73rd Eastern Snow Conference Scientific Program & Abstracts

73rdEasternSnowConferenceScientificProgram&Abstracts

❄ Airborneandspaceborneremotesensingofsnowandice ❄

HighbanksMetroPark,Columbus,Ohio,February2015.

❄June14-16,2016,attheByrdPolar&ClimateResearchCenterandtheWexnerCenterfortheArts,TheOhioStateUniversity ❄

73rdEasternSnowConference

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TheEasternSnowConferenceTheEasternSnowConference(ESC)isajointCanadian/U.S.organization.TheEasternsnowconferenceisdescribedinthefirstpublishedEasternSnowConferenceProceedingsasarelativelysmallorganizationoperatingquietlysinceitsfoundingin1940byasmallgroupofindividualsoriginallyfromeasternNorthAmerica.Theconferencemeteighttimesbetween1940and1951.ThefirstEasternSnowConferenceProceedingscontainedpapersfromits9thAnnualMeetingheldFebruary14and15,1952,inSpringfield,Massachusetts.Today,itsmembershipisdrawnfromEurope,Japan,theMiddleEast,aswellasNorthAmerica.Ourcurrentmembershipincludesscientists,engineers,snowsurveyors,technicians,professors,studentsandprofessionalsinvolvedinoperationsandmaintenance.ThewesterncounterparttothisorganizationistheWesternSnowConference(WSC),alsoajointCanadian/USorganization.Everyfifthyearorso,theESCandWSCholdjointmeetings.Atitsannualmeeting,theEasternSnowConferencebringstheresearchandoperationscommunitiestogethertodiscussrecentworkonscientific,engineeringandoperationalissuesrelatedtosnowandice.ThelocationoftheconferencealternatesyearlybetweentheUnitedStatesandCanada,andattendeespresenttheirworkbygivingtalksorpresentingposters.AuthorssubmittheirmanuscriptsforpublicationinouryearlyProceedingsoftheEasternSnowConference.VolumesoftheEasternSnowProceedingscanbefoundinlibrariesthroughoutNorthAmericaandEurope;paperscanalsobefoundthroughtheNationalTechnicalInformationService(NTIS)intheUnitedStatesandCISTIinCanadaandissuessince2000areavailableontheconferenceswebsiteatwww.easternsnow.org.Inrecentyears,theESCmeetingshaveincludedpresentationsonsnowphysics,managementandhydrology,snowandiceloadsonstructures,riverice,wintersurvivalofanimals,remotesensingofsnowandice,glacierprocesses,snowscienceasateachingtoolandsocio-politicalimpactsofwinter.

CorporateMembersandSponsorsTheESCcouldnotoperatewithoutthesupportofitscorporatemembershipovertheyearsand2016sponsor.ThisyeartheESCwouldliketothankGeonor(www.geonor.com),andCampbellScientificCanada(https://www.campbellsci.ca).ThankstotheByrdPolar&ClimateResearchCenterfortheirsupportofthe73rdmeeting!


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TheESCencouragesstudentresearchthroughitsWiesnetMedal.Thismedalispresentedannuallytothebeststudent

paperpresentedattheconference.CampbellScientificCanadaalsograciouslyawardsacashprizetothestudentresearch

showingthemostinnovativeuseoftechnologyinthegatheringofdata.Finally,theDavidMillerAwardisawardedto

thebeststudentposterattheannualConference.

Year Winner Affiliation2015 NicolasLeroux UniversityofSaskatchewan2014201320122011

JustinHartnettAndreasDietz

ElizabethBurakowskiKathrynSemmens

SyracruseUniversity,Syracruse,NYEarthObservationCenter/DFD,Germany

UniversityofNewHampshire,NHLehighUniversity

2010 SimonvondeWall UniversityofVictoria,BC2009 SiChen DartmouthCollege2008 ChrisFurhman UniversityofNorthCarolinaatChapelHill,NC2007 notawarded 2006 Y.C.Chung UniversityofMichigan2005 M.Javan-Mashmool UniversitéduQuébecàChicoutimi,ChicoutimiQC2004 J.Farzaneh-Dehkordi UniversitéduQuébecàChicoutimi,ChicoutimiQC2003 AlexandreLanglois UniversitédeSherbrooke,SherbrookeQC2002 PatrickMénard UniversitédeLaval,SteFoy,QC2001 C.Tavakoli UniversitéduQuébecàChicoutimi,ChicoutimiQC2000 notawarded 1999 S.Brettschneider UniversitéduQuébecàChicoutimi,ChicoutimiQC1998 AndrewGrundstein UniversityofDelaware,Newark,DE1997 NewellHedstrom UniversityofSaskatchewan,SaskatoonSK1996 SuzanneHartley UniversityofDenver,DenverCO1995 PaulWolfe WilfredLaurierUniversity,WaterlooON1994 G.E.Mann UniversityofMichigan,AnnArborMI1993 G.Devarennes UniversitédeQuébecàQuébec,QC1992 D.W.Cline UniversityofColorado,BoulderCO1991 D.Samelson CornellUniversity,IthacaNY1990 A.K.Abdel-Zaher UniversityofNewBrunswick,FrederictonNB1989 A.Giguere McGillUniversity,MontréalQC1988 MauriPelto UniversityofMaine,OronoME1987 CameronWake WilfredLaurierUniversity,WaterlooON1986 CraigAllan TrentUniversity,PeterboroughON1985 RobertSpeck RensselaerPolytechnicInstitute,TroyNY1984 N.K.Kalliomaki LaurentianUniversity,Sudbury,ON1983 DavidBeresford TrentUniversity,PeterboroughON


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1982 notawarded 1981 JeffreyPatch UniversityofNewBrunswick,FrederictonNB1980 BryanWolfe TrentUniversity,PeterboroughON1979 MargaretLeech McGillUniversity,MontréalQC1978 MichaelEnglish TrentUniversity,PeterboroughON1977 DonMcLaughlin&

GeorgeDugganRensselaerPolytechnicInstitute,TroyNY

1976 DwayneMcMurter TrentUniversity,PeterboroughON1975 NigelAllan SyracuseUniversity,SyracuseNY1974 notawarded 1973 StanMathewson TrentUniversity,PeterboroughON


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LifeMembersTheEasternSnowconferencegratefullyrecognizesindividualswhohavemadelifelongcontributionstothestudyofsnowandfortheirsupportofthisorganization.Ourcurrentlifemembersare:

PeterAdams

ArtEschner

BarryGoodison

GerryJones

JohnMetcalfe

HildaSnelling

DonaldWiesnet


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TheEasternSnowConferenceannuallybestowsuponadistinguishedsnowscientistwho,instrivingforexcellenceinsnowresearch,contributestoaneventofnotablehumorthe

highlycovetedSno-fooAward.

Year Winner Affiliation2015 KevinCoté UniversitédeSherbrooke,Sherbrooke,QC2014201320122011

DorothyHallBenoitMontpetitDonPiersonKenRancourt

NASA-Goddard,MDUniversitédeSherbrooke,Sherbrooke,QCNYCDEP,NYMountWashingtonObservatory,NorthConway,NH

2010 Kyung-Kuk(Kevin)Kang UniversityofWaterloo,Waterloo,ON2009 RobHellström BridgewaterStateUniversity,Bridgewater,MA2008 StevenFassnacht ColoradoStateUniversity,FortCollins,CO2007 thegroupof9* U.Saskatchewan,UBC,AlbertaEnvironment,U.Calgary2006 AndrewKlein TexasA&MUniversity,CollegeStation,TX2005 ClaudeDuguay UniversityofAlaska-Fairbanks,Fairbanks,AK2004 ChrisDerksen MeteorologicalServiceofCanada,Toronto,ON2003 MilesEcclestone TrentUniversity,PeterboroughON2002 DannyMarks U.S.D.A.,BoiseID2001 BrendaToth UniversityofSaskatchewan,SaskatoonSK2000 MauriPelto NicholsCollege,DudleyMA1999 RossBrown MeteorologicalServiceofCanada,Montréal,PQ1998 MaryAlbert CRREL,Hanover,NH1997 JeanStein UniversitédeLaval,SteFoy,QC1996 ColinTaylor TrentUniversity,PeterboroughON1995 MikeDemuth N.H.R.I.,SaskatoonSK1994 BertDavis CRREL,Hanover,NH1993 JohnPomeroy N.H.R.I.,SaskatoonSK1992 TomNiziol N.W.S.,Buffalo,NY1991 TerryProwse N.H.R.I.,SaskatoonSK1990 KersiDavar UniversityofNewBrunswick,Fredericton,NB1989 GerryJones INRS-EAU,SaintFoy,QC1988 RobertSykes SUNY,SyracuseNY1987 JohnMetcalfe MeteorologicalServiceofCanada,Toronto,ON1986 PeterAdams TrentUniversity,PeterboroughON1985 DonWiesnet NationalWeatherService,Minneapolis,MN1984 BarryGoodison MeteorologicalServiceofCanada,Toronto,ON

*JimmyMacDonald(U.Sask.),BillFloyd(UBC),ChrisDeBeer(U.Sask.),WendellKoenig(ABEnv.),JaimeHood(U.Calgary),DankiaMuir(U.Calgary),JohnJackson(U.Calgary),SarahForte(U.Calgary),Prof.MasakiHayashi(U.Calgary)


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73rdESCExecutiveCommittee2015-2016PastPresident: BakerPerry,ElksPark,NCPresident: AlainRoyer,Sherbrooke,QCVicePresidentandProgramChair: MichaelDurand,Columbus,OhioTreasurerand1stSecretary,CA: MilesEcclestone,Peterborough,ON2ndSecretary,CA: AlexanderLanglois,Sherbrooke,QC1stSecretary,US: KennethRancourt,Conway,NH2ndSecretary,US: DerrillCowing,Monmouth,MEEditor,ESCProceedings: AlexanderLanglois,Sherbrooke,QCEditors,PhysicalGeography: MauriPelto,Dudley,MA,Chair RobertHellstrom,Bridgewater,MASteeringCommittee: AllanFrei,NewYork,NY,Chair JanetHardy,Hanover,NH GeorgeRiggs,Gambrills,MD RaeMelloh,Hanover,NH ChrisFuhrman,ChapelHill,NC CraigSmith,Saskatoon,SK AlexRoy,Sherbrooke,QC SteveHowell,Toronto,ON LauraThomson,Ottawa,ON JohnSugg,Boone,NCResearchCommittee: SeanHelfrich,Suitland,MD,Chair JamesBrylawski,Augusta,NJ RickFleetwood,Fredericton,NB KevinKang,Waterloo,ON KrysChutko,NorthBay,ON BartForman,CollegePark,MDWebMaster: AndrewKlein,CollegeStation,TXLocalArrangements: MichaelDurand&BryanMark,Columbus,OH


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WelcomeTuesday,June14❄WexnerCenterCafé

5:00-7:00pm RegistrationandIcebreakerreception

Session1.RecentAdvancesinRemoteSensingWednesday,June15❄ByrdPolarClimate&ResearchCenter(BPCRC)❄Chair:BartForman

8:00am Welcome:EllenMosley-Thompson,DirectoroftheBPCRC

8:10am DavidRobinson:50YearsofSatelliteSnowCoverExtentMappingOverNorthernHemisphereLands

8:30am ChrisDerksenetal.:Userrequirements,algorithmreadiness,andmodelingstudiesinsupportofterrestrialsnowmassradarmissionconcepts

8:50am BrianHennetal.:Comparisonofhigh-elevationLiDARsnowmeasurementswithdistributedstreamflowobservations

9:10am ManuelaGirottoetal:ALandsat-era(1985-2015)SierraNevada(USA)SnowReanalysisDataset(Invited)

9:30am NoahMolotchetal.:DevelopmentofUniversalRelationshipsbetweenSnowDepth,SnowCoveredAreaandTerrainRoughnessfromNASAAirborneSnowObservatorydata(Invited)

Session2.AdvancesinRemoteSensingTheoryandMethodsWednesday,June15❄ByrdPolarClimate&ResearchCenter(BPCRC)❄Chair:BryanMark

10:00am EdKimetal.:TheNASASnowExairbornesnowcampaign

10:30am LeungTsangetal.:SnowMicrostructureCharacterizationandNumericalSimulationofMaxwell’sEquationin3DAppliedtoSnowMicrowaveRemoteSensing(Invited)

10:50am AlainRoyeretal.:Comparisonofthreemicrowaveradiativetransfermodelsforsimulatingsnowbrightnesstemperature

11:10am EliDeebetal.CharacterizingSatellite-BasedPassiveMicrowaveEstimatesofSnowWaterEquivalentatSub-GridResolution

11:30am BartForman:SeeingandFeelingSnowfromSpace:AUnifiedRadiometricandGravimetricApproach


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11:50am 3-minutetheses(3MT,go.osu.edu/3mt):Recentadvancesinpassivemicrowaveremotesensingmethods,withpresentationsbycurrentgraduatestudents(orrecentgraduates):YonghwanKwon,DongyueLi,MohammadMousavi,OlivierSaint-Jean-RondeauandYuanXue.

Session3.PostersWednesday,June15,1:30–3:00❄MershonAuditoriumLobby

1. MilesEcclestoneetal.:Apictorialhistoryofchangesinpolarscienceandtechnology:anexamplefromglaciermeasurementsonAxelHeibergIsland,Nunavut,Canada,1959-2015.

2. DongyueLietal.:HowmuchwesternUnitedStatesstreamfloworiginatesassnow?

3. EricBurtonetal.:AirflowAssociatedwithSnowfallEventsontheQuelccayaIcecapofPeruDuringthe2014-2015WetSeason

4. JillColemanandRobertSchwartz:AnUpdatedU.S.BlizzardClimatology:1959-2014

5. KelseyCartwrightetal.:TerrainCharacteristicInfluenceonSnowAccumulationandPersistence:CaseStudy

6. ReedParsons&ChristopherHopkinson:In-situLightEmittingDiodeDetectionandRangingfortheMappingofSnowSurfaceTopographyandDepth

7. RogerdeRooetal.:Inexpensivein-situsnowpacksensorsfortemperature,densityandgrainsize:Firstlight

8. KrystopherChutko:Seasonalandinterannualvariabilityinsnowandstreamflowδ18Osignatures

9. YonghwanKwonetal.:Canassimilationofmicrowaveradiancedataimprovecontinental-scalesnowwaterstorageestimates?(Invited)

10. MohammadMousavietal.:ElevationAngularDependenceofWidebandAutocorrelationRadiometric(WiBAR)RemoteSensingofDrySnowpackandLakeIcepack

11. OlivierSaint-Jean-Rondeauetal.:Parameterizationofsnowmicrostructureforpassivemicrowaveradiometry

12. JulieMilleretal.:MappingfirnaquifersontheGreenlandandAntarcticicesheetsfromspaceusingC-bandsatellite-bornescatterometry

13. AlexandraBringeretal.:ObservationsofsnowpackswithanUltraWideBandRadiometer

14. YunaDuanetal.:ABayesianretrievalofGreenlandicesheetinternaltemperaturefromultra-widebandsoftware-definedmicrowaveradiometer(UWBRAD)measurements

15. EunsangChoetal.:ComparisonbetweenAMSR2andAMSR-ESnowWaterEquivalentusingSSM/IovertheNorthCentralU.S.

16. RyanCrumleyetal.:AnalyzingSeasonalSnowCoverFrequencyUsingtheMODIS/TerraDailySnowCoverProductwithGoogleEarthEngineinthePacificNorthwestandCalifornia


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17. CarrieVuyovichetal.:SensitivityAnalysisofpassivemicrowavebrightnesstemperaturestodistributedsnowmelt

18. ElizabethDyer&JoanRamage:InvestigatingtheinterplaybetweenwarmwinteranomaliesandglacialmeltingintheArctic:doearlywarmingeventsmatter?

19. DorothyHalletal.:ComparisonofMODISandVIIRSsnow-coverproductstostudydata-productcontinuityintheCatskillMountainwatersheds,NewYork

20. RichardKellyetal.:TheGCOM-W1Satellite-basedMicrowaveSnowAlgorithm(SMSA)

21. JoanRamageetal.:MELTONTHEMARGINS:CalibratedEnhanced-ResolutionBrightnessTemperaturestoMapMeltOnsetNearGlacierMarginsandTransitionZones

22. YuanXueandBartonForman:Decouplingatmospheric-andforest-relatedradianceemissionsfromsatellite-basedpassivemicrowaveobservationsoverforestedandsnow-coveredlandinNorthAmerica

Session4.PostersWednesday,June15,3:15–4:45❄MershonAuditoriumLobby

23. JasonEndriesetal.:VerticalstructureandcharacterofprecipitationinthetropicalhighAndesofsouthernPeruandnorthernBolivia

24. JamesFeiccabrino:Usingcloudbaseheighttodecreasemisclassifiedprecipitationinhydrologicalmodels

25. JohnathanKirk:LargeprecipitationeventsatSNOTELsitesandstreamflowvariabilityintheUpperColoradoRiverBasin

26. AndrewKlein:DailysnowdepthatPalmerStation,Antarctica,2007-2014:aninitialanalysis

27. SebastianSchlögletal.:Howdostabilitycorrectionsperformoversnow?

28. AaronThompsonetal.:SpatialvariabilityofsnowatTrailValleyCreek,NWT

29. MelissaWrzesienetal.:ConsiderationofMountainSnowStoragefromGlobalDataProducts

30. KellyElder&MatthewSturm:3rdWintercourseforfieldsnowpackmeasurementsNASASnowWorkingGroup-Remotesensing(iSWGR)

31. MartinSchneebeli&JuhaLemmetyinen:2ndEuropeanSnowScienceWinterSchool

32. QinghuanLi&RichardKelly:Terrestriallaserscanningobservationsoftreecanopyinterceptedsnow

33. SeanHelfrichetal.:EvaluationofAlgorithmAlternativesforBlendedSnowDepthintheIMS

34. AdamHunsakeretal.:Evaluationofsatellite-basedobservationsforcapturingearlywintersnowmeltwithinmid-latitudebasins

35. RhaeSungKimetal.:Spectralanalysisofairbornepassivemicrowavemeasurementsforclassificationofalpinesnowpack


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36. AlexLangloisetal.:Rain-on-snowandicelayerformationdetectionusingpassivemicrowaveradiometry:Anarcticperspective

37. DongyueLietal.:EstimatingsnowwaterequivalentinamountainousSierraNevadawatershedwithspaceborneradiancedataassimilation

38. JinmeiPanandMichaelDurand:FormulationofaBayesianSWEretrievalalgorithmusingX-andKu-measurements

39. GeorgeRiggsetal.:StatusoftheMODISC6SnowCoverandNASASuomi-NPPVIIRSSnowCoverDataProducts

40. SaberiNastaranetal.:SnowPropertiesRetrievalusingDMRT-MLinaStatisticalFrameworkUsingPassiveMicrowaveAirborneObservations

41. ShurunTanetal.:Modelingpolaricesheetemissionfrom0.5-2.0GHzwithapartiallycoherentmodeloflayeredmediawithrandompermittivitiesandroughness(Invited)

42. OliverWigmoreetal.:UAVMappingofDebrisCoveredGlacierChange,LlacaGlacier,CordilleraBlanca,Peru

43. YuanXueandBartonForman:Canregional-scalesnowwaterequivalentestimatesbeenhancedthroughtheintegrationofamachinelearningalgorithm,passivemicrowavebrightnesstemperatureobservations,andalandsurfacemodel?

BanquetWednesday,June15❄OSUFacultyClub

6:00-8:00pm† Thebanquetagendaincludespresentationofawards.Thebanquetkeynotespeaker,ProfLonnieThompson,ispresentingon:“GlobalClimateChange:aperspectivefromtheWorld'sHighestMountains.”

†Happyhourbeginsat5pmontheFacultyClubpatio.

Session5.Remotesensingapplicationsforcryosphericscience:Fromtheicesheetstothemid-latitudesThursday,June16❄ByrdPolarClimate&ResearchCenter(BPCRC)❄Chair:JoanRamage

8:30am NathanAmador:AssessingaDepth-retrievalmethodfordeterminingSupraglacialMeltLakeVolume

8:50am KyungInHuhetal.:Evaluating50yearsoftropicalPeruvianglaciervolumechangefrommulti-temporaldigitalelevationmodels(DEMs)andglacierflowandhydrologyintheCordilleraBlanca,Peru(Invited)

9:10am CarolineDolantetal.:DetectionofRain-On-SnoweventsintheCanadianArcticArchipelagobetween1980-2014usingPassiveMicrowaveRadiometry

9:30am JessicaCherryetal.:RecentairbornemeasurementsofsnowandiceinInteriorAlaska


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9:50am RuneSolbergetal.:Singleandmulti-sensorsnowwetnessmappingbySentinel-1andMODISdata

10:10am SamuelTuttleetal.:ComparisonofSatellitePassiveMicrowave,AirborneGammaRadiationSurvey,andGroundSurveySnowWaterEquivalentEstimatesintheNorthernGreatPlains

Session6.Snowandiceprocesses,hydroclimatology,andchangeThursday,June16❄ByrdPolarClimate&ResearchCenter(BPCRC)❄Chair:KrystopherChutko

10:45am RossBrownetal.:NorthernHemispherewinterthawevents–characteristics,trendsandprojectedchanges

11:05am AaronWilsonetal.:ImprovingatmosphericcirculationandturbulentheatfluxeswiththeArcticSystemReanalysis(Invited)

11:25am SebastianSchlögl:Energybalanceandmeltoverapatchysnowcover


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AssessingaDepth-retrievalmethodfordeterminingSupraglacialMeltLakeVolume

NathanaelAmador

OhioWesleyanUniversity,DepartmentofGeologyandGeography,Delaware,OH

2000–2012Landsat-7imageryisusedtomonitortheevolutionoffivesupraglacialmeltlakesintheablationzonenorthofJakobshavnIsbrætorelatemeltlakevolumeandtherequiredsensibleenergytoproducethemeltwater.Iutilizethecumulativepositivedegreeday(cPDD)metricformeltproductionandadepth-reflectancemethodologytoestimatemeltlakedepths,andthusderivetotalmeltlakevolume.In71%ofinstanceswhentheannualpeakmeltlakevolumeoccurs,thecalculatedvolumeexceedstheKrawczynskietal.(2009)thresholdforhydrofracturing.Thevolumeresultsfortheselakesindicatethattheyhavethepotentialtohydrofracturemultipletimesoverthestudyperiod,whichcanaffectnearbyiceflowvelocityviabasallubrication.Theinter-annualvariabilityinmeltlakevolume,whencomparedtocPDD,suggeststhatmeltwaterproductionislessimportanttomeltlakesize(areaandvolume)thanthelocalicesheetsurfacetopography.Whenrelatinglakedepthsusingthedepth-reflectancemethodology,thereisminimaldifferenceinthemaximummelt-lakedepthbetweenin-situmeasurementsandthedepth-reflectancemethodology(~9%),suggestingthatthedepth-reflectancemethodologyaccuratelyestimatesmelt-lakeinundationdepthforsupraglaciallakes.


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ObservationsofsnowpackswithanUltraWideBandRadiometer

A.Bringer1,J.Johnson1,K.Jezek2,M.Durand2

1ElectroScienceLaboratory,DepartmentofElectricalandComputerEngineering,TheOhioState

University,Columbus,OH2SchoolofEarthSciences&ByrdPolarResearchCenter,TheOhioStateUniversity,Columbus,

OH Microwaveradiometersareoftenusedforcryosphericstudiesandespeciallytoobservesnowpacks.Theyusuallyoperateatasinglefrequency,18GHzor37GHz,ashighfrequenciesaresensitivetotheinternalstructureofsnow(layering,grainsize,density).However,recentstudieshaveshownthepotentialofusinglowerfrequenciessuchasLBand(1.4GHz)toretrieveinformationaboutthefreeze/thawstateofthesoilbeneaththesnowpack.Thebrightnesstemperatureatsuchfrequenciesshowssensitivitytothethicknessofthefrozensoilandthesnowthickness.

Wearepresentlydevelopingaradiometerforcryosphericstudies,calledtheUltraWideBandSoftwareDefinedRadiometer(UWBRAD).Itmeasuresthermalemissionoverfrequenciesfrom0.5to2GHzin12frequencychannels.Becauseofthedielectriccontrastbetweenthesnowpermittivityandthefrozensoilone,weinvestigatewhetherUWBRADmicrowavespectracanbeusedtomeasurethesnowthickness.

Thesoilismodeledasatwolayermedium:afrozenlayerontopandathawedlayerbelow.Thesnowpackisconsideredasaplanarlayeredmediawithvariationsintemperatureanddensity.Becausetheelectricalpropertiesaretemperaturedependent,weadoptasimple,linearmodelforthetemperatureprofileinthesnow.Weuseacoherentradiativetransfermodeltocalculatethesnowpackbrightnesstemperature.Inourpreliminarystudies,weobserveanoscillatingpatternwithfrequencywhichalsovarieswithsnowthickness.ThisindicatesthatUWBRADmaybeusedtoinfersnowthicknessoverfrozensoil.


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NorthernHemispherewinterthawevents–characteristics,trendsandprojectedchanges

RossBrown,LiboWang,PeterToose,andChrisDerksen

ClimateProcessesSection,EnvironmentandClimateChangeCanada,Montréal,Québec

Wintermelt/refreezeeventsmodifythephysicalpropertiesofsnowwithpotentiallysignificantimpactsonthesurfaceenergybudget,hydrologyandsoilthermalregime.Therefreezingofmeltwatercanalsocreateicelayersthatadverselyimpacttheabilityofungulatetravelandforaging,andexertuncertaintiesinsnowwaterretrievalfrompassivemicrowavesatellitedata.Theconventionalwisdomisthatthefrequencyoftheseeventsincreasesunderawarmingclimate.Thishypothesisisevaluatedfromananalysisofwinterthaweventsfromatmosphericreanalyzes,satellitepassivemicrowavedataandclimatemodels.Theanalysisshowsthattrendsinwinterthaweventsarestronglydependentontheanalysismethod,andthattheuseofafixedseasonalwindowcangenerateartificialincreasesinwinterthawfrequenciesfromatemporalshiftintheperiodoftheyearwheretheseeventsaretypicallyobserved.TheanalysisalsoshowsthatthefrequencyofwinterthaweventsissignificantlycorrelatedtothelengthofthesnowaccumulationseasonoverlargeareasoftheNHsnowcoveredarea,whichimpliesdecreasesinwinterthawfrequenciesinresponsetowarming.ProjectedchangesinthawfrequencyarepresentedforsomeofthemodelsparticipatingintheCMIP5andCORDEXexperiments.


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AirflowAssociatedwithSnowfallEventsontheQuelccayaIcecapofPeruDuringthe2014-

2015WetSeason

EricJ.Burton1,L.BakerPerry1,AntonSeimon1,2,JasonL.Endries1,MaxwellRado3,SandroArias4

1DepartmentofGeographyandPlanning,AppalachianStateUniversity,Boone,NC2ClimateChangeInstitute,UniversityofMaine,Orono3UniversidadNacionaldeSanAntonioAbáddeCusco,Perú4ServicioNacionaldeMeteorologíaeHidrología(SENAMHI),Perú

TheQuelccayaIcecap,locatedintheCordilleraVilcanotaofSouthernPeru,isthelargestglacierinthetropicsfromwhereicecoresdatingbacknearly2,000yearsprovideoneofthemostimportantrecordsoflate-Holoceneclimates.Thisposteranalyzesthetiming,trajectories,andsynopticpatternsassociatedwithprecipitationeventsduringthe2014-2015wetseason.Ameteorologicalstationinstalledat5,650maslneartheQuelccayasummitinOctober2014providesmeteorologicaldataincludingprecipitationamount,typeandintensity,snowdepth,insolation,relativehumidity,andwindspeedanddirection.NOAA’sHybridSingleParticleLagrangianIntegratedTrajectoryModel(HYSPLIT)provides72-hourbackwardairtrajectoriesforeachprecipitationeventusingGDASdatawith0.5°resolution,andERA-Interimdataareusedtoexaminesynoptic-scalepatternsofvariousmeteorologicalvariablesforprecipitationevents.Resultssuggestthattrajectoriesassociatedwithprecipitationeventscomepredominantlyfromthenorthandnorthwest(63%)withanothermaximumfromthesoutheast(25%).Northwesttrajectorieshavethehighestnetcontribution(34%ofannualtotal),whilethosefromthePacificproducethelargestsnowfalleventsonaverage(6.3cm).Compositeplotsofvectorwindssupportthetrajectoryanalysis.Temperatureandwindspeedvariedlittlethroughouttheinitialstudyperiod,andthepresentweathersensorshowsthatfrozenprecipitation,inparticulargraupel,wasthedominantprecipitationtype.Ofthe250precipitationeventsthatoccurredduringthestudyperiod,88%hadasourceregionintheAmazonBasin.Anighttimemaximuminprecipitationisinferredtobepredominantlystratiforminnature,whileanafternoonmaximumisinferredtobepredominantlyconvective.


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TerrainCharacteristicInfluenceonSnowAccumulationandPersistence:CaseStudy

KelseyCartwright,N.ReedParsons,GerrardBiggins,JoshuaBaerg,ChristopherHopkinson

DepartmentofGeography,UniversityofLethbridge,Lethbridge,AB

Mountainsnowmeltcontributes70-90%ofstreamflowinWesternCanada.Anenrichedunderstandingofsnowpackdynamicsinheadwaterregionsisessentialtowaterresourcemanagementinthefaceofunpredictableclimaticpatternsassociatedwithclimatechange.CurrentsnowpackmonitoringintheOldmanWatershedtoapproximateSWEforwatersupplyandfloodriskpredictionsdonotprovideanaccuraterepresentationoftruesnowwaterequivalencyduetothelargespatialvariationinmountainousterrainattributes,forexampleslope,aspect,substrateandforestcoveracross~26,000km2.Asaresultofthesedynamicterraincharacteristics,snowdepthexhibitsanevengreaterspatialvariationincomparisontosnowdensity.FieldworkwascarriedoutintheWestCastlewatershed,thesecondhighestyieldingsub-watershedoftheOldmandrainage,ataskihillwhereourhydrometeorologicalstationsoccuralonganelevationalgradientaspartofaGovernmentofAlbertafundedwaterresourcemonitoringresearch.Depthmeasurementswerecollectedinareasrepresentativeofvariousterrainattributesandecotones.Usingregionalin-situmeteorologicalstationdata,fieldvalidationmeasurementsandLiDARremotesensingdata(September,February2014;April2016)collectedmid-winterandattheonsetofspringmelt,relationshipsbetweenthevariousmacroandmicroscalecatchmentprocessesprovideanimprovedunderstandingoftheterraincharacteristicinfluenceonsnowaccumulationandpersistence.

Boththeidentificationandquantificationoftheterraincharacteristicinfluenceonsnowaccumulationandpersistence,enablethemodellingofdepthacrosslargerareasthusprovidingtheprecisedatarequiredtomakeinformedwaterresourcemanagementdecisions.


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RecentairbornemeasurementsofsnowandiceinInteriorAlaska

JessicaCherry

InternationalArcticResearchCenter,UniversityofAlaska,Fairbanks

ThistalkwilldiscussresultsfromrecentairbornemeasurementsofsnowandiceinInteriorAlaskafromimaging(optical,nearinfra-redandthermalinfra-red)andmicrowavesensorsusingStructurefromMotionandothertechniques.ImpactsofGPSaccuracyonsnow-relatedphenomenawillbedescribed,includingthepositionalerrorbudget.OurgrouphastwomodifiedCessnasforthiseffortandwillalsodiscusstheeconomicsofairbornemeasurementsfromunmannedversusmannedsystems.


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ComparisonbetweenAMSR2andAMSR-ESnowWaterEquivalentusingSSM/Ioverthe

NorthCentralU.S.

EunsangCho,SamuelTuttle,andJenniferM.Jacobs

CivilandEnvironmentalEngineering,UniversityofNewHampshire,Durham

Satellite-basedpassivemicrowavesensorsenablespatiallydistributedsnowpackmonitoringataglobalscale.TheAdvancedMicrowaveScanningRadiometer2(AMSR2)isarelativelynewpassivemicrowavesatellitethatprovidesestimatesofsnowdepth(SD)andsnowwaterequivalent(SWE).AMSR2continuesthelegacyoftