability of HARP. The top cloud image shows an intensity image of a cloud field collected during the LMOS experiment while the bottom image shows an image of the polarization field (-Q component of the Stokes parameters) emphasizing the structure of the cloudbow. The plot on the right-hand side shows the unique characteristic of HARP which provides a full profile of the cloudbow for each individual pixel throughout the image. In this particular case we show a full cloudbow profile with 150m resolution and its equivalent microphysical retrieval.

4.3 Ocean AsdetailedinSection2,theACEoceanecologyscienceobjectivesrequireanexpansioninthespectralrangeandresolutionofpassiveoceancolormeasurementscomparedtoheritagesensors,thedevelopmentofalgorithmsforderivingplanktonpropertiesfromlidarsubsurfacescatteringreturns,anevolutioninsatelliteinversionalgorithms,andtheretrievalofnewoceanecosystemandcarboncycle

properties.ACEpre-formulationstudieshavefocusedonkeyadvancesinoceanretrievalsneededtoprepareformissionlaunch.Inparticular,algorithmdevelopmentstudieshavetargeted(1)inversionsforinherentopticalpropertiesandaddedvalueofremotesensingmeasurementatultravioletwavelengths,(2)evaluationofremotessensingofphytoplanktonfunctionalgroups,(3)advancementofretrievalsforcoloreddissolvedorganicmatterandattenuationcoefficientsoverthefullrangeofopen-oceantonear-shoreenvironments,(4)evaluationofphysiologicalsignaturesinchlorophyllfluorescenceretrievals,(5)expansionofnetprimaryproduction(NPP)algorithmstoaccommodatetheadvancedoceangeophysicalparametersretrievedduringACE,(6)advancedunderstandingofphytoplanktonphysiologytoreduceuncertaintiesinNPP,(7)assessmentofRamanscatteringimpactsonoceancolorinversionalgorithms,(8)developmentofspacelidarretrievalsofglobalplanktoncarbonstocks,(9)demonstrationoflidarretrievalsofvertical(depth)structureinoceanproperties,and(10)improvedalgorithmsforatmosphericcorrections.ThefollowingsubsectionsbrieflydescribeadvancesmadeonthesetopicsinpreparationforACE.

Inversion Algorithms for Inherent Optical Properties

Semi-analyticalalgorithms(SAAs)provideonemechanismforinvertingthecolorofthewaterobservedbytheACEoceanradiometerintoinherentopticalproperties(IOPs).FewSAAsarecurrentlyparameterizedappropriatelyforretrievalfromallwatermassesandallseasons.Acommunity-widediscussionoftheselimitationswasthereforeinitiatedandtwoworkshopsconductedtoaccelerateprogresstowardconsensusonaunifiedSAAframework.TheseeffortsresultedinthedevelopmentofgeneralizedIOP(GIOP)modelsoftwarethatcouldbeappropriateforimplementationduringtheACEmission.TheGIOPpermitsisolationandevaluationofspecificmodelingassumptions,constructionofSAAs,developmentofregionallytunedSAAs,andexecutionofensembleinversionmodeling.ApreliminarydefaultconfigurationforGIOP(GIOP-DC)wasidentifiedduringtheworkshops,withalternativemodelparameterizationsandfeaturesdefinedforsubsequentevaluation.AnexampleglobalimageofphytoplanktonabsorptionbasedonMODISAquadataisshowinFigure4.10anddetailsontheGIOPalgorithmwerepublishedinWerdelletal.(2013a).

FollowingdevelopmentoftheGIOPalgorithm,anadditionalstudywasconductedtoevaluatethesensitivityofSAAstotheassumedconstantspectralvaluesforseawaterabsorptionandbackscatteringandspectralshapefunctionsforabsorptionandscatteringbyphytoplankton,non-algalparticles,andcoloreddissolvedorganicmatter(cDOM).Thestudyrevealedthatuseoftemperature-andsalinity-dependentseawaterspectrasignificantlyelevatesSAA-

Figure 4.10 Example GIOP global product based on MODIS Aqua data.

derivedparticlebackscatteringcoefficients,reducesnon-algalparticleandcDOMvalues,andleavesphytoplanktonabsorptioncoefficientsunchanged.DetailedresultsfromthestudywerepublishedinWerdelletal.(2013b).

Inparallelwiththeaboveinversionalgorithmdevelopments,workwasalsobeenconductedonimprovingtheGarver-Siegel-Maritorenaalgorithm,whichisoneoftheleadinginversionalgorithmsappliedtoheritageoceancolordata.Thisworkaimedatimprovingvariouscomponentsofthemodel,includingphytoplanktonabsorption,slopeofparticulatebackscattering,absorptionbynon-algalparticlesanddissolvedmatter,therelationshipbetweenreflectanceandbackscattering-to-absorptionratio,reflectionandrefractionprocessesattheair-seainterface,andextensionofthemodelintotheUVdomainmeasuredbyACE.Theoutcomeofthisworkimprovedthespectralaccuracyofthemodel,yieldinglowerretrievalbiases.

AnimportantsourceofuncertaintyintheUVisassociatedwithinsitumeasurementsofoceanicopticalproperties,includingthespectralabsorptioncoefficient.AfocusedstudywasconductedtoquantifyuncertaintiesintheUVspectralregionforthecommonly-employedfilterpadmethodofmeasuringtheabsorptioncoefficientofmarineparticles.Thesemeasurementsrequireacorrectionforpathlengthamplificationresultingfromlightscatteringbyglassfiberfiltersontowhichparticlesareconcentrated.Thiscorrectioncanbeexpressedastherelationshipbetweenthereferenceopticaldensityofasuspensionofparticles,ODs,andthecorrespondingopticaldensityofparticlesonfilters,ODf(Stramskietal.2015).Dedicatedlaboratoryexperimentsutilizinganimprovedmeasurementconfigurationwithasampleplacedinsideanintegratingsphereofbench-topspectrophotometerandcoveringawiderangeofseawaterenvironmentsindicatedthattheuseofpreviouslyestablishedcorrectionsforthevisiblespectralregion(Stramskietal.2015)canbeusedinthe350–400nmrangewithreasonableuncertainties(~13%randomerrorand~10%bias).TheuncertaintiesincreaseatshorterUVwavelengthssuggestingthatfurtherworktodevelopUV-specificcorrectionsareneededtoprovidethehighestaccuracythroughouttheUVregion.

Phytoplankton Functional Groups

SincethelaunchoftheSeaWiFSsatellite,ithasbecomeincreasingapparentthatunderstandingoceanecosystemdynamicsandcarboncyclingrequiresamorerefinedseparationofphytoplanktontypesinthesurfaceocean.Accordingly,theoceanecosystemscienceobjectivesforACEincludetheretrievalofprimaryphytoplanktonfunctionalgroups.Buildingfromearlierproof-of-conceptapproaches,astudywasthereforeconductedtoinvestigatetheuseofinversionmodelsforidentifyingkeyphytoplanktongroups.ThestudywasfocusedondistinguishingtwoparticularphytoplanktontypesknowntodominatesurfacepopulationsinthenorthernArabianSea.Thestudyidentifiedconditionsunderwhichtheinversionapproachwassuccessfulinretrievingspecificphytoplanktongroupsandwhenthecurrentapproachisnotsuccessful.Inaddition,thestudyindicatedthatthecurrentstate-of-the-artapproachalreadyshowspromisefor

qualitativegroupseparations,butthatquantitativeassessmentsrequirefurtheralgorithmdevelopment.DetailedresultsfromthestudywerepublishedinWerdelletal.(2014).

Additionalstudieswerealsoundertakentoevaluatealternativeapproachesforassessingphytoplanktonfunctionalgroups.IntheworkofChaseetal.(2017),diagnosticpigmentmarkersforphytoplanktongroupsweredescribedasGaussiandistributionsandthentheseGaussianapproximationsusedtodecomposespectralreflectancedataintophytoplanktongroups.InthestudyofCartlettandSiegel(2018),spectralderivativeanalyseswereconductedtoidentifyfunctionalgrouppigmentmarkers.Akeyoutcomeofthisstudywasthatitdemonstratedtheimportanceofaccuratewaterleavingreflectancedataacrossthefull350to700nmhyperspectralrangeoftheACEoceancolorinstrument.

Colored dissolved organic matter and attenuation

InSection3,abriefsummaryisprovidedonprogressininstrumentdevelopmentoftheC-OPSsystem.Datafromthisinsitusystemhasbeenevaluatedintermsofdevelopingimprovedalgorithmsforretrievalsofin-waterspectraldiffuseattenuationcoefficients(Kd)andcDOMabsorption(aCDOM).Forexample,theleftpanelinFigure4.11illustratestheuseofC-OPSdataforevaluatingsubsurfaceretrievalsofKdfromalidar.TherightpanelinFigure4.11showsparticularlyencouragingresultsfromanemergingglobalalgorithmforaCDOMretrievalsat440nm.ThisresultisparticularlynoteworthyinitsrobustcapabilitiesovercDOMvaluesspanningthreedecadesofdynamicrange,fromclear,deep-oceanconditionstoturbid,shallowcoastalwaters(Hookeretal.2013).

Chlorophyll Fluorescence

Satellitechlorophyllfluorescence(FLH)retrievalshavethepotentialforprovidingcriticalinformationonphytoplanktonstandingstocks,physiology,and

Figure 4.11 A prototype HSRL algorithm and refined aCDOM (440) algorithm based on C-OPS Kd data.

photosynthesis,butimprovementsareneededtooptimizefluorescenceretrievalcapabilitiesforACEandinterprettheunderlyingphysiologicalsignal.Studieswerethereforeconductedto(1)evaluatesourcesoferrorinexistingMODISFLHproductsbasedoninsitudataandradiativetransfersimulationsand(2)improveunderstandingofphysiologicalmarksusingfielddataandFLHretrievalsfromMODISandtheKoreanGeostationaryOceanColorImager(GOCI).DuringACEpre-formulation,significantprogresswasmadeonthephysiologicalinterpretationofFLHdatathatisessentialtoACEoceanecosystemscienceobjectives.ThestudyofBehrenfeldetal.(2013)providedthefirstglobalanalysisofMODISfluorescencedataandlinkedspatial-temporalpatternsinfluorescencequantumyieldstothepresenceorabsenceofiron-limitedphytoplanktonpopulations.ThesubsequentstudyofWestberryetal.(2013)furtherdevelopedthislinkbetweenelevatedquantumyieldsandironstressbymergingMODISdatawithfieldironenrichmentstudies.Finally,inthestudyofO’Malleyetal.(2014),chlorophyllfluorescencedatafromthegeostationaryGOCIsensorwasusedtoimprovedescriptionsoflight-protectionmechanismsinphytoplankton,termednon-photochemicalquenching(NPQ),andtodescribehowtheextentofNPQvariedoverseasons.

Primary Production

Oneofthekeypropertiesofoceanecosystemswithrespecttofisheriesproductionandglobalbiogeochemistryistherateofphytoplanktonnetprimaryproduction(NPP).Approximately50%ofbiosphericNPPiscurrentlyassignedtotheglobalphytoplankton(Fieldetal1998)andthisrateshowsclearfluctuationswithclimatevariability(Behrenfeld2001,2006).Theseestimates,however,arebaseduponNPPalgorithmsapplicabletothelimitedretrievalcapabilitiesofheritageoceancolorsensors.FortheACEmission,observationalcapabilitieswillbegreatlyexpanded.Accordingly,newNPPalgorithmsareneededtoaccommodatetheadditionalretrievedgeophysicalpropertiesandthustorealizethepotentialimprovementsinNPPassessments.Tothisend,arevisedNPPalgorithmframeworkwasdevelopedcalltheCarbon,Absorption,Fluorescence,andEuphotic-resolving(CAFÉ)model(WestberryandBehrenfeld2014).TheCAFÉmodelbuildsaroundnewretrievalsofphytoplanktoncarbon(Behrenfeldetal.2005,Westberryetal2008)ratherthanthetraditionalapproachofestimatingNPPfromchlorophyll.Themodelthenincorporatesadditionalinformationonphytoplanktonabsorptionspectra,functionalgroups,andinformationonironstressfromfluorescencequantumyieldretrievalstoimprovethedescriptionofphotosyntheticefficiencies.TheCAFÉapproachwasfurtherrefinedbySilsbeetal.(2016)andshowntohaveimprovedperformanceoverallotherNPPalgorithmswhencomparedtofielddatasets.

PhysiologyAfundamentalchallengeregardingtheinterpretationofACE(andheritageOCsensor)oceanecosystemdataisreliablyrelatingobservableproperties(opticalproperties)togeophysicalproperties(e.g.,planktonstocksandcomposition)andbiogeochemicalprocesses(e.g.,NPP,carbonexport)ofinterest.ForassessmentsofNPP,akeyissueisunderstandingphysiologicalacclimationtime-scalesandtheir

species-specificvariations.Workwasthereforeconductedtoevaluatehowphotosyntheticpropertieschangeovertimeindifferentphytoplanktonspecies.Inparticular,changesinpigmentabsorptionandbiomassaccumulationweremeasuredduringasimulatedmajorregimeshiftinlightandnutrientavailabilitysimulatingadeep-mixingevent.Thecollectionofphotosyntheticpropertiesrevealedthattrade-offsbetweenenergeticinvestmentstonon-photochemicalquenching,carbonfixation,andgrowthunderlietheextremelyrapidresponseofdiatomstolowlight/highnitrogenconditionscomparedtogreenalgae(Figure4.12).TheseresultssuggestthatrelatingphytoplanktonchangesobservedbyACEtochangesinNPPandcarbonbiogeochemistrywillrequireinformationonbothcommunitycompositionandtimescalesofchangeinthephysicalenvironment.

Figure 4.12 Rapid recovery of T. pseudonana growth (left) compared to D. tertiolecta (middle) following a regime shift from high light/low N to low light/high N. NPQ (right) changes show that the diatom dispenses with investments into photoprotection to maintain high growth capacity

Satellite Lidar RetrievalsAuniqueandpowerfulaspectoftheACE

missionwillbeitssimultaneous measurementsofoceanpropertieswithalidarandoceanradiometer.TheabilitytoretrievesubsurfaceplanktonpropertieswithaspacelidarwasunprovenduringinitialformulationoftheACEmissionconceptandwasthereforeahigh-prioritytargetforpre-formulationinvestigations.Alidarspecificallydesignedforoceanretrievalshasneverbeenflowninspace.However,theatmosphericCALIOPlidarhasbeenproducingglobaldatasince2006andprovidedanopportunityforaproof-of-conceptevaluationanddevelopmentofalgorithmsfortheACElidar.ThroughacollaborationofresearchersfromOregonStateUniversity,LaRC,andPlymouthMarineLab,thefirstsuccessfulsatellitelidarretrievalsofphytoplanktoncarbonstocksandtotalparticularorganiccarbonwasachieved(Figure4.13),thusdemonstratingthefeasibilityandimportanceoftheadvancedlidarcapabilitiesplannedfortheACEmission.DetailedresultsfromthestudywerepublishedinBehrenfeldetal.(2013).

Withtheabovenotedsuccess,afollow-onstudywasconductedofplanktonbloomdynamicsandclimatesensitivitiesforthepolarregions.Solarelevations,periodsofpolarnight,andpersistentcloudconditionshavemadeoceancolorobservationsofthepolarregionshistoricallyproblematic.Lidarretrievalsofplanktonpropertiescanbemadebetweencloudsandthroughsignificantcloudandaerosolloads(Figure4.14).Inaddition,theactivenatureoflidarmeasurementsmeansthatoceanobservationscanbemadethroughouttheyear,includingpolarnight.Withtheseadvantages,CALIOPphytoplanktonbiomassretrievalswereusedtounravelkeyaspectsofphytoplanktonbiomassannualcyclesandtodeterminehemisphericdifferencesinecologicalversusicecoverchangeeffectsondecadal-scalebiomassvariations(Behrenfeldetal.2016).

Figure 4.13 CALIOP lidar based global ocean surface particulate carbon concentration.

Figure 4.14

Theaforementionedaccomplishmentsrepresentonlyasmallfractionoftheoceanecologyadvancesthatcanberealizedwithasatellitelidar.Notably,CALIOPwasnotdesignedforoceanapplicationsandthushasfundamentallimitiationsrestrictingfurtheradvances.Inparticular,CALIOPdoesnothavetheverticalretrievalresolutiontoprovidecriticalinformationplanktondepthdistributions(Schulienetal.2017)ortheappropriatedetectionsystem(e.g.,HSRL)fordirectlyseparatingabsorbingandscatteringcoefficients.RequirementsfortheACElidaridentifiedintheOceanEcosystem(andother)STMaddresstheshortcomingsofCALIOPforoceanapplications.Averydetailedreviewofafutureocean-optimizedlidarisprovidedinHoestetleretal.(2018),alongwithahistoricalaccountoflidarapplicationsinmarinestudies.

Airborne HSRL Retrievals

AsdiscussedinSection3.3,advancesinairbornelidarinstrumentationduringtheACEerahaveenabledfirst-everoceanprofilingmeasurementswiththeHSRLtechnique.Inadditiontoprovidingdepth-resolvedmeasurementsascalledforintheACEoceanSTM,theHSRLtechniqueenablesindependentmeasurementoflightattenuationandparticulatebackscatter.TheretrievalissimilarinnaturetothatappliedintheatmosphereforindependentmeasurementofaerosolextinctionandbackscatterandisdescribedinHostetleretal.(2018).HSRL-1oceanretrievalshavebeenprocessedandarchivedfromtheShipandAtmosphereBio-opticsResearch(SABOR)experimentandthreedeploymentsoftheNorthAtlanticAerosolandMarineEcosystems(NAAMES)mission.Figure4.15showsbothoceanandatmosphereretrievalsfromtheMay2016NAAMESdeployment.InitialassessmentsoftheparticulatebackscatteranddiffuseattenuationcoefficientfromSABORhavebeenpublished(Hairetal.2016andSchulienetal.,2017)andsimilarcomparisonsareongoingwiththeNAAMESdata.Inaddition,initialcomparisonsoftheHSRLdataproductswiththecurrentsatelliteretrievals(MODIS,VIIRS)areongoingandpromising.TheairborneHSRL-1retrievalsdemonstratethatoceanSTMrequirementscanbemetbylidarandHostetleretal.(2018)describehowthiscapabilitycanbeexpandedtoaspaceborneACEHSRLinstrument.

Notably,Figure4.15illustratestheadvantageofcoincidentHSRLmeasurementsforadvancingoceancoloralgorithms.TheatmosphericdatashowadensesmokelayerfromCanadianforestfiresthatwasadvectedovertheWesternAtlantic.Thisstronglyabsorbingsmokelayerwouldchallengetraditionaloceancoloralgorithms,yettheHSRLoceanretrievalsareunaffected.OceanmeasurementssuchasthesefromaspaceborneHSRLwouldprovidealargedatabasefortestingandimprovingOCIatmosphericcorrectionalgorithms,whichinturncouldbeappliedtotheentireOCIdataset.Moreover,Stamnesetal.(2018)usedtheHSRLaltitudeinformationontheheightoftheaerosollayerstoimprovepolarimeterretrievalsofbothoceanandatmosphericopticalparameters(e.g.oceanparticulatebackscatter,diffuseattenuationcoefficient,andaerosolopticalthickness).ThisworksetthestagetoprobesynergiesbetweentheACEHSRLandpolarimeterforjointatmosphere-oceanretrievals.

Figure 4.15 Retrieved atmospheric profiles of backscatter and aerosol type simultaneously sampled ocean backscatter (bbp) and attenuation (Kd) along a flight track from off the Virginia coast to just off the Nova Scotia coast during the May 2016 NAAMES transit flight.

Raman Scattering

RamanscatteringhasthepotentialtosignificantlyaffectACEretrievalsofinherentopticalproperties(thus,derivedgeophysicalproperties)retrievedwithsemi-analyticalinversionalgorithms(seeabove).StudieswerethereforeconductedtoevaluatethemagnitudeofthesepotentialerrorsanddevisedalgorithmstocorrectforRamaneffects.ThesestudiesdemonstratedthaterrorsinparticulatebackscatteringcoefficientsresultingfromRamancontaminationcanbeaslargeas30%inclearoceanregions(LeeandHuot2014).BothanalyticalandempiricalmethodsweredevelopedtoremovetheRamancontributionfromremotesensingreflectancesandthenappliedtosatelliteoceancolordatafromOMIandMODIS(Leeetal.2010,2013,Westberryetal.2013).ThesestudiesestablishedimportantandusefulapproachesforaddressingtheRamanscatteringissueduringanalysesofACEoceancolordata.MethodsfromtheWestberryetal.(2013)studywereincorporatedintotheGIOPframework(McKinnaetal.2016).

Atmospheric Correction

Atmosphericcorrectionreferstoremovingtheatmosphericcontributiontothetop-of-atmosphere(TOA)radiancefromtheradianceobservedbyanoceancolorsensor.Theatmosphericcontributionis85%to90%overtheopenocean(depth>1000km)and~95%andmoreovercoastalregions(e.g.ChesapeakeBay)andmainlyconsistsofRayleighscatteredphotonsbyairmoleculesandMiescatteredphotonsbyaerosols.Theformervariesasλ-4andthelatterasλ-nwhere,nvariesfrom~0to2.Theaccuracyoftheatmosphericcorrectiondependsonmicrophysicalandopticalpropertiesofaerosols(e.g.,particlesizedistribution,complexindexofrefraction),whichvaryspatiallyandtemporally.

AsapartofACEpre-formulation,radiativetransfer(RT)studieswereconductedtounderstandabsorbingandnon-absorbingaerosoleffectsonsatelliteoceancolorretrievals.ResultsshowedthattheatmosphericcorrectionalgorithmproposedbyGordonandWang(1994)typicallyworksverywellforopenoceanconditionswhereaerosolsaremostlyoceanicinnatureandnon-absorbing.Inthepresenceofabsorbingaerosols(e.g.,dust,smoke,industrialpollution),errorsinretrievedoceancolorbecomeverylarge,often>20%.Resultsalsoshowedthatknowledgeofsinglescatteringalbedo(ωo)andaerosollayerheight(h)areextremelyimportantwhenabsorbingaerosolsarepresent.AsillustratedinFigure4.16,anerrorof1kminaerosollayerheightchangestheTOAradianceat412nmby~0.7%,yieldingan~7%changeinwater-leavingradianceattheoceansurface.Thiserrorincreaseswithincreasingaerosolopticalthickness(τaer)intheatmosphere.TheRTsimulationsstudieswerefurtherextendedtoincludeabsorbingaerosolsinthenearUVpartofthespectrum.Resultsshowedthatabsorbingaerosolsunderlowaerosolloadingconditions(amajorconcerninatmosphericcorrection)couldbedetectedwithACEmeasurementsat340and380nm.

Duetotheimportanceofaccurateatmosphericcorrectionsinthepresenceofabsorbingaerosols,additionalACEsupportedalgorithmdevelopmentstudieswereconductingbasedontheBayesianapproachtoinverseproblems.Inthisapproach,thesolutionisexpressedasaprobabilitydistributionthatmeasuresthelikelihoodofencounteringspecificvaluesoftheinputvariables(spectralmarinereflectance)giventheobservedoutputvariables(spectraltop-of-atmospherereflectanceinthevisibleandnearinfrared).Thisallowsforcomputationofboththeconditionalexpectationofthemarinereflectancetobecomputedandtheconditionalcovariance(ameasureofuncertainty),the“p-value”(quantifyingthelikelihoodofanobservationwithrespecttothemodel),andassessmentofsituationswhereobservationsandmodeloutputareincompatible(p-value<0.05).DetailsoftheapproachandresultsarereportedinFrouinandPelletier(2014).

Thefeasibilityofusingmulti-angularmeasurementsoftop-of-atmospherereflectancetoestimateaerosolabsorptioneffectsonmarinereflectanceretrievalswasalsoinvestigated.Themethodconstrainsthespectralextrapolationofscatteringpropertiesobservedinthenearinfraredbyavalueoftheaerosolabsorptioneffectobtainedintheshort-wavelengthbands.Aseparateestimationoftheaerosolabsorptionopticalthicknessandverticaldistribution(variablesthatgoverntheaerosolabsorptioneffect)isnotnecessary.First,thetop-of-atmospherereflectanceiscorrectedformolecularandaerosolscatteringusingspectralbandsinthenearinfraredand/orshortwaveinfrared,asintheclassicatmosphericcorrectionscheme.Second,theresidualsignalinallviewingdirections,!TOA’,composedoftheaerosolabsorptioneffectandthemarinesignal,normalizedbytheatmospherictransmittanceisrelatedtoanabsorptionpredictor,i.e.,afunctionrepresentingthedirectionaleffectofanabsorbingaerosol,namelytheproductofmolecularreflectance,!mol,andairmass,m*.Figure4.17illustratesthemethodforfineandcoarseaerosols.Neglectingaerosoltransmittance,themarinereflectance(0.02inthiscase)isobtainedbyextrapolatingtherelationbetween!TOA’/Tmoland!molm*tozeroairmass,whereTmolisthemoleculartransmittance.

Figure 4.16 Percent change in the top-of-reflectance (TOA) at 412 nm as a function of aerosol layer height. Reflectance values at different heights are normalized with respect to the reflectance values at 3 km with single scattering albedo (ωo) of 1.0. The simulations are for solar zenith angle of 30o, view zenith angle of 38o and relative azimuth angle of 90o. The aerosol optical thickness defined at 869 nm is 0.25.

4.4 Aerosol-Ocean

New wind speed-AOD relationship

Wehaveinvestigatedthewindspeeddependenceofseasprayaerosolopticaldepthat532nm(AOD532)basedonfiveyearsofsatelliteretrievalsofaerosolopticalpropertiesfromtheCloud-AerosolLidarwithOrthogonalPolarization(CALIOP)onboardtheCALIPSOsatelliteandthewindspeeddatafromtheAdvancedMicrowaveScanningRadiometer(AMSR-E).TheresultsofouranalysisforAOD532vs.surfacewindspeed(U10)relationshipindicatethreedistinctregions(Figure4.18).At

Figure 4.17 Simulated rTOA’/Tmol, versus rmol m* for fine aerosols (left) and coarse aerosols (right). Wavelength is 412 nm and aerosol optical thickness is 0.3. Wind speed is 5 m/s and marine reflectance is 0.02. Solar zenith angle is 30 deg., viewing azimuth angle varies between 0 and 80 deg., and relative azimuth angle is 90 deg. Aerosol scale height varies from 1 to 8 km (8 km correspond to mixed aerosols and molecules). The fine aerosols are defined by rf = 0.1 µm, sf = 0.20, and mf = 1.40 - 0.010i (single scattering albedo of 0.94), and the coarse aerosols by rc = 2.0 µm, sc = 0.30, and mc = 1.55 - 0.002i (single scattering albedo of 0.88).

Figure 4.18 The relationship between CALIPSO AOD532 and AMSR-E wind speed. Dotted line indicates that the AOD – wind speed relationship for U10 >24ms−1. Circles and error bars show mean values and standard deviation of AOD for each 1 ms−1 wind speed bin, respectively. Logistic regression relationship between AOD532 and wind speed is shown with the solid black line.

lowwindspeed(U10≤4ms-1)seasprayproductionisminimalandaerosolpropertiesareexpectedtobedominatedbytransport.UndersuchconditionsAOD532islow,weaklydependentonsurfacewindspeedandrepresentativeofbackgroundmarineaerosols.Atanintermediatewindspeedvalues(4<U10≤12ms-1)regressionanalysisrevealedaconstantslopeof0.0062sm-1.Athighwindspeedvalues(U10>12ms-1)theAOD532-windspeedrelationshiplevelsoff.AnalysisofCALIPSO-retrievedAOD532andAMSR-Ewindspeedsuggeststhatatveryhighwindspeedvaluesaerosoleffectsonopticalturbidityofatmosphereappeartoleveloff,asymptoticallyapproachingvalueof0.15.Theseresultshavebeenpublishedin(Kiliyanpilakkil&Meskhidze,2011).

Spaceborne observations of the lidar ratio of marine aerosols.

Wehavedevelopedanewmethodtocalculatethelidarratioofseasprayaerosolusingtwoindependentsources:theAODfromtheSynergizedOpticalDepthofAerosols(SODA)algorithmandtheintegratedattenuatedbackscatterfromCALIOP.Withthismethod,theparticulatelidarratiocanbederivedforindividualCALIOPretrievalsinsingleaerosollayercolumnsovertheocean.Theglobalmeanlidarratioforseasprayaerosolswasfoundtobe26sr,roughly30%higherthanthecurrentvalueprescribedbyCALIOPstandardretrievalalgorithm.Dataanalysisalsoshowedconsiderablespatiotemporalvariabilityinthecalculatedlidarratioovertheremoteoceans(Figure4.19).Thecalculatedaerosollidarratiosareshowntobeinverselyrelatedtothemeanoceansurfacewindspeed:increaseinoceansurfacewindspeed(U10)from0to>15ms−1reducesthemeanlidarratiosforseasprayparticlesfrom32sr(for0<U10<4ms−1)to22sr(forU10>15ms−1).SuchchangesinthelidarratioareexpectedtohaveacorrespondingeffectontheseasprayAOD.TheoutcomesofthisstudyarerelevantforfutureimprovementsoftheSODAandCALIOPoperationalproductandcouldleadtomoreaccurateretrievalsofseasprayAOD.TheseresultshavebeenpublishedinDawsonetal.(2015).

Figure 4.19 Probability density function of clean marine aerosol lidar ratio for selected AMSR-E wind speed regimes. The μ parameter shows the mean of each distribution.

5 Field Campaigns

ACEleveragedtheadvancesintechnicaldevelopmentandreadinessofbothinstrumentconceptsandtheirrelatedalgorithmsdevelopmentmadepossiblewithESTOsupport.Accordingly,ACEhasinitiatedaseriesoffieldexperimentswiththepurposeofbetterdefiningthemeasurementcapabilitiesoftheACEairborneinstrumentsimulators,aswellasadvancethecorrespondingL1andL2algorithms.ThesedeploymentsincludethePolarimeterDefinitionExperiment(PODEX)inJanuary-February2013,theRadarDefinitionExperiment2014(RADEX-14)inMay-June2014,RADEX-15(November-December,2015)andAerosolCharacterizationfromPolarimeterandLidar(ACEPOL,October-November,2017).

ACEscienceandinstrumentteamshavealsobeenleveragingthescientificdemandbythelargercommunityfortheuseoftheirACEinstrumentsimulatorsintheircampaigns.NASA,DoE,NSFaswellasEuropeanpartnershaveprovidedsupportforACEscientistsandinstrumentteamstoparticipateinaseriesofhighprofilefieldcampaigns.Amongthesecampaignsare1)StudiesofEmissionsandAtmosphericComposition,CloudsandClimateCouplingbyRegionalSurveys(SEAC4RS),2)Ship-AircraftBio-OpticalResearch(SABOR),3)DerivingInformationonSurfaceConditionsfromColumnandVerticallyResolvedObservationsRelevanttoAirQuality(DISCOVER-AQ),4)NorthAtlanticAerosolsandMarineEcosystemsStudy(NAAMES),5)ObseRvationsofAerosolsaboveCLoudsandtheirintEractionS(ORACLES),6)the2012AzoresCampaign,7)theGPMOlympicMountainExperiment(OLYMPEX,coordinatedwithRADEX-15),8)DoE’sTwo-ColumnAerosolProject(TCAP),aswellastheEuropeanUnionAtlanticMeridionalTransect(AMT)program.

5.1 Aerosol Related Campaigns Roughlyfiftyairbornefieldcampaignswerecarriedoutinthe2007-2017periodthatwerebothrelevanttoACEaerosolscienceandinvolvedparticipationofmulti-anglepolarimetersand/orlidarsthatreceivedsupportfromACE(seesections3.2and3.3foradescriptionoftheseinstruments).Ofthosefieldcampaigns,twenty

There were 50 ACE relevant field campaigns performed in the 2007-17 period: 20 deployed a lidar 4 deployed a multi-angle polarimeter 26 deployed both together 18 used high altitude aircraft, such as the ER-2 or Global Hawk 4 were coordinated with shipborne observations 33 were conducted in the Continental United States (CONUS) 12 were primarily over the ocean 2 were at ligh latitudes in North America (Alaska and Canada) 6 were in tropical regions of Africa, Central America or the Caribbean

werewithalidaraloneandfouramulti-anglepolarimeteronly.26fieldcampaignshadboth.Avarietyofaircraftwereemployed,primarily,butnotexclusively,fromtherosterofNASAAirborneScienceProgramaircraft.18ofthefieldcampaignsdeployedinstrumentsonhighaltitudeaircraft,suchastheER-2andtheGlobalHawk,anidealanalogfororbitalremotesensing.Amajorityoffieldcampaigns(33)wereperformedwithinCONUS,andasignificantfraction(12)wereprimarilyovertheocean.AsmallernumberwereperformedinAlaska/NorthernCanada,theCaribbean,orsub-SaharanAfrica.Fourfieldcampaignshadcoordinatedmeasurementswithinstrumentsdeployedonresearchships.

ThefirsthalfoftheACEperiod(2007-2011)sawthedeploymentofasinglemulti-anglepolarimeter(RSP),andtwolidars(CPLandHSRL-1),andonlyonefieldcampaignutilizingahighaltitudeaircraft(TC4).ThesecondhalfoftheACEperiodhadgreateractivity.Newmulti-anglepolarimetersweredeployedin2013(AirMSPI),2016(AirSPEX)and2017(AirHARP),whiletheHSRL-2lidarbeganoperatingin2012.TheHSRL-1wasupdatedin2013toprovidebetterobservationsofinwaterproperties,andwasdeployedinallfourfieldcampaignsthathadshipborneobservations(SABORandthethreeNAAMESdeployments).Roughlyhalfofthecampaignsemployedhighaltitudeaircraft.

Table 5.1 2007-2017 field campaigns relevant to aerosols, with involvement by either multi-angle polarimeters or lidars (or both) that received support from ACE. Dates and locations are approximate. Field campaigns highlighted in black received direct funding from ACE, and are described in more detail in

Field campaign Date Location Aerosol AirHARP AirMSPI RSP AirSPEX CPL DIAL/HSRL HSRL-1 HSRL-2 Aircraft ShipCATS/CALIPSO val. 1-2007 US east coast Lidar yes B-200San Joaquin Valley 2-2007 California Lidar yes B-200CHAPS/CLASIC 6-2007 Oklahoma Lidar yes yes B-200TC4 7-2007 Costa Rica Lidar yes ER-2Caribbean 1 1-2008 Caribbean Lidar yes B-200ARCTAS Spring 4-2008 Alaska Lidar yes B-200ARCTAS Summer 6-2008 NW Canada Pol, Lidar RSP1 yes B-200Birmingham 9-2008 Alabama Pol, Lidar RSP1 yes B-200CALIPSO validation 1-2009 Eastern US Lidar yes yes B-200RACORO 6-2009 Southern Great Plains Pol, Lidar RSP1 yes B-200Ocean Subsurface 9-2009 US east coast Pol, Lidar yes B-200, CIRPAS Twin OtterCALIPSO validation 4-2010 US east coast Lidar yes B-200CALIPSO/Gulf Oil Spill 5-2010 Gulf of Mexico Pol, Lidar RSP1 yes B-200CALNEX 5-2010 Calfornia Pol, Lidar RSP1 yes B-200, NOAA P-3CARES 6-2010 California Pol, Lidar RSP1 yes B-200COCOA 8-2010 Caribbean Pol, Lidar RSP1 yes B-200DISCOVER-AQ '11 7-2011 DC-Baltimore Lidar yes P-3, B-200EPA 8-2011 SE Virginia Lidar yes B-200DEVOTE 10-2011 US east coast Pol, Lidar RSP1 yes B-200, UC-12CALIPSO validation 3-2012 Eastern US Lidar yes B-200DC3 5-2012 Central US Lidar yes DC-8, G-VTCAP 7-2012 Cape Cod Pol, Lidar RSP1 yes B-200HS3 '12 9-2012 Atlantic Ocean Lidar yes Global HawkAZORES 10-2012 Azores Pol, Lidar RSP1 yes P-3DISCOVER-AQ '13 1-2013 Central California Lidar yes B-200, P-3PODEX 1-2013 S. California Pol, Lidar PACS (no data) yes RSP2 yes ER-2SEAC4RS 8-2013 CONUS, Gulf of Mexico Pol, Lidar yes RSP2 yes yes ER-2, DC-8HS3 '13 8-2013 Atlantic Ocean Lidar yes Global HawkDISCOVER-AQ '13 fall 9-2013 Houston Lidar B-200, P-3HyspIRI '13 10-2013 US west coast Pol yes RSP2 ER-2Pre-PACE 4-2014 US west coast Pol yes ER-2, Twin OtterHyspIRI '14 spring 4-2014 US west coast Pol RSP2 ER-2CALIPSO validation 6-2014 Eastern US Lidar yes B-200Bermuda (Pre-SABOR) 6-2014 Bermuda Pol, Lidar RSP1 B-200SABOR 7-2014 Atlantic Pol, Lidar RSP1 yes B-200 R/V EndeavorDISCOVER-AQ '14 7-2014 Denver Lidar yes B-200, P-3HS3 '14 8-2014 Atlantic Ocean Lidar yes Global HawkHyspIRI '14 fall 10-2014 US west coast Pol, Lidar RSP2 ER-2 CalWater2 1-2015 California Pol, Lidar yes yes ER-2CCAVE 8-2015 California Lidar yes ER-2NAAMES '15 11-2015 NW Atlantic Pol, Lidar RSP1 yes C-130 R/V AtlantisRADEX/OLYMPEX 11-2015 Washington state Pol, Lidar yes yes ER-2, UND CitationSPEX-PR 2-2016 US west coast Pol yes yes ER-2NAAMES '16 5-2016 NW Atlantic Pol, Lidar RSP1 yes C-130 R/V AtlantisImPACT-PM 7-2016 Calfornia central valley Pol, Lidar yes yes ER-2, Twin OtterORACLES '16 9-2016 SE Atlantic Pol, Lidar yes RSP1, RSP2 yes ER-2, P-3LMOS 5-2017 Lake Michigan Pol, Lidar yes UC-12ORACLES '17 8-2017 SE Atlantic Pol, Lidar RSP2 yes P-3NAAMES '17 9-2017 NW Atlantic Pol, Lidar RSP1 yes C-130 R/V AtlantisACEPOL 11-2017 S. California Pol, Lidar yes yes RSP2 yes yes yes yes ER-2

Multi angle polarimeters Lidars

this document. The presence of Lidars or Polarimeters (abbreviated “pol”) in each field campaign is described in the ‘Aerosol’ column, and participation of specific instruments indicate to the right.

Table5.1isanoverviewofAerosolrelevantairbornefieldcampaignsintheACEperiod(2007-2017)thatincludedtheparticipationofACEfundedlidarsand/orpolarimeters.ACEfundedmissionsarehighlightedinblack.WhatfollowsinabriefdescriptionofsomeofthemostimportantmissionsofrelevancetoAerosols.WestartwithadescriptionofACEfundedcampaigns(PODEX,ACEPOL)followedbythesameforsomeofthemostimportant(foraerosolremotesensing)otherfieldcampaigns(ARCTAS,SEAC4RS,ORACLES).Weconcludewithabrieferdescriptionofotherrelevantcampaigns.Pleaseseesections3.2and3.3foradescriptionofpolarimeterandlidarinstruments,respectively,andsection4.1foraerosolrelevantalgorithmsfromthoseinstruments.

ACE Polarimeter Definition Experiment (PODEX)

TheACEinstrumentrequirementscallforapolarimetertoprovideretrievalsofaerosolopticalandmicrophysicalproperties.Thepolarimeterdesignscurrentlyunderdevelopmentvarywidelyintheirdesign,spectralandangularcoverage,andradiometriccalibration/uncertaintyrequirements.Therefore,thePOlarimeterDefinitionEXperiment(PODEX)missionwasfundedbyACEandconductedin2013tohelpoptimizepolarimeterdesign,assessthepolarimeteraerosolandcloudretrievals,andintercomparevariousmethodsofretrievingaerosolopticalproperties(e.g.,absorption,phasefunction,refractiveindex).

PODEXwasconductedfromtheArmstrong(formerlyDryden)FlightResearchCenter(AFRC)facilityinPalmdale,CaliforniaduringJanuaryandFebruary2013.ThreepolarimetersweredeployedfromtheNASAER-2(809)aircraft:theAirborneMultiangleSpectroPolarimetricImager(AirMSPI),theResearchScanningPolarimeter(RSP),andthePassiveAerosolandCloudSuite(PACS).AdditionalsensorsontheER-2includedtheAutonomousModularSensor(AMS)whichprovidedmultiwavelengthcalibratedradiancesandcloudproductsgeneratedusingMODISalgorithms,theCloudPhysicsLidar(CPL)whichprovidedreal-timeandpostflightaerosol/cloudbackscatterprofilestolocateandidentifyaerosolandcloud

Figure 5.1 ER-2 flight tracks during PODEX.

ER-2 Flight Tracks

layers,andtheSolarSpectralFluxRadiometer(SSFR)whichprovidedspectrallyresolvedshortwaveirradiancemeasurements.TheER-2flightsconductedduringPODEXwerecoordinatedwithairborneandground-basedmeasurementsacquiredduringthethirddeploymentoftheDISCOVER-AQEarthVenture-Suborbital(EV-S1)project.DISCOVER-AQusedtheNASAP-3andNASALaRCKingAiraircrafttostudyairqualityovertheCaliforniaSanJoaquinValleyduringthisperiod.TheNASAP-3aircraftwasequippedwithseveralinsitusensorsthatmeasuredtracegasconcentrationsandaerosoloptical(scattering,absorption)andmicrophysical(size,composition)properties.OfparticularinterestisthePolarizedImagingNephelometer(thePI-Neph)developedbythePACSgroupforthedetailedmeasurementoftheP11andP12elementsofthescatteringmatrixoftheaerosolparticles,whichcanbedirectlycomparedtothepolarimetricretrievalsofthePACS,AirMSPIandRSPsensors(DolgosandMartins,2014).TheKingAirdeployedtheLaRCHighSpectralResolutionLidar-2,whichisaprototypeofthemultiwavelengthlidarcalledforbyACEtoprovidelayer-resolvedretrievalsofaerosolopticalandmicrophysicalretrievals.TheDistributedRegionalAerosolGriddedObservationNetwork(DRAGON)ofAERONETsun-skyphotometerswasalsodeployedinthesouthernpartoftheSanJoaquinValleyandprovidedmeasurementsofaerosolopticaldepth(AOD)andretrievalsofcolumnaveragedaerosolopticalandmicrophysicalproperties.

DuringPODEX,theER-2acquired49hoursofsciencedataduring10flightsbetweenJanuary14andFebruary6,2013.Theflightsweredesignedsothatthepolarimetersacquireddataoverbright(desert,snow)anddark(ocean)scenes,duringlightandmoderateaerosolloadingconditionsinmaritime,ruralandurbanregions,andoverfog,stratus,stratocumulus,andcirrusclouds.DatawerealsoacquiredoverthecalibrationtargetslocatedatRosamondDryLake,Ivanpah,andRailroadValley.TheflightsovertheSanJoaquinValleycontainedseverallegsabovetheDRAGONAERONETsensorsandwerecoordinatedwiththeDISCOVER-AQaircraftsothatcorrelativemeasurementsofaerosolopticalandmicrophysicalpropertieswereobtained.DISCOVER-AQalsoconductedflightsovertheoceantosupportthePODEXflights.ThePODEXflightswentwell,withtheexceptionoftheflightonJanuary28whenRSPlostoperationoftheSWIRbandsduetooperatorerror.ThisalsopreventedtheoperationoftheseSWIRbandsonsubsequentPODEXflights.Postmissionrepairsandcalibrationshowedthatthevisiblechannelswerenotaffected.

FinalversionsofthePODEXpolarimeterdatasetshavebeenarchived,andarepubliclyavailable.AirMSPILevel1(L1,atsensorcalibratedpolarimetricradiances)dataareattheLaRCAtmosphericScienceDataCenter(ASDC):https://eosweb.larc.nasa.gov/project/airmspi/airmspi_table

SeethePODEXAirMSPIdataqualitystatement(Dineretal,2017)andvanHartenetal.(2018)formoredetails.WhileLevel2(L2,geophysicalproduct)dataarenotavailabletothepublic,theyhavebeengeneratedandanalyzedinpublicationssuchasXuetal.,(2016and2017).

PACSdatahavenotbeenmadeavailableduetoanunforeseendetectorlinearityproblem,whichimposeserrorsgreaterthanacceptableforL2productgeneration.However,thisexperienceinformeddesignchangeswiththenextgenerationHARP,AirHARPandHARP2instruments.

CalibratedL1RSPdataareavailableattheGISSRSParchive:

https://data.giss.nasa.gov/pub/rsp/PODEXAsmentionedpreviously,noRSPSWIRchanneldataareavailablefortheJanuary,28,2013flightorsubsequentPODEXflights.L2productsareavailableatthisarchiveforbothliquidandicephaseclouds,basedonalgorithmsandanalysisdescribedinAlexandrovetal(2012a,2012b,2015,and2016),andvanDiedenhovenetal.(2013).RSPPODEXdatahavealsobeenusedtoinvestigatesnowproperties(Ottavianietal,2015),andtheMAPP(Microphysicalaerosolpropertiesfrompolarimetry)simultaneousaerosolandoceanretrievalalgorithm(Stamnes,etal.,2018)canbeappliedtoPODEXdata.Furthermore,aneuralnetworkhasbeenusedtocomputeaerosolpropertiesoverland(DiNoiaeta.,(2017)),andmethodstoderiveaerosollayerheighthavealsobeendetermined(Wuetal,2016).

FortheRSPliquidcloudretrievals,notethatcomparisonsofRSPcloudbowandAMSabsorbingbanddropletsizeretrievalsdonotshowthetypeofbiasespreviouslyreportedincomparisonsbetweenMODISandPOLDERcloudproducts(BreonandDoutriaux-Boucher,2005).Infact,thebiasesareconsistentwiththequasi-adiabaticverticalvariationsinliquidwatercontentobservedforthestratocumuluscloudsinPODEXandourunderstandingoftheweightingfunctionsassociatedwith1.6,2.2and3.7µmspectralbands(Platnick2000).Thatis,thereisanegligibledifferencebetweencloudbowanddropletsizesretrievedusingthe3.7µmabsorbingbandwhilethe2.2µmdropletretrievals,withaweightingfunctiondeeperintothecloud,are1-2µmsmaller(Alexandrovetal.2015).

Multi-instrumentscenecomparisonswereaprimarygoalofPODEX,andthejustificationforthewidevarietyofscenetypesobservedduringthecampaign.ComparisonscanbeperformedforbothLevel1andLevel2dataproducts.L1comparisonsshowhowwellcalibratedinstrumentradiances,reflectancesorpolarimetricobservationsagreeforagivenscene.Factorsthatcanimpactthelevelofagreementincludedatageolocation,instrumentcalibrationandrandomerrors(noise).Animportantaspectofthesecomparisonsistoaccountforexpectedmeasurementuncertaintyinastatisticallyappropriatemanner.Knobelspiesseetal.(2019)comparedL1datafromtheAirMSPIandRSPinstrumentsforavarietyofscenes.TheyfoundtheLevelofAgreement(AltmanandBland,1983,BlandandAltman,1986)tobelargelyconsistentwithexpectationsofmeasurementuncertaintiesforthepairedinstruments.Exceptionswereforthe470nmreflectancechannel(AirMSPIroughly6%higherthanRSP)andforpolarimetricobservationsofdarkoceans,wherethecontributionofrandomerrorsislargerthanexpectedintheuncertaintymodel.Theseresultsrepresentasignificantimprovementoverthe

initialanalysisofPODEXdata,andaretheresultofyearsofrefinementstothegeolocation,characterizationandcalibrationofbothinstruments.Knobelspiesseetal.(2019)alsoreportstheuncertaintymodelsforbothinstruments,andcorrectionsmadetogeolocationduetowingflexintheER-2aircraft.Giventheseresults,onecanexpectthatintercomparisonsofthesamescenesatL2wouldfindthesameLevelofAgreement,providedthattheL1-L2algorithmsaresuccessfulandthattheinstrumentshavesimilarlevelsofinformationcontent.Thisparticularanalysis,however,hasyettobeperformed.

Aerosol Characterization from Polarimeter and Lidar (ACEPOL) TheACEPOLfieldcampaignconsistedofnineflightsbetweenOctober19thandNovember9th,2017,withafullcomplementofmulti-anglepolarimeterandlidarinstrumentsdeployedonthehighaltitudeER-2aircraft.LikePODEX,itwasbasedattheArmstrongFlightResearchCenterinPalmdale,California.

AcollaborationbetweenNASA(ACEmission)andtheNetherlandsInstituteforSpaceResearch(SRON),theACEPOLcampaigntargetedawidevarietyofscenetypesinordertotestandvalidateobservationsfromthefourmulti-anglepolarimetersonboardtheER-2.Twolidarsprovidedobservationsoftheaerosolverticalprofileforcontextandcomparison.ACEPOLalsowassupportedbytheCALIPSOmissionforvalidationpurposes.

Instruments deployed on the ER-2 aircraft during ACEPOL Multi-angle Polarimeters AirHARP (1st time on the ER-2, 2nd field campaign overall) AirMSPI RSP SPEX (1st full field campaign) Lidars CPL HSRL-2 Other coordinated measurements Column Aerosol ground observations from AERONET and MPLnet Rosamond Dry Lake surface characterization (October 25th)

Figure 5.1 Flight tracks for the ACEPOL field campaign. Avarietyoftargetsceneswereidentifiedpriortothemission,andmostoftheseweresuccessfullyobservedwithfullyfunctionalinstruments.Thisincludedobservationsofdarkoceansandbrightlandsurfaceswithnocloudsandminimalaerosolload.ArosetteofcoordinatedflightsoverRosamondDryLakewereperformed,whileateamfromJPLheadedbyCarolBrueggecharacterizedthelandsurfaceBRDF.CoordinatedunderflightsofCALIPSOandCATSorbitallidarswereperformed,aswellascoincidentobservationswithsatelliteimagerssuchasMODIS,MISRandVIIRS.Theseweredoneinbothcloudyandclearconditions.OverflightsofAERONETandMPLnetgroundsiteswereperformedinlowandmoderateaerosolloadconditions.Unfortunately,overflightsforhigheraerosolloadscouldnotoccurduetoanatypicallackofaerosolsintheregion.HigheraerosolloadswereencounteredforflightsovercontrolledburnsinArizona,butthesedidnothavecoincidentgroundobservations,sovalidationdataofaerosolretrievalsintheseareasarelimitedtothelidarobservations.

Figure 5.2 HARP True color imagery from November 9th, 2017 of a controlled burn in Arizona. Calibration and geolocation in this scene is preliminary. OneofthemostvaluableACEPOLsceneswasobservedonOctober23rd,2017foraflighttrackoffLosAngelesinthevicinityofSanClementeandSantaCatalinaislands.Thisflighttrackwasperformedinthesolarprincipalplane(theheadingwasalignedwiththesolarazimuthangle)incloudfreeconditions.Thismeansthatthemulti-anglepolarimeterswereabletoobservethereflectedoceansunglintatorneartheBrewster’sAngle,wherethereflectedlightisfullypolarized.Themulti-anglepolarimeterobservationscanthereforebecomparedatthemaximumpotential

Figure 5.3 Carol Bruegge (JPL) and team with the GroundMSPI at Rosamond Dry Lake.

rangeofpolarizationvalues,whicharerarelyobservedinthefield.Furthermore,thepresenceofislandsintheflighttrackcanbeusedtoconfirmscenegeolocation,whiletheoverflightoftheUSCSeaPRISMAERONETsiteprovidescolumnaerosolandoceanreflectancepropertiesforvalidation(Zibordietal,2009).Forthesereasons,thisscene,amongothers,isofgreatinteresttotheACEPOLinstrumentteamsastheyprocess,refine,validateandcomparetheirdata.

Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) TheARCTASfieldcampaignwasconductedintwothree-weeksegments.ThefirstwasinAlaskainAprilof2008,andthesecondinJuneandJulyofthesameyear.Threeaircraftweredeployedforthiscampaign,ofwhichtheLangleyB-200hadapayloaddevotedtoremotesensing,containingtheHSRLandRSPinstruments(RSPwasdeployedinthesummercampaignonly).ARCTAShadfoursciencegoals:1)understandingmid-latitudepollutioninflux,2)understandingborealforestfires,3)characterizingaerosolradiativeforcingfortheseevents,and4)understandingthechemicalprocessesbehindtheseevents(Jacobetal,2010).

TheobjectivesofARCTASwerebroadandinvolvedalargecommunity.Fromtheperspectiveofaerosolremotesensingwithmulti-anglepolarimetersandlidars,ARCTASdataprovidedusefulinsight.Burtonetal.(2013)usedARCTASdata(amongothers)toderiveanHSRLclassificationapproach,andvalidateagainstCALIPSOresults.Knobelspiesseetal(2011)investigatedretrievalsofaerosolopticalpropertieswithcombinedpolarimeterandlidardata.BecauseoftheintensityandageofbiomassburningsmokeobservedinthesummerphaseofARCTAS,thisisaninvaluabledatasetfortheinvestigationoftheevolutionsuchaerosolsintheirfirsthourstodays.

Studies of Emissions and Atmospheric Composition, Clouds and

Climate Coupling by Regional Surveys (SEAC4RS).

AlthoughPODEXprovidedaveryimportantinitialdatasetforevaluatingthepolarimeterdesignsandretrievaltechniques,itdidnotprovidethefullsuiteofmeasurementtargetsthatarerequiredtofullyevaluatetheseinstruments.MeasurementsofveryhighaerosolloadingssuchasdenseforestfiresmokeanddustwerenotobtainedbecausesignificantforestfiresanddustoutbreaksdidnotoccurwithinrangeoftheER-2duringthePODEXmeasurementperiod.TherewasalsonoopportunityduringPODEXtomeasuresmokeordenseaerosolaboveclouds,whichpresentsaparticularlyimportantandchallengingretrievalsituationforthepolarimeters.TherewererelativelyfewmeasurementsofcirrusduringPODEX,particularlyincaseswheretherewerenounderlyingclouds.

Fortunately,theSEAC4RSexperimentprovidedanotheropportunitytoobtaintheseconditions(Toonetal.2016).ThreeaircraftweredeployedduringSEAC4RS:theNASADC-8andER-2andtheSPECLearJet.AsinPODEX,AirMSPIandRSPweredeployedfromtheER-2andacquireddatasetsimportantforevaluatingthepolarimetermeasurementsandretrievals.Likewise,CPLandSSFRwerealsopartoftheER-2payloadasinPODEX.However,PACSwasnotontheER-2forSEAC4RS.InplaceofAMS,theenhancedMODISAirborneSimulator(eMAS)wasdeployedontheER-2andprovidedhighspatialresolutionimageryofaerosolandcloudfields.TheDC-8payloadincludedseveralinsituinstrumentsformeasuringaerosolandopticalproperties,anairborneHSRLinstrument,andthenew4STARinstrumentforprovidingsunandskymeasurementsfromwhichaerosolopticalandmicrophysicalpropertiesareretrievedinamannersimilartoAERONET.TheSPECLearJetcarriedinsitusensorsformeasuringcloudandiceparticlesizedistributionsandliquidandicewatercontent.AsinPODEX,anetworkofAERONETSun-SkyphotometerswasdeployedoverthesoutheasternUStoprovidemeasurementsofaerosolopticaldepth(AOD)aswellasretrievalsofaerosolabsorption.

DuringSEAC4RS(June2012-June2013),theDC-8andER-2eachflewmorethan150scienceflighthours;theLearJetflewover50hours.Inthefirstpartofthecampaign,theaircraftwerebasedoutoftheAFRCinPalmdale,CAandflewoutofEllingtonFieldnearHouston,TXfortheremainder.AlthoughtheSEAC4RSflightswereconcentratedmoreheavilyinthesoutheasternUSandtheGulfofMexico,therewereseveralflightsoverthewesternUStoobservetargetsofinterest;inparticular,flightstargetedsmokefromfiresinCaliforniaandOregon.OfparticularinterestwastheflightonAugust6,2013,wheninstrumentsfrombothaircraftwereabletoobserveandmeasuresmokepropertiesabovestratocumulusclouds.AirMSPIandRSPresearchteamsusedthesedatasetstodevelopandevaluateaerosolandcloudproperties.Forexample,AirMSPIaerosolretrievalresultsforAOD,singlescatteringalbedo,sizedistributionforafewcasesareconsistentwiththosederivedfromAERONET(Xuetal,2017).Unfortunately,theAirMSPIin-flightcalibratordidnotworkaswellasinthePODEXfieldcampaign,anissuethathasbeenresolvedforsubsequentmissions(vanHartenetal,2018).InitialRSPretrievalsofcirruscloudparticlesize,opticalthickness,andasymmetryparametercomparefavorablywiththosederivedfromcoincidenteMASretrievals.Duringthesameexperiment,thePACSgroupflewtheRPIportableimagingpolarimeter(analogoustoPACS)andthePI-Neph(DolgosandMartins,2014,Espinosaetal,2017)fordatavalidationonboardtheNASADC-8aircraft.BothinstrumentsarebeingusedforthedevelopmentofACEaerosolandcloudretrievalsaswellaspotentialvalidationfortheER-2polarimeters.

EvaluationofaerosolalgorithmsandaerosolpropertiesretrievedfromACEinstrumentswillrelysignificantlyonAERONETretrievalsofcolumn-averagedaerosolproperties.Currently,theAERONETretrievalsrequireasetofaminimumaerosolopticaldepth(at440nm)of0.4andasolarzenithanglegreaterthan50°toobtainhighestquality(L2.0)dataproducts.SEAC4RSmeasurementsprovidedan

opportunitytotesttherepresentativenessoftheAERONETabsorptionretrievalsforalimitednumberofthesehighAODcasesaswellasmanyothercasesatlowerAODlevels.SEAC4RSdatacanbeusedtocomparedifferenttechniquesformeasuringandretrievingaerosolabsorption.

ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES)

TheORACLESfieldcampaignwasdevotedtotheobservationofpoorlyunderstoodaerosolsabovecloudsintheSouthEastAtlanticOcean,wheresuchphenomenonaredominantintheaustralspring.Theobservationofaerosolsaboveclouds,andcorrespondingradiativeforcingandcloudindirecteffects,hasbeendifficultformostspacebornesensors(Yuetal.,2013,Knobelspiesseetal.,2015),althoughnewalgorithmshavebeendevelopedthatdeterminetheAerosolOpticalDepthwithassumptionsaboutaerosolmicrophysicalproperties(Jethvaetal.,2013,2014,Meyeretal.,2015,Sayeretal.,2016).ORACLEShadthefollowingscientificobjectives(quotedfromtheORACLESoverviewathttps://espo.nasa.gov/ORACLES/content/ORACLES_Two-page_ORACLES_Flyer):

1. DeterminetheimpactofAfricanBB(BiomassBurning)aerosoloncloudpropertiesandtheradiationbalanceovertheSouthAtlantic.

2. Acquireaprocess-levelunderstandingofaerosol-radiationinteractionsandresultingcloudadjustments,andaerosol-cloudinteractions,thatcanbeappliedglobally.

3. Substantiatefuturemeasurementsbygatheringtestbeddatasetsthatcanbeusedtoverifyandrefinecurrentandfutureobservationmethodsandsimulationtechniques.

Figure 5.4 Schematic of ORACLES mission plan

ORACLESconsistedofthreedeploymentstotheSouthEastAtlanticOcean.In2016,theER-2andtheP-3werebasedinWalvisBay,Namibia.TheER-2carriedapayloadofremotesensinginstruments,includingtheAirMSPIandRSPpolarimeters,andtheHSRL-2lidar.TheP-3wasprimarilyintendedforin-situaerosolandcloudsampling,althoughitalsocarriedasecondcopyoftheRSPandacloudandprecipitationradar.Deploymentsin2017and2018weremadewiththeP-3only,andwerebasedontheislandofSãoTomé,SãoToméandPríncipe.Intheseyears,theP-3hadbotharemotesensingandanin-situsamplingrole,andfortheformertheHSRL-2wasmovedtotheP-3(alongwiththeRSPandradarsthatwerealsodeployedin2016).

Severalotherfieldcampaignswereconductedinthisregionatroughlythesametime.TheUKCLARIFY(CloudsandAerosolRadiativeImpactsandForcing:Year2017),headedbytheUKMetOffice,deployedtheFAAMBAe-146aircraftfromAscensionIslandin2017,withwhichtheORACLESP-3performedcoordinatedflights.TheDOEfundedLASIC(LayeredAtlanticSmokeInteractionswithClouds)campaigndeployedtheARMMobileFacilitytoAscensionislandformultipleyears.TheFrenchAEROCLO-sA(AerosolRadiationandCloudsinsouthernAfrica)campaignenhancedgroundsitesinNamibiaandSouthAfricaanddeployedtheF20aircrafttoWalvisBay,Namibiain2017.MoredetailsonthesecampaignscanbefoundinZuidemaetal.(2016).

Whiledataprocessingandanalysisarestillunderway,severalpapershavealreadybeenpublishedwithORACLESresults.Xuetal.,(2018)presentedanoptimalestimationretrievalalgorithmthatdeterminedcloudopticaldepth,dropletsizedistributionandtopheightalongwithaerosolopticaldepthandmicrophysicalpropertiesfromAirMSPIobservations.Segal-Rozenhaimeretal.(2018)createdaneuralnetworkalgorithmtodeterminecloudopticalpropertiesfromRSPobservations.ItcanbeconsideredacomplementtootherRSPcloudretrievalalgorithms,anditmeanttobeasteppingstonetoneuralnetworkbasedsimultaneousretrievalsofaerosolandcloudopticalpropertiesfromthatinstrument.Burtonetal.,(2018)presentedananalysisofHSRL2ORACLESobservationsaswell.Atleasttwentyotherpublicationsareinprogress,bothfromORACLESfundedteammembersandexternalcollaborators.

Additional Field Missions BothAirMSPIandRSPweredeployedontheER-2aspiggybackinstrumentsonflightsthatwereconductedoverCaliforniaaspartoftheHyspIRIairbornecampaign(https://hyspiri.jpl.nasa.gov/airborne,Leeetal,(2015)).TheprimaryinstrumentsflownintheseflightsweretheAirborneVisible/InfraredImagingSpectrometer(AVIRIS)andtheMODIS/ASTERAirborneSimulator(MASTER).RSPwasdeployedontheER-2forelevenflightsbetweenOctober2013andAugust2014,andAirMSPIwasdeployedforsevenoftheflightsduringAprilandMay2014.Theywerenotdeployedsimultaneously.Amongotherachievements,someoftheseobservationswereusedtoimproveatmosphericcorrectiontechniques,bywhichaerosolimpacts

ontheremotesensingoflandandoceanopticalpropertiesareremoved(Kudelaetal.,(2015),Palaciosetal.,(2015),Thompsonetal.,(2015)).

RSPandtheHSRL-1werealsodeployedonaNASALangleyKingAiraircraftduringJuly-August2014fortheShip-AircraftBio-OpticalResearch(SABOR)experiment(Hostetleretal.2014;Sinclairetal.2014;Powelletal.2014).Twenty-fiveresearchflightswereconductedoverthewesternAtlanticOceancoincidentwithin-waterbio-opticalmeasurementsmadefromtheR/VEndeavorin2014.Amongotherinstruments,theR/VEndeavordeployedanabovewaterpolarimeter,theHyperSAS-POL(Ottavianietal.,2018),ofuniquevalueincomparisontotheairborneRSP.Thesedatahavebeenusedtoimprovealgorithmsforlidarandpolarimeterretrievalsofoceanpropertiesandatmosphericcorrectionsforoceancolorretrievals.SimultaneousretrievalofaerosolandoceanpropertieswastestedwithdatafromthiscampaignfornewretrievalalgorithmsdescribedinGaoetal,(2018)andStamnesetal.,(2018).

TheNorthAtlanticAerosolsandMarineEcosystemsStudy(NAAMES)fieldcampaignwascarriedoutinmultipledeploymentsbetween2015and2018.TheprimaryNAAMESfocusisoceanplanktonecology(Behrenfeldetal.,2017),however,oneofthebaselinescienceobjectivesisrelevantforACE:“ResolvehowremotemarineaerosolsandboundarylayercloudsareinfluencedbyplanktonecosystemsintheNorthAtlantic”Forthiseffort,NAAMESdeployedtheR/VAtlantisfromitsbaseatWoodsHoletotheNorthAtlanticfortransectsalongthe40˚Wlongitudeline,andtheNASAC-130aircraftfromSt.John’sinNewfoundland,Canada.RemotesensinginstrumentsontheC-130includedtheHSRL-1lidar(optimizedforinwateroceanprofiling),theGeoCAPEairbornesimulator(GCAS),theSpectrometerforSky-ScanningSun-TrackingAtmosphericResearch(4STAR)andtheRSP.Whilecloudfreeconditions,idealforpassiveremotesensing,areinfrequentintheNorthAtlantic,thiscampaigngatheredvaluablelidardata,wasusedforGCASatmosphericcorrectionalgorithmdevelopment(Zhangetal.,2018),provideddataforpolarimetriccloudretrievalvalidation(Alexandrovetal.,2018).SomeofthecloudfreesceneswereusedinpreviouslymentionedinGaoetal,(2018)andStamnesetal.,(2018).Furthermorefundamentalocean/aerosol/cloud

Figure 5.5 NAAMES mission graphic

relationshipswereexploredwithbothremotesensingandinsitusampleddata(Quinnetal.,2017,Sanchezetal.,2018).

Inadditiontotheabovementionedcampaigns,theairborneHSRL-2acquireddatawhileflyingontheNASALaRCKingAirduringfouratmosphericfieldmissionsconductedsince2012.ThefirstwasduringtheDOETwo-ColumnAerosolProject(TCAP)inJuly2012overtheAtlanticOceaneastofCapeCod(Müller et al. 2014).ThefollowingthreedeploymentswereinsupportoftheNASADISCOVER-AQcampaignsheldin1)theCaliforniacentralvalleyinJanuary-February,2012,(Ferrareetal.2013,Hostetleretal.2013)HoustoninSeptember2013,and3)DenverinJuly-August2014(Scarinoetal.2013,2014).Approximately260sciencehoursofdatawereacquiredbytheHSRL-2duringatotalof77scienceflightsduringthesefourmissions.

Beyondsupportingthescienceoftheseparticularmissions,HSRL-2dataacquiredduringthesemissionsarebeingusedtohelpdevelopandassesstheadvancedlidarretrievalalgorithmsdesignedtomeettheACEaerosolrequirementsdiscussedinSection2.Operationalcodehasbeendevelopedtoimplementtheseretrievals.ThecodehasbeenusedtoproduceACE-likeL2productsincludinglayer-resolvedaerosoloptical(scattering,extinction)andmicrophysical(size,concentration)propertiesin“curtains”belowtheaircraft.Inaddition,DISCOVER-AQoverflewtheDRAGONAERONETnetworkofSun-skyphotometersthathadbeenspecificallydeployedduringthecampaign.TheseinsituaerosolmeasurementsandAERONETretrievalshaveprovenvaluableforassessingtheresultsofthemulti-wavelengthlidaraerosolretrievalalgorithmsandforcomparingdifferenttechniquesformeasuringandretrievingaerosolproperties(Scarinoetal.2013).Thesecomparisonsareongoingasareeffortstoimprovetheaccuracyandspeedoftheretrievals.

5.2 Cloud Related Campaigns Overthepastdecade,NASAhasinvestedheavilyingeneratingsuborbitaldatasetsthataresuitableforaddressingmanyofthegoalsofACEclouds.Acriticalaspectofthisworkisthecoordinatedcollectionofremotesensingdatasets(whichmimicorcanbeusedtomimicmeasurementsthatwillbeusedbyACE)andinsitumeasurementsofavarietyofcloudmicrophysicalpropertiesneededtoassessretrievalapproachesandmeasurementneeds.Relevantdatasetswerecollectedin

Figure 5.7 Map of the 2014 DISCOVER-AQ campaign near Denver, Colorado.

the2007NASATC4campaignwherebothremotesensingfromtheER-2andDC-8areavailableinadditiontoextensiveinsitudatabytheDC-8thatwascollectedinclosecoordinationwiththeER-2.However,theradarsuiteontheER-2didnotidenticallymimicwhatisplannedforACEalthoughdualfrequencyDopplerradardata(WandXbands)werecollectedalongwithpassivemicrowave(AMPR)andvisibleandIRradiancedata(MODISAirborneSimulator).SeveralflightsalsoincludedsubmillimeterwavelengthmeasurementsfromtheCOSSIRinstrument.AnotherdatasetthatcanbeusefultoACEcloudswascollectedduringtheSEAC4RScampaignin2013.InSEAC4RS,theNASADC-8carriedtheAPR-2radarthatcollectedscanningDopplerdataintheKuandKabands.TheStrattonParkEngineeringCorporationLearJetprovidedinsituvalidation.TheprimarytargetinSEAC4RSwasconvectionbothovercontinentallocationsandovertheGulfofMexico.

Morerecently,theACEprogramdirectlysupportedtwocloudcampaignsthe“IntegratedPrecipitationandHydrologyExperiment”(IPHEX)andthe“OLYMPicmountainEXperiment”(OLYMPEX).BothoftheseexperimentswereundertakenincoordinationwiththeGPMGroundValidationteamtothemutualbenefitofbothprograms.

TheACEportionofthesetwocampaignsisalsoknownbytheacronymsRADEX-2014/RADEX-2015(forRAdarDefinitionExperiment)-thoughwewillusethenamesIPHEXandOLYMPEXthroughoutthisdocumentratherthanRADEX.AsthetitleRADEXsuggests,andwhatsetsIPHEXandOLYMPEXapartfromotherfieldexperiments,wasafocuson(andrecognitionoftheneedfor)moreACE-likepackagestotestmeasurementsynergies–andinparticulartheneedformulti-frequencyradardatasetswithcollocatedinsitudata.

Integrated Precipitation and Hydrology EXperiment (IPHEX)

DuringIPHEX,theER-2wasinstrumentedwiththreeDopplerradarsbuiltbyGerryHeymsfield’sgroupatNASAGoddardandcollecteddataintheW,Ka,Ku,andXbands.Inaddition,theER-2carriedtheAMPRandtheCoSMIRmicrowaveradiometers.ThepayloadisshownbelowinFigure5.8asanexampleofdatacollectedduringIPHEX.TheUniversityofNorthDakota(UND)CessnaCitationcollectedcoordinatedinsitudata.ACEfundingaugmentedtheinstrumentationand

totalnumberofCitationflighthours.

Figure 5.8 ER-2 Payload for IPHEX

ACEcloudshadtwospecifictargetsforIPHEX:firstwarmraininshallowconvectionandsecondcloudsproducingstratiformprecipitationthatwasinitiatedassnowabovethefreezinglevel.SeveralIPHEXflightscollecteddatainshallowwarmcumulusinadditiontoextensivemixedphasecloudsandconvectionbothoffshoreandoverthemountainsofNorthCarolina.Table5.2providesamoredetailedbreakdownoftheflighttargets.

Table 5.2. Case studies of note for ACE-related science goals generated during IPHEX. Many of these flight days were funded by GPM GV indicating the fruitful collaboration between ACE and GPM GV.

Date (2014) NotesMay 12: Offshore Convection

Developing convergence line resulted in deepening convection along the Gulf Stream. ER-2 sampled convection in various stages of the lifecycle while the Citation collected data in situ nearby.

May 16: Offshore Frontal Precipitation

Deep frontal clouds and stratiform rain with embedded convection were systematically sampled by the ER-2 while the Citation collected in situ data along sections of the ER-2 track.

May 18: Baroclinic system over the Appalachians

Clouds and precipitation formed by a weak synoptic system in the early morning hours were sampled over the Appalachians by the ER-2 and Citation.

May 19: GPM overpass and warm rain offshore

ER-2 and Citation collected data in a weakening precipitation area offshore. The GPM overpass was closely coordinated by the ER-2 over deeper clouds. Following the overpass, shallower clouds producing warm rain were sampled by both aircraft.

May 28: Warm rain offshore

This flight provided excellent coordinated data in shallow convection and warm rain offshore. ER-2 and Citation were closely coordinated. Likely the best case for warm rain during the campaign.

June 6: Congestus over ground-based

Congestus over Maggie Valley was sampled by ground-based remote sensors in the ACHIEVE instrument suite while the Citation collected data in situ.

OLYMPic mountain EXperiment (OLYMPEX)

WhileIPHEXproducedamulti-frequencyradar&microwaveradiometerdataset,itdidnotincludeanylidar,VIS-IRimagerorpolarimetermeasurements.BetweenIPHEXandOLYMPEx,additionalmodificationswheremadetofacilitatealargerER-2payloadtoenableamoreACE-likepackage,asshownbelowinFigure5.9.DuringOLYMPEXboththeCRS(W-band)andHIWRAP(Ka/Kuband)wereplacedinthesamesuperpodmakingspacetoincludetheMODISairbornesimulator(MAS),aVIS-IRimager.Abackscatterlidar(CPL)replacedCoSMIR(whichflewontheDC-8),andinaddition,twoER-2noseswhereused.OnenosecarryingEXRAD(X-bandradar)andonecarryingAirMISP-2,ascanningimagingpolarimeter.OfthetenER-2flightsduringOLYMPEX,fiveincludedAirMISPIandfocusedoncollectingdatainornearthesolarprincipalplaneoffshoreandfive(morefocusedonheavyprecipitation)includedEXRAD.

Figure 5.9 ER-2 Payload for OLYMPEX

InadditiontotheER-2,theDC-8andUNDcitationalsoparticipatedinOLYMPEX.TheDC-8carriedCoSMIR,APR3(scanningW,Ku,andKabandradars),anddropsondes,andwasfrequentlyflownintightcoordinationwiththeER-2.TheUND

citationwasconfiguredtomeasureinsitucloudmicrophysics(including2D-S,2D-C,CPI,andtwoHVPSorientedhorizontallyandvertically)andonseveraloccasionsmadetwoforays,stoppingtorefuelinbetween.

OLYMPEXtookplaceduringNovember/Decemberof2015,aspartofacampaigntoexamineliquidandmixedphasecloudsoverandoffshoreoftheOlympicPeninsulainWashingtonState[Houzeetal.2017].ThePeninsulawasextensivelyinstrumentedfortheexperiment,asshowninFigure5.10.ThisincludesgroundbasedDopplerradarsatseveralfrequencyandseveralsites(varioussquares)andothergroundsiteswithavarietyofprecipitationgauges,disdrometersandotherinstrumentforcharacterizingthesurfaceprecipitation(whitex’s).

SpecificACE-cloudgoalsinOLYMPEXwereto:1)collectanACE-likedatasetformaritimeconvectionincoldairsectorbehindfronts,2)examinethewarmrainprocessinstratiformcloudsaheadoffronts,and3)collectmixedandice-phasecloudandprecipitationdatainfrontalbands.Eachofthesesituationsrepresentsignificantandspecificchallengesforalgorithmdevelopmentwherecloudprocessesinturbulentverticalmotionsgenerateprecipitationinthecloudthatiseventuallyrealizedatthesurfaceaseitherrainorsnow.DemonstratingthedegreetowhichtheseprocessescanbediagnosedwithactualdataisfundamentaltothegoalsofACEclouds.

Duringtheexperiment,theER-2typically(thoughnotexclusively)fleweitherlongracetracksupordowntheQuinaultValleypassingoverthehighterrainandextendingwelloffshore,orsmallerracetracksflownoffshoreandalignedwiththesolarprincipalplane.TheseracetracksareconceptuallyillustratedinFigure5.10aswithdashedorangelines.Figure5.11showsanexampleofradarreflectivitymeasurementsduringonetransectdowntheQuinaultValleyandextendingoffshoreduringanatmosphericriverevent(thatis,warm-sectorprecipitationfromaparticularlymoistcyclonicsystem).Themelting-layercanbeseendippingdownward(loweringinaltitude)nearthehightopographydueinlargemeasuretoincreasingprecipitationasoneapproachesthewesternslopesoftheOlympicmountains.Allthreeradarfrequenciesshowinterestingstructuresresultingfromchangesinicecrystalhabitsthatwereoftenobservedtobeassociatedwithlayersofsupercooledliquidwater.

PostfrontalconditionswerealsoafocusofER-2flights.Inparticular,postfrontallowcloudswereobserved(withsupportinginsitucloudmicrophysics)onseveralflightsincluding11/14,12/04,and12/13.Allthreeoftheseflightscontainsceneswhichcanbeusedtotestpolarimetricandotherretrievalsforlowclouds,andsuchworkiscurrentlyunderway(Figure5.12).

Figure 5.10 Map of OLYMPEX Ground Network. Dashed orange line shows “racetrack” patterns often used by ER-2. Flights often featured a long transect either up or down the Quinault Valley passing over the high terrain and extending offshore where NPOL radar was making sector scans, or with generally smaller racetracks flown offshore and aligned with the solar principal plane.

Figure 5.11 Example of ER-2 CRS & HIWRAP radar reflectivity data along Quinault Valley

Figure 5.12 Example of Supernumerary and Cloud Bows for stratocumulus observed on 11/24 from AirMSPI (Degree of Linear Polarization; DOLP)

Summary and Discussion of Cloud Related Campaigns

Insummary,theACEsupportedIPHEX&OLYMPEXexperimentshavegatheredmultifrequencyradardatasetswithcoordinatedcollectionofinsitumeasurementsofawidevarietyofcloudmicrophysicalproperties.TheserichobservationaldatasetsarebeingusedbyACEinvestigators(seesection4.2)tostudyACE-cloudmeasurementneedsandretrievalapproaches–thoughwenotethatmuchstillneedstobedoneinthisregard(seesection??).AveryshortrecapoftheIPHEXandOLYMPEXtargetsisgivenbelow,withmoredetailedlistofflightconditionsgiveninTables5.2and5.3.

Asvaluableasthesedatasetsare,itisworthnotingthatafullACE-equivalentdataset,meaningthefull(notbaseline)measurementpackage=multi-frequencyDopplerradar(atleastW+Kabands)+highspectralresolutionlidar(HSRL)+highresolutionVIS-IRimagery(withpolarizationorseparatepolarimeter)+microwaveandsubmillimeterwavelengthradiometersdoesnotexist.OLYMPEXcomesclose,butstilllackedHSRLandsomeshorterwavelength(submillimeter)radiometermeasurements–bothofwhichareexpectedtobevaluableinconstrainingthepropertiesoficeparticles(includingicecrystalhabit)nearcloudtop,andmoregenerally,foropticallythiniceclouds.

Likewise,whileIPHEXandOLYMPEXcasescovermanycloudtypes,agreaternumberandbreadthofcasesremainshighlydesirable.Inparticular,the“full”measurementsuitetargetingavarietyofcirrustypesandthetopsofthickhigh-altitudeiceclouds(convectiveandotherwise)ANDincludesafocusinsitucloudobservationsnearthetopsofhigh-icecloudsshouldbeconsidered.WhileOLYMPEXcertainlyincludesdeepfrontalclouds,theinsitumicrophysicscontainslittledataregardingpropertiesnearcloudtop(inadditiontolackingHSRLandsub-millimeteremissions).

Short summary of IPHEX and OLYMPEX cases:

• IPHEX

o Off-shorewarmraininshallowconvection(2cases)

o Off-shoredeeperconvection(2cases)withstratiformprecipitationthatwasinitiatedassnowabovethefreezinglevel(1case).

o Congestusand/orweakfrontalconvectionoverland(2cases).

• OLYMPEX

o Warmconveyorand/orfronts(icephaseprocesses)(4cases)

o Postfrontalconditions(4flights)

o Onlyafewcases(1?)withembeddedfrontalconvection

Table 5.3. Detailed summary of OLYMPEX ER-2 flights. See also: http://olympex.atmos.washington.edu/missions/Marchand_Mace_LEcuyer_RADEX_Flight_Summary.pdf

Flight Target / Highlight Instruments / Other Notes

11/18 A cirrus shield from an advancing warm frontal overrunning system was approaching from the Southwest while post frontal shallow showers continued in the cold air behind the previous frontal system.

DC8 and ER2 conducted multiple long coordinated race tracks that had their eastern ends near the NPOL radar site. The first race track was oriented east-west and a second race track was set up more southwest-northeast.

The Citation conducted stepped sampling in the advancing ice cloud.

Due to disk switch issue, lost about 1 hour of data from HIWRAP and CRS. Other Instruments nominal.

Noted +30db from showers under high overcast on NPOL.

Stratiform rain at far end of racetrack by end of flight.

11/23 ER2 and DC8 conducted a coordinated flight in an advancing frontal band offshore of the Olympic Peninsula. SW-NE oriented race tracks that were entirely offshore were conducted initially followed by a NW-SE oriented racetrack that had NPOL on SE end.

Citation conducted two flights. Early flight was conducted under the NE end of the early race tracks. Second flight was near the SE end of the later racetrack.

Frontal Rainbands advanced and clouds thickened during the flight. The early racetracks were oriented along the flow while the later race track was oriented perpendicular to the flow.

All instruments nominal except some data loss by Hiwrap and CRS near the end and during a brief period when during the flight.

2nd Citation flight took place after the DC8 and ER2 departed to RTB.

11/24 An Inland cold front with strong northerly post frontal flow over the Olympex region.

All three aircraft targeted orographically enhanced snow along the northern slopes of the Olympics form ~15 to 17 UTC.

Coordinated ER2 and citation sampling of offshore transition from cloud free to extensive stratocumulus cloud cover from ~ 19:30 to 22 UTC.

GMAO model runs indicated a continental aerosol plume being advected offshore, consistent with CPL backscatter and Citation observations of high cloud droplet number concentrations.

ER-2: flew near principal plane during off shore legs,

AirMSPI was in the nose. Some loss (20-30 minutes) Hiwrap (Ku and Ka) early in the flight.

12/1 An occluded front with stratiform precipitation, and significant orographic enhancement.

ER-2 and DC-8 flew coordinated racetracks over Quinault valley (radar sites) 22 to 24 UTC with coincident citation profiles.

Strong rain shadow to the NE of the Olympics.

Variety of ice crystal habits (irregulars, plates, plate aggregates needles), melting layer near 7 kft (was sloping).

EXRAD in nose but went/stayed down after 23:30 UTC. HIWRAP up after 22:06 UTC.

12/3 Strong Frontal/Pre-Frontal Precipitation over the Olympex.

Coordinated data from all three aircraft with a GPM overpass of the high Olympics at 15:22 UTC.

The GPM under-flight was followed by sampling along the Quinault Valley using a racetrack similar to that used during the Dec. 1 flight until ~ 16:45 UTC.

Citation observed a variety of (large aggregates, needles, slide plates on aggregates, capped columns, stellar plates and signficant quantites of supercooled liquid near the time over the GPM overpass.

EXRAD in nose. AMPR 19 GHz channel failed (others OK).

12/4 Post-frontal conditions with shallow convection along the coast after about 10 UTC.

All three aircraft sampled a shallow precipitating convective line that was propagating eastward at 13 UTC just off the coast near the NPOL radar site.

A small, developing low-level offshore cumulus was observed by ER-2 and UND Citation between 17:45 and 19:10 UTC.

Near 13 UTC: Citation observed large amounts of cloud liquid water (at -2 C) and irregular aggregates at colder temperature through the convective line.

~ 17:30 to 19:30 UTC: The ER-2 flew a variety of legs in the solar principal plane. Polarimetric (AirMSPI) data appear to be of very good quality for retrievals.

12/5 Warm sector precipitation with orographic enhancement near coast and heavy snow over the Olympic mountains

All three aircraft sampled heavy snow along the Quinault Valley west of the mountains crossing into the rain shadow to the east. Coordination with Citation in situ was good along the valley. The elongated southwest to northeast racetracks of the ER-2 include ~30 nm of sampling light rain over the ocean.

The ER-2 flew two long/level N-S loops in decaying light rain behind the front all the way north to Vancouver Island that may provide a good target for sensitivity testing.

1450 UTC: Citation sampled cloud along valley at multiple altitudes reporting large aggregates at (-8 C) but no super-cooled LWC; capped columns at -20 C; and small ice crystals extending up to 30 kft

1525 UTC: ER-2 crossed bands of embedded convection offshore

12/8 Offshore precipitation associated with ‘atmospheric river’ event

ER-2 flew independent flight consisting of three 200 nm racetracks offshore including a Suomi NPP underflight (though west of the nadir ground track). EXRAD, HIWRAP, and CRS operated in test mode during the first racetrack but collected science data in light to moderate liquid precipitation along the other two.

Flight hours were split between RADEX and Gerry Heymsfield’s radar test flight.

The North-South racetrack from 21-22 UTC provides an interesting transect across the northern half of the moisture plume. AMPR characterized the transition from moderate stratiform precipitation with embedded convection to non-precipitating liquid clouds.

More than 6” of rain reported at OLYMPEX ground sites over this 24 hour period

12/10 Tail-end of occluded front followed by post-frontal shallow convection.

ER-2 and DC-8 flew beautifully coordinated racetracks, along Quinault continuing well offshore between 17 and 19 UTC..

After 19 UTC, the ER-2 began a sequence of off shore legs in the solar principal plane to sample classic post-frontal shallow convection. Some cirrus was present but also some cirrus free cloud suitable for polarimetric “rainbow” retrievals is also visible.

No ER-2 + Citation: Citation and DC-8 were on station earlier than ER-2. Citation left at 16:40 UTC, with the intent of refueling and returning, but encountered a maintenance issue and was unable to return.

AirMSPI in nose. Worked well. CPL backscatter images suggest noteworthy levels of boundary layer aerosols, perhaps due to very high surface winds.

12/12 Occluding warm front and trailing showers.

The DC-8 dropped 8 dropsondes along a racetrack across front prior to ER-2 takeoff. Citation collected microphysics at 2, 5, 8, 11, 14, 17, and 20 kft along one leg of this track.

ER-2 flew independent NNE-SSW racetracks near the shore aligned with the coast (due to ER-2 delayed takeoff). ER-2 observed overrunning precipitation while northbound and trailing isolated warm showers on the southbound leg.

Citation observed significant supercooled liquid up to -15 C and a wide variety of crystals from large aggregates to columns.

CRS data collection failed (other frequencies operated nominally). eMAS IR channels were also suboptimal.

12/13 A surface low centered over/near Vancouver Island brought cold, moist NW flow and significant post-frontal precipitation to the Washington coast.

17 to 19 UTC: All three aircraft sampled shallow convection with ample upper level ice cloud on/near the coast.

19:31 UTC: Terra underflight by ER-2.

20 to 20:30 UTC: ER2 and Citation sampled low-level clouds and shallow convection off shore. Some lingering cirrus complicates analysis.

ER-2: CRS failed near start of flight, and despite several tries could not be operated. HIWRAP was operated without CRS after ~17 UTC. AirMPSI in the nose, flew in principal plane after 20 UTC.

2DC went out during on the part of mission for about 20 minutes, but otherwise was fine.

5.3 Ocean Related Field Campaigns Foroceanecosystemscienceobjectives,animportantattributeoftheACEmissiondesignisitscombinationofanadvancedoceanradiometer,subsurface-andvertically-resolvinglidar,andadvancedpolarimeter.Eachoftheseinstrumentsprovidesunique,aswellascomplementaryinformationonoceanproperties.However,fieldcampaignsdemonstratingtheutilityofthisinstrumentsuitehavebeenvirtuallynon-existent.Toaddressthisissue,twomajoroceanfieldcampaignshaverecentlybeenconductedinvolvingaircraft,ship,andsatellitemeasurementsandincludinglidar,polarimeter,andoceanradiometermeasurements.Thetwostudieswerereferredtoasthe2012AzoresCampaignandthe2014SABORcampaign.WhileACEpre-formulationfundingcontributedtothesefieldefforts,additionalmajorsupportwasprovidedbyNASA’sOceanBiologyandBiogeochemistryProgram,theCALIPSOmission,theUnitedKingdom’sAtlanticMeridionalTransect(AMT)program,andindividualPIgrants.TheoutcomeofthesecampaignshasbeenhighlyrelevanttobothACEandPACEmissions.Dataanalysisfrombothcampaignsisstillon-going,butearlyresultshavebeenhighlyencouraging.

2012 Azores Campaign

Theprimaryobjectiveduringthe2012Azorescampaign,wastocollectsimultaneousship,aircraft,andsatelliteoceanopticalmeasurementsofparticulatescatteringcoefficients.ThestudyinvolvedcollaboratorsfromOregonStateUniversity,LangleyResearchCenter(LaRC),andPlymouthMarineLabandenjoyedsomesupportbytheCALIPSOandACEprojectsforsupplementalflighthours.SatellitedataincludedlidarmeasurementsfromCALIOPandoceancolormeasurementsfromMODISAqua.AircraftinstrumentsincludedtheNASAGISSResearchScanningPolarimeter(RSP)andtheLaRCHighSpectralResolutionLidar(HSRL).Shipdatafocusedonin-line,continuousflow-throughmeasurementsofsurfacelayerparticulatescatteringandabsorptioncoefficients.

Figure5.13panelashowstheshiptrackandaircraftflighttracksduringthecampaign.Aircraftflightswereoptimizedtooverflyshipinsitumeasurements,aswellasdatacollectedbyCALIOP.Figure5.13panelbshowsmatch-updataforoceanparticulatebackscattercoefficients(bbp)measuredinsitu(blackline),byCALIOP(redline),andasretrievedfromMODISusingcurrentoceancolorinversionalgorithms(greenline=Garver-Siegel-Maritorena(GSM)algorithm;blueline=quasi-analyticalalgorithm(QAA).Fig5.3cshowsmatch-upresultsforbbpfromthe

Figure 5.13 Ocean particulate backscattering coefficients (bbp) during the 2012 Azores campaign. (a) black line indicates ship track, solid orange, dashed peach, and dotted brown lines indicate aircraft tracks. (b) bbp values for (black) in situ ship data, (red) CALIOP retrievals, MODIS (green) GSM and (blue) QAA products. (c) bbp values for the airborne campaigns (see panel a). From Behrenfeld et al. (2013)

airborneHSRL,CALIOP,andMODISdatausingtheGSMalgorithmandcorrespondingtothe3flighttracksshowninFig5.3a.

The2012Azorescampaignwasahighlysuccessfulstudy.Thedemonstratedcorrespondencebetweeninsitu,aircraft,andCALIOPlidarretrievalsprovidedakeyproof-of-conceptfortheACEinstrumentconfigurationregardingoceanecosystemretrievals.ItwasalsothefirstdemonstrationofeffectiveoceanscatteringretrievalsfromCALIOPandyieldedthefirstspacelidaralgorithmforassessingphytoplanktoncarbonandtotalparticulateorganiccarbon(seeSection4above).Initialresultsfromthepolarimetermeasurementsarealsoencouraging,althoughfinaldataanalysisisstillon-going.Anotheroutcomeofthecampaignwasthatithighlightedsomeofthetechnicalchallengesassociatedwithsubsurfaceparticlescatteringmeasurementswithalidar,leadingtosubsequentrevisionsintheHSRLinstrumentdesigninpreparationforthesubsequent2014SABORcampaign.

The2012AMTshiptransectwasalsousedtoconductdailyradiometricandsupportingmeasurementsacross10,000kmoftheAtlanticOceaninanACEfundedefforttoassemblefieldmatchupdataforsatelliteFLHproducts.Similardatawerecollectedduringthe2014SABORcampaign.AnalysisofFLHmatchupdataison-going.

2014 SABOR Campaign

TheShip-AircraftBio-OpticalResearch(SABOR)campaign,was,observationally,agreatlyexpandedexperimentcomparedtothe2012Azoresstudy.SABORwasonlyrecentlyconductedbetween17Julyto7August,2014,soonlypreliminaryresultsarecurrentlyavailable.SABORmeasurementswerefocusedonthestrongecologicalgradientspersistentovertheUSnortheastcontinentalshelfregion(Figure5.14).ThecampaignbroughttogetherseveralPI-leadscienceprojectsfocusedonthebiogeochemistryofplankton,radiativetransfer,andinsituandremotelysensedoceanoptics.Theshipmeasurementcontingencyincluded(1)sevenflow-throughinstrumentscollectingopticaldatafromwhicharederivedadozeninherentopticalpropertiesofseawater,(2)eightinstrumentsforoceanprofilingopticalmeasurementsforassessinginherentopticalpropertiesthroughthewatercolumn,and(3)awidediversityofdiscretesurfaceandsubsurfacesamplecollectionsforassessingbiogeochemicalproperties,includingparticulateandphytoplanktoncarbonandplanktonspeciescomposition.Similartothe2012Azoresstudy,theairborneinstrumentcomplementduringSABORincludedandupgradedLaRCHSRLandtheGISSRSP.FlightswereconductedoutofMassachusetts,Bermuda,andVirginia.Someadditionalflighthoursforthecampaignweremadepossiblewith

Figure 5.14 hip track and sampling stations during SABOR.

additionalsupportfromCALIPSOandACEprojects.SupportingsatellitedatawereprovidedbyCALIOP,MODISAqua,andNPPVIIRS.

WithrespecttoACEoceanecosystemscienceobjectives(aswellasatmosphericscienceobjectives),datacollectedduringthe2014SABORcampaignwillbehighlybeneficialforthedevelopmentofadvancedsatelliteretrievalalgorithms.TheupgradedHSRLusedduringSABORwillallowassessmentofdesignimprovementsfortheACElidar(Figure5.51).InwaterandaircraftpolarimetricmeasurementsduringSABORishighlyrelevanttotheACEobjectiveofusingaspace-basedpolarimetertoaddressatmosphericandoceanrelatedscience.Furthermore,theextensiveship-basedopticalandbiogeochemicalmeasurementscollectedduringSABORwillprovidecriticalinsightsonalgorithmdevelopmentforretrievingkeygeophysicalpropertiesfromACEremotesensingdata.ThesemeasurementsincludedtheassemblageandtestingofaninstrumentpackageformeasuringwatercolumnInherentOpticalProperties(IOPs)(Figure5.16),whicharepropertiesfundamentaltoACEOceanEcosystemobjectives.Thepackageemployedstate-of-the-artsensortechnology,includingcustomMASCOTandSequoiaLISSTsensorswhich,incombination,measuredthefullangularvolumescatteringfunctionforlightscatteringinwater.Theinstrumentsalsomeasuredthedissolvedphaseandattenuationinanopenpath(notpumped)configuration.Preliminaryanalysesindicatethatresultantdataareifthehighestqualitypossible.

Figure 5.16: Inherent optical property instrument package deployed during SABOR.

Figure 5.15 Preliminary HSRL results from the SABOR campaign. Top panel = vertical distributions of aerosol backscatter. Bottom panel = subsurface ocean total backscatter ratio. Data from a single aircraft transect conducted on July 30, 2014.

NAAMES Campaign

TheNAAMESprojectwasfundedasanEarthVentureSuborbital2mission,butprovidedextensivedatarelevanttotheACEmission.NAAMESobservationsincludediverseoptical,chemical,andecologicalship-basedmeasurements,airborneremotesensingwithanadvancedHSRL,theRSP,ahyperspectraloceancolorsensor(GCAS),anddownwardradiancesensor(4STAR).NAAMESencompassedfourfielddeploymentstargetingspecificeventsintheannualplanktoncycleinthesubarcticAtlantic.NAAMESscienceobjectivesfocusedonunderstandingdriversoftheannualphytoplanktonbloomandlinksbetweenoceanecosystems,aerosols,andclouds.ShipdeploymentswerelargelybasedoutofWoodHole,MA.,whiletheairbornecampaigndeployedfromSaintJohns,Newfoundland.AsthefinalNAAMEScampaignwascompletedonlyshortlybeforepreparationofthecurrentACEfinalreport,mostdataanalysesremainon-going.

Potential locations for future field studies of marine organic aerosols

Oceansurfacewaterscontainlargeconcentrationsofsmallparticulatesincludingphytoplankton,algae,bacteria,viruses,fragmentsoflargerorganismsandorganicdetritus.OrganicmatterintheoceanscontributestooneofthelargestactivereservoirsoforganiccarbononEarth.Agrowingbodyofevidenceshowsthatthisseawater-derivedorganicmattercanbetransferredintheatmospherewhereitcanalsoundergophotochemicalandbacterialdegradation(aging)leadingtophysicochemicalmodificationoforganiccompounds.Importanteffectofseawater-derivedorganicmatteronatmosphericsolarradiationtransferandcloudprocesseshasbeenwelldocumented.Yet,duetothecomplexmixtureofoceanicandcontinentalprecursors,veryfewstudieshaveattemptedtocharacterizeagingofmarineorganics.Throughimplementationofmarineorganicaerosoltracersinglobalchemistry-transportmodelweareabletoidentifytheregionswithlargecontributionsoffreshly-emittedoragedaerosol,potentiallocationsforfuturefieldstudiesfocusedonimprovedcharacterizationofmarineorganicaerosols(seeFigure5.17).AdditionaldetailswerepublishedinGanttetal.(2014).

6 ACE and the 2017 Decadal Survey ThescienceandtechnologyadvancementsproducedbytheACEstudy,togetherwiththeexpertisegainedbythescientistsandengineersinvolvedintheACEstudy,providestoalargedegreethebasisforupcomingworkaddressingthe2017DecadalSurvey’srecommendationsasspecifiedbelow.

TheDecadalSurveyforEarthscienceandapplicationsfromspace(referredtoas‘DecadalSurvey’)provideslongtermguidanceforNASAEarthScienceDivision.MembersofUSEarthSciencecommunitydefineNASA’sEarthScienceprioritiesforthecoming10years,recommendobservations,andfundsneededtoaddressthesciencequestions.Thetitleofthe2017report(PDF:http://nap.edu/24938DOI:10.17226/24938)is“ThrivingonOurChangingPlanet:ADecadalStrategyforEarthObservationfromSpace”.Inordertoperformambitiousscience,despiteconstraints,itcallsinastrategicframeworkto:

• Embraceinnovativemethodologiesforintegratedscience/applications;• Committosustainedscienceandapplications;• Amplifythecross-benefitofscienceandapplications;and• Leverageexternalresourcesandpartnerships(incl.international).

The observable approach of the Decadal Survey is as follows: • ProgramofRecordtobecompleted• DesignatedObservables(ObservingSystem)

o Mostimportantscience/Largemissionso Instrumentsarecompetedorcontributed(incl.international)o Scienceteamandcalibration/validationprogramarecompetedo Costcap:$300to$800M(fullmissioncosts)

• EarthExplorero Veryimportantscience/Mediummissionso Costcap:<$350M(fullmissioncosts)

• Incubationtomaturetechnology• EarthVentureContinuity(additiontoSuborbital,Instrument,andMissionstrand)

o Todemonstratesustainedobservationsatlowercostso Costcap:<$150Mo

Moreinformationonthe2017DecadalSurveyandNASA’sresponsetoitcanbefoundhere:https://science.nasa.gov/earth-science/decadal-surveys

Table 8.1. Designated Observables Summary from the 2017 Decadal Survey.

6.1 Aerosols Observable DuringtheACEmeetinginMay2018,theACEaerosolSTMwasreviewedinlightofthe2017DecadalSurveyrecommendationforaDesignatedAerosolMission.ManyoftheDecadalSurveyaerosol-relatedscienceobjectiveswereverysimilartotheACEaerosol-relatedobjectivesdiscussedinsection2.1.TheobjectivesassociatedwiththeDecadalSurveyClimateVariabilityandChangepanelinparticularwereverysimilartotheACEaerosolobjectives.Forexample,theDecadalSurveyrecommendationtoreducetheIPCCAR5totalaerosolradiativeforcinguncertaintybyafactoroftwoissimilartotheACEDARF/DAREgoals.TheDecadalSurveyrecommendationtoimproveestimatesoftheemissionsofnaturalandanthropogenicaerosolsandtheirprecursorsisverysimilartotheACEsources,processes,transport,andsinks(SPTS)goals.TheDecadalSurveyrecommendationtoquantifytheeffectthataerosolhasoncloudformation,cloudheight,andcloudproperties,includingsemi-directeffects,isverysimilartotheACEgoalsassociatedwithaerosol-cloudinteractions.TheDecadalSurveyaerosolobjectivesincludeanincreasedemphasisonairqualityrelatedobjectives;inparticularonobservingPMconcentrationandspeciation.Consequently,movingforward,theaerosolSTMshouldexpandtoincludetheairqualityrelatedquestions/objectives.

InlightoftheseDecadalSurveyrecommendations,theACEaerosolcommunitydevelopedthefollowingsetofrecommendations:

Science Objectives and Science Traceability Matrix

• EstablishaprocessforprioritizingDecadalSurveyscienceobjectives,correspondingmeasurementrequirements,andinstrumentcapabilitiesthat

capitalizeonACEprogressandadvancesandfitwithintheDecadalSurveycostcap

• Developthresholdandbaselineaerosolmission,whichincludesprioritizedaerosolscienceobjectives

• SciencequestionQ4intheACESTMshouldbemovedtotheaerosol-cloudinteractionssubsection(i.e.cloudresponsetotheaerosolradiativeheatingshouldbepartofaerosol-cloudinteractions).

• ThescienceassociatedwiththeAerosols-OceanSTMshouldmoveforwardasmuchaspossible.

Observing System

• ObservingSystemdesignstudiesshouldincluderepresentativesofmultidisciplinarystakeholdercommunities,includingapplications,thathaveexpertiseandknowledgeofsatellitemeasurementsandcapabilities(e.g.airquality,oceans,etc.)

• ObservingSystemdesignsshouldperformorbitandmeasurementtradestudiesthataddresslidarandpolarimeteroverlapandhowthisaffectsinstrumentdesign,capabilities,technicalresourcesandcosts.

• Differentcoveragesandimplementations(singleplatform,multiplatform)shouldbeconsidered,inthecontextoftheexistingProgramofRecord,whenaddressingthesciencequestions

• Theimportanceofdatalatencyandavailabilityshouldbeconsideredastheyrelatetothesciencequestionsandobjectives

• ObservingSystemdesignsneedtoaccountforsciencerequirements,instrumentcapabilities,andcosts

• Complementarysuborbital/groundmeasurementstoaddresssciencequestionsandobjectivesunattainablefromspaceshouldbeconsidered.Theseincludesystematicand/ortargetedairbornemeasurements,groundnetworks,etc.

• IdentifyLEOandGEOsatellitesandaerosoltransportmodelsthatwillbeoperationalinthefutureandmaptheircapabilitiesandcontributionstoDecadalSurveyobjectives

• Theroleofmodelstofillinobservationalgapsmustbeconsidered

Algorithm Development

• Algorithmdevelopmentneedstoaccountfordifferentcombinationsoflidarandpolarimetercapabilitiestoaddressthescienceobjectives

• Algorithmsshouldexaminethemeasurementsthatwillbeusedtoconstrainandevaluatemodels

6.2 Aerosol-Ocean Ecosystems Synergisms TheACEAerosol-OceanscienceobjectivesarehighlysynergistictoACEAerosol,Cloud,andOceanEcosystemsElementsandaddressmutuallyinteractingprocesses–atmospheretooceaninteractions,marinebiologytoatmosphereinteractions,aeolianfertilizationoftheoceans,emissionofprimarymarineaerosol,andreleasetotheatmosphereofhighlyreactivetracegasesbythemarineecosystem–allofwhichhavekeyeffectsonthemarineboundarylayercloudcondensationnucleibudgetandmicrophysicalpropertiesofmaritimeclouds.Thedetailedmechanismsforaerosol-oceaninteractionprocessesandtheirradiativefeedbacksintheEarthclimatesystemarebestunderstoodthroughthecombinationofinsitudata,satelliteremotesensing,andmodels.Duetoitsimportanceforimprovedclimatechangeassessmentsandhighlyinterdisciplinarynature,theaerosol-oceaninteractionremainsanareaofincreasinginteresttothescientificcommunity(e.g.,Carslawetal.,2013;McCoyetal.,2015;DeMottetal.,2016;Popeetal.,2017;Dani&Loreto,2017;Meskhidzeetal.,2017;Hostetleretal.,2018).Severaltopicsofinterestaresummarizedbelow:

Cloudsinremotehigh-latitudeoceans(i.e.,intheArctic,theSouthernOcean,andtheAntarcticmarginalseas)playasignificantroleinregulatingclimate.Yetmanyexistingdatasourceshaveweaknessesthatrestricttheirusability,particularlyathighlatitudes.

Ocean-derivedprimaryaerosolandprecursorgasesleadingtoaerosolproductionarebelievedtobeasignificantsourceofCloudCondensationNuclei(CCN)andIceNucleatingParticles(INP)intheremotemarineboundarylayersandquantifyingtheirimpactwillbenecessaryforresolvingrelativecontributionsofnaturalandanthropogenicaerosolradiativeeffectsonclimate

Currentanalysesestablishcorrelationsbetweenoceanecosystemstateandcloudproperties.However,quantitativeknowledgeofaerosol-oceaninteractionisrequiredforestablishingprocesslinkagesanddevelopmentofphysically-basedparameterizationsformodels.TheseprocesslinkagesmustbebuiltintocoupledEarthSystemModels(ESMs)topredicttheimpactofoceanecosystemchangeoncloudsandradiationastheecosystemrespondstowarmingoceans.

Thisistrulyinterdisciplinarysciencetopicthatrequiresexpertiseinatmosphericchemistry,organicgeochemistry,photochemistry,optics,chemicaloceanography,dust/aerosolgeochemistry,andvariousaspectsofatmospheric,ocean,andEarthsystemstudies

6.3 Other Cross-cutting aspects Thedesignatedobservables,assuggestedbythe2017DecadalSurvey,onAerosolsandonCloud,Convection,Precipitation(A-CCP)providesubstantialopportunities

forsynergieswithotherdesignatedobservablesandmanyotheraspectsofNASA’sEarthScienceDivisionportfolio.

Inparticular,theSurfaceBiologyandGeology(SBG)designatedobservablecouldstronglybenefitfrompreciseaerosolinformation(e.g.spectralAODandaerosoltype)toreduceuncertaintiesintheatmosphericcorrectionprocess.Inreturn,A-CCPmaybenefitfromadditionalspectralinformationcontentforradiativetransfercalculationstobeusedwithpolarimeterdata.Inreturn,datafromSBG,forexample,mightbehelpfultobetterconstrainthechemicalcompositionofatmosphericconstituentsandthespectralsurfacealbedo.AnotherexampleofsynergiestotheMassChange(MC)designatedobservableisgiventhroughtheconnectionofprecipitation(waterandsnow)withgroundwaterstorageandicesheetmasschanges.Andsimilarly,theSurfaceDeformationandChange(SDC)designatedobservablecanbelinkedtoA-CCPbyvolcanicplumecharacterizations(3Dshapeandcomposition)throughmultiangleandlidaraerosolobservations.Thosearejustafewexamples.ThoseandmoreareasofsynergieswillbelookedatduringtheA-CCPstudyleadinguptotheA-CCPMissionConceptReviewinearly2022.

A-CCPmayalsoprovidebenefitsandcrucialobservationsforscienceareasnotcoveredbythedesignatedobservable.Forexample,ithasbeenshownthataspaceborneatmosphericlidarcanprovideusefuldataforoceanecosystemsresearch(seeabove).FurthersynergiesarelikelyfoundwhereoptimizedA-CCPobservationscanprovidedataforscienceaddressedbytheexplorerandincubatorprograms,suchassnowdepthandecosystemstructure.

Realizationofsomeofthesynergieswilldependontheobservingsystemimplementationandoperation.Thedesignatedobservablestudies,whichstartedinearlyFY19,areencouragedtoidentifyandaddressthosesynergiesandsuggestimplementableapproaches.Someofthoseapproachedmayincludeairborne,smallsatellites,orothernon-traditionalcomponents,includingthecurrentprogramofrecord.Modelswilllikelyplayafundamentalroleinconnectingdifferentobservableswithdifferentscienceandapplicationobjectives.

7 Programmatic Assessment and Recommendations InthissectionwepresenttheprogrammaticassessmentandrecommendationsforimprovementoftheprocessofdevelopmentofDecadalSurveySatelliteMissions.The2007DecadalSurveyrecommendedaseriesofsatellitemissionswithsupportingsciencequestionsandsciencetraceabilitymatrixesaswellasrecommendationsforsensorpayloadsandmissionarchitectures.Someofthesepreliminarymissionconcepts,ACEincluded,wereassignedtoscienceworkinggroupstodevelopandrefinetheDecadalSurveyrecommendationssotherecommendedmissionscouldbetransitionedfrompre-formulationtoformulation.

Littleguidancewasprovidedastowhatwasrequiredofthescienceworkinggroupsandwhenitwasdue.Intothatvacuum,afalsesenseofurgencypervadedthescienceworkinggroups,whichleadtotheperceptionthatthesooneracompleteplanwassubmitted,thesoonerthatmissionwouldtransitiontoformulation.

Thisfalsesenseofurgencyleadtoanumberofundesirableoutcomes.First,manyaspectsoftheproposedmissionswereaddressedinaparallelstove-pipedfashion.Asaresult,therefinementofthesciencequestionsanddevelopmentofsciencetraceabilitymatrixesweremoreseparatethantheyshouldhavebeenfromdevelopmentofinstrumentconceptsandmissionarchitecture.Forexample,changesinthesciencetraceabilitymatrixesdidnotpropagateasquicklyandcompletelythroughtherestoftheACEstudyaswouldhavebeenoptimal;theprocesscostmorethanitshouldhave.Second,worthwhilecrossmissionfertilizationessentiallydidnottakeplace.Further,neithertheaugmentationofexistingsatelliteconstellationsnorthedevelopmentofnextgenerationsatelliteconstellationswasseriouslyconsidered.Third,therushtobecomingformulationreadylimitedworkingwiththeEarthScienceTechnologyOffice(ESTO)todevelopnewtechnologies.ThisisnottoimplyESTOdidnotworkwiththeDecadalSurveymissionscienceworkinggroups.Quitetheoppositeistrue.However,theinteractionsweremostlywiththosewhodevelopedsensorconcepts.Thus,thecrossmission,inter-sensorperspectivewaslargelymissing.

Aremedyfortheseissuesisfairlystraightforward.Headquartersshouldprovideguidanceastoataskdescriptionduedateforoutputfromthescienceworkinggroup.Financialguidancewouldalsobehelpful.Theleadershipofthescienceworkinggroupsshouldbeencouragedtocarryoutthemissionstudiesinamoreserialmanner.Sciencequestionsandsciencetraceabilitymatrixesshouldbedevelopedfirst.Asthesciencetraceabilitymatrixesbecomefairlymature,appropriateinstrumentconceptstudiesshouldbetransitionedfromalowerlevelpreliminarystatetoalargerfocusedeffort.Headquartersshouldestablishastudygroupwhosetaskistostudycrossmissionfertilizationandaugmentationofexistingsatelliteconstellationsorthedevelopmentofnewsatelliteconstellations.Lastly,plansformissionarchitectureshouldbedevelopedbasedontherecommendationsofthescienceworkinggroupandrecommendationsfromtheHeadquartersinstitutedcrossmission/constellationstudygroup.

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List of Acronyms ACATS–AirborneCloudAerosolTransportSystem

A-CCP-AerosolsandCloud,Convection,PrecipitationDesignatedObservableaCDOM–absorptionbyColored/CHROMOPHORICDissolvedOrganicMatter

ACE–AerosolCloudEcosystemsmission

ACERAD–AtmosphericProfilingRadarforACEACHIEVE-Aerosol,Cloud,Humidity,InteractionsExploringandValidating

Enterprise

ACI–Aerosol-CloudInteractionsACR-AirborneCloudRadar/CloudSatValidationRadar

ACT-AdvancedComponentTechnologiesProgramAERONET–AerosolRoboticNetwork

AESLA–ActiveElectronicallyScanningLinearArraysAFRC–NASA’sArmstrongFlightResearchCenter(formerlyDrydenResearchFlight

Center)

AirMISR–AirborneMulti-angleImagingSpectroRadiometerAirMSPI-AirborneMultiangleSpectroPolarimetricImager

AITT-AirborneInstrumentTechnologyTransition

AMPR–AdvancedMicrowavePrecipitationRadiometerAMS-AutonomousModularSensor

AMSR-E-AdvancedMicrowaveScanningRadio

AMT–AtlanticMeridionalTransectprogramoftheUnitedKingdoAOD–AerosolOpticalDepth

APR-2-AirborneSecondGenerationPrecipitationRadarAPS–AerosolPolarimetrySensor

ASIC–ApplicationSpecificIntegratedCircuit

ASTER-AdvancedSpaceborneThermalEmissionandReflectionRadiometerA-Train–The“AfternoonConstellation”includingtheOCO-2,GCOM-W1,Aqua,

CALIPSO,CloudSat,PARASOL,andAurasatellites.

AVIRIS-AirborneVisible/InfraredImagingSpectrometerbbp-oceanparticulatebackscattercoefficientsBRF–BidirectionalReflectanceFactorsCALIPSO-TheCloud-AerosolLidarandInfraredPathfinderSatelliteObservation(C

CALIOP-Cloud-AerosolLidarwithOrthogonalPolarization

CAMP2Ex–Cloud-Aerosol-MonsoonPhilippinesExperimentCARES-CarbonaceousAerosolsandRadiativeEffectsStudy

CATS–CloudAerosolTransportSystemCCN–CloudCondensationNuclei

CDOM–ColoredDissolvedOrganicMatter

[Chl-a]-chlorophyll-aconcentrationCloudSat–theNASAsatellite-basedcloudexperimentmission

CO2–CarbonDioxide

C-OPS-Compact-OpticalProfilingSystemCOSSIR-CompactScanningSub-millimeter-waveImagingRadiometer

CoSMIR-ConicalScanningMillimeter-waveImagingRadiometerCOTS–CommercialOrbitalTransportationServices

COVE-2-CubeSatOn-boardprocessingValidationExperiment-2

CPL–CloudPhysicsLidarCPR–CloudProfilingRadar

CRM–CommonResearchModel

C-PrOPS-Compact-PropulsionOptionforProfilingSystemsCRS–CloudRadarSystem(at94GHz)CubeSat-atypeofminiaturizedsatelliteforspaceresearchthatismadeupof

multiplesof10×10×11.35cmcubicunits

DAOF-DrydenAircraftOperationsFacility

DARF–DirectAerosolRadiativeForcingDISCOVER-AQ-DerivingInformationonSurfaceConditionsfromCOlumnand

VERtically.ResolvedObservationsRelevanttoAirQualityDOLP–DegreeofLinearPolarization

DRAGON-DistributedRegionalAerosolGriddedObservationNetwork

DS–DecadalSurveyEC-EarthCARE-EarthClouds,AerosolsandRadiationExplorer

eMAS–enhancedMODISAirborneSimulator

EMC–ElectroMagneticCompatabilityEMI–ElectroMagneticInterference

ER-2–NASA/CivilianversionoftheAirForce'sU2-Sreconnaisanceplatform

ERF–EffectiveRadiativeForcing

ESPO–NASA’sEarthScienceProjectOfficeESTO–NASA’sEarthScienceTechnologyOffice

EV-I–EarthVentureInstrumentEV-M–EarthVentureMission

EV-S–EarthVentureSuborbitalwithEV-S1beingthefirstroundofEV-SfundingtheEV-S2beingtherecentlycompetedandawarded(FY-15)secondopportunityofEV-Sfunding.

EXRAD–ER-2X-BandRadar

FLH–SatelliteChlorophyllFluorescenceFPGA–FieldProgrammableGateArray

GCM–GeneralCirculationModelGH–NASAGlobalHawkUnmannedAirbornePlatform

GIOP–GeneralizedInherentOpticalProperties

GIOP-DC–GIOPdefaultconfigurationGISS–NASAGoddardInstituteforSpaceStudies

GOCECP–GlobalOceanCarbonEcosystemsandCoastalProcessesmission

GOCI–GeostationaryOceanColorImagerGEOS-5–GoddardEarthObservingSystemModelVersion5

GPM–GlobalPrecipitationMeasurementGPM-GV–GlobalPrecipitationMeasurementGroundValidationprogram

GPU–GraphicalProcessingUnit

GRASP-GeneralizedRetrievalofAerosolandSurfacePropertiesGroundMSPI–portable,ground-basedMultiangleSpectroPolarimetricImager

GSFC-NASAGoddardSpaceFlightCenterGSM-Garver-Siegel-MaritorenaAlgorithm

HARP–HyperAngularRainbowPolarimeter

HIWRAP–High-AltitudeImagingWindandRainAirborneProfilerHSRL–HighSpectralResolutionLidar

HSRL-1–Firstgeneration

HSRL-2–SecondgenerationHyspIRI-HyperspectralInfraredImager

ICDH-InstrumentCommandDataHandling

IDL–InstrumentDesignLaboratory

IFOV–instantaneousfieldofviewIIP–ESTOInstrumentIncubatorProgram

IMDL–IntegratedMissionDesignLaboratoryIOP–InherentOpticalProperties

IPCC–IntergovernmentalPanelonClimateChange

IPHEx–IntegratedPrecipitation&HydrologyExperimentIRAD–InternalResearchandDevelopment

ISS–InternationalSpaceStation

ITCZ–Inter-TropicalConvergenceZoneJPL–NASA’sJetPropulsionLaboratoryKa-Band–segmentofthemicrowaveregionoftheelectromagneticspectrum26.5-

Ku-Band-segmentofthemicrowaveregionoftheelectromagneticspectrum12-18GHz

LaRC–NASALangleyResearchCenter

LDCM–LandsatDataContinuityMission

LED–LightEmittingDiodeLEO–LowEarthOrbit

LES–LargeEddySimulationsLISST-SubmersibleSuspendedSedimentSensor/laserparticlesizeanalyzer

MAS–MODISAirborneSimulator

MASTER-MODIS/ASTERAirborneSimulatorMCAD–MarkovChainAdding-Doubling

MCMC–MarkovChainMonteCarloMISR–Multi-angleImagingSpectroRadiometer

ML–MixedLayer

MODIS–Moderate-ResolutionImagingSpectroradiometerMPC–MissionPeculiarCost

mrad-milliradian

MSPI-MultiangleSpectroPolarimetricImagerNAS–NationalAcademyofScience

NEXRAD-Next-GenerationRadar

NIR–NearInfraredportionofelectromagneticspectrumwithwavelengthsof0.8-2.5μ

NPEO–NASAPlanforEarthObservationsNPPVIIRS–NationalPolar-orbitingPartnershipVisibleInfraredImaging

RadiometerSuite

NRC–NationalResearchCouncil

OBB–NASA’sOceanBiologyandBiochemistryProgramOCEaNS–OceanCarbonEcosystemandNear-Shoremission

OE–OptimalEstimationOLYMPEX-OlympicMountainsGroundValidationExperimentsupportedbythe

GPMgroundvalidation(GV)program

OMI–OzoneMonitoringInstrumentORCA–OceanRadiometerforCarbonAssessment

OSPREy-OpticalSensorsforPlanetaryRadianceEnergy

OSSE–ObservationalSystemSimulationExperimentO2A-Band–oxygenabsorptionbandintheelectromagneticspectrumnear0.76μ

PACE–Plankton,Aerosol,CloudandOceanECOSystemmission;formerlyPre–AerosolCloudEcosystemmission

PACS–PassiveAerosolandCloudSuitemultiangleimagingpolarimeter

PDF–ProbabilityDistributionFunctionPEMs-photoelasticmodulators

PhyLM–PhysiologyLidarMultispectralMission

PI-Neph-PolarizedImagingNephelometerPODEX–PolarimeterDefinitionExperiment

POLDER-POLarizationandDirectionalityoftheEarth'sReflectancesPSD–ParticleSizeDistribution

PSG–PolarizationStateGenerator

PWG–PolarimeterWorkingGroupQAA–QuasiAnalyticalAlgorithm

QRS–QuickResponseSystem

QWPs–Quarter-waveplatesRADEX–RadarDefinitionExperiment

RFT–RainbowFourierTransform

ROIC-ReadOutIntegratedCircuit

RPI–RensselaerPolytechnicInstituteRSP–ResearchScanningPolarimeter

RT–RadiationTransferSAA–Semi-AnalyticalAlgorithm

SABOR-Ship-AircraftBio-OpticalResearchFieldCampaign

SBIR–SmallBusinessInnovationResearchprogramSCA–SensorChipAssembly

SCIPP–SuperCompositeImageProcessingPipeline

SCPR–SinglyCurvedParabolicReflectorSDT–ScienceDefinitionTeamSEAC4RS-StudiesofEmissionsandAtmosphericComposition,CloudsandClimate

CouplingbyRegionalSurveys

SEL–SingleEventLatchup

SEWG–SystemsEngineeringWorkingGroupSIDECAR-SystemforImageDigitization,Enhancement,ControlAndRetrieval

SNR–SignaltoNoiseRatio

SODA-SynergizedOpticalDepthofAerosolsSOS–SuccessiveOrderofScattering

SPTS–Sources,Processes,TransportsandSinksSSH–SeasSurfaceHeight

SST–SeaSurfaceTemperature

STM–ScienceTraceabilityMatrixSTTR–SmallBusinessTechnologyTransferprogramSWIR–Short-WavelengthInfraredportionofelectromagneticspectrumwith

wavelengthsof1.4-3μ

Tb–BrightnessTemperature

TCAP–TwoColumnAerosolProjectfundedbytheDOETC4–NASA’sTropicalComposition,CloudandClimateCouplingmission

TIRS–ThermalInfraredSensor

TOA–TopofAtmosphereTRL–TechnicalReadinessLevel

X-Band-segmentofthemicrowaveregionoftheelectromagneticspectrum8.0-12.0GHz

UND–UniversityofNorthDakota

UV–Ultra-violetUVDIAL–Ultra-VioletDifferentialAbsorptionLidar

U10–OceanSurfaceWindspeed

VIS–Visibleportionoftheelectromagneticspectrumwithwavelengthsof0.4-0.7μVNIR–VisibleandNearInfraredportionofelectromagneticspectrumwith

wavelengthsof0.4-1.4μW-Band-segmentofthemicrowaveregionoftheelectromagneticspectrum75–

110GHz

WiSCR-Wide-SwathSharedApertureCloudRadar3CPR–ThreeBandCloudandPrecipitationRadar

3β+2α+2δ–Backscatterin3Channels(1064,532and355nm),Extinctionin2Channels(532and355nm)andDepolarizationin2Channels(532and355nm)

4STAR-SpectrometersforSky-Scanning,Sun-TrackingAtmosphericResearch

Aerosol, Cloud, Ecosystems (ACE) Final Study Report Silva1247.pdf2015/02/12 · 1 Aerosol, Cloud,...

Documents

Aerosol, Cloud and ocean Ecosystem Mission (ACE)...Jun 09, 2014 · Specific Tasks: Complete the Science Traceability Matrices Complete the UV laser lifetime study, and publish the

Organic Carbon Aerosol in the Free Troposphere: Insights from ACE-Asia and ICARTT

Ambient aerosol sampling using the Aerodyne Aerosol Mass ...cires.colorado.edu/jimenez/Papers/2001JD001213_published.pdf · Ambient aerosol sampling using the Aerodyne Aerosol Mass

Deep Blue Retrievals of Asian Aerosol Properties During ...€¦ · Deep Blue Retrievals of Asian Aerosol Properties During ACE-Asia N. Christina Hsu, Si-Chee Tsay, Michael D. King,

Aerosol-Cloud-Ecosystems (ACE) Technology Investments · Aerosol-Cloud-Ecosystems (ACE) Technology Investments. 2 Mission and Payload The primary goal of the Aerosol-Cloud-Ecosystems

The Aerosol Cloud Ecosystem Mission was recommended by the 2007 NRC decadal survey. ACE is presently in pre-formulation and will be realized in some form

ATI Aerosol Photometer & Aerosol Generator Basics

Estimating aerosol emissions by assimilating aerosol ...icap.atmos.und.edu/AERP/MeetingPDFs/SourceSink/Huneeus_Inverse... · Estimating aerosol emissions by assimilating aerosol optical

Studies of Aerosol - Ocean Ecosystems - Cloud Interactions ...€¦ · ecosystems already stressed by a wide range of other human activities. As humans significantly perturb the nitrogen

Regional aerosol properties: Comparisons of boundary layer ...Regional aerosol properties: Comparisons of boundary layer measurements from ACE 1, ACE 2, Aerosols99, INDOEX, ACE Asia,

ACE CRC climate futures for tasmania - Department of ...€¦ · ACE CRC ANTARCTIC CLIMATE & ECOSYSTEMS CRC climate futures for tasmania ... Lake in the Macquarie River catchment

2i Digital Aerosol Photometer 2i Digital Aerosol Photometer

ACE CARGO ADVANTAGE LIAINSURANCE POLICY · PDF fileMARINE . ACE Cargo Advantage Insurance Policy. PT ACE Jaya Proteksi © 2016 ACE Group. ACE®, ACE logo® and ACE insured® are trademarks

An emerging ground-based aerosol climatology: Aerosol Optical

Aerosol Optical Thickness (AOT), Aerosol Particle Size

“ Feedbacks between the terrestrial biosphere and the atmosphere through biogenic aerosol pathways (?)” The coupling between land ecosystems and the atmospheric

Spectral (380 to 1558 nm) aerosol optical depth (AOD) … · Web viewClear-sky closure studies of lower tropospheric aerosol and water vapor during ACE 2 using airborne sunphotometer,

Aerosol Manufacturing System - Southern Aerosol Point/Powerpoint pdfs/Aerosol_Syste… · Aerosol Manufacturing System ... no aerosol plant which was equipped with the required safety

Plankton, Aerosol, Cloud, and ocean Ecosystems (PACE) Missionweb.whoi.edu/ocb-workshop/wp-content/uploads/sites/... · Multiangular capabilities enhance the ability to estimate many

ECOSYSTEMS & BIOMES ECOSYSTEMS SUPPORT LIFE MATTER CYCLES THROUGH ECOSYSTEMS ENERGY FLOWS THROUGH ECOSYSTEMS BIOMES CONTAIN MANY ECOSYSTEMS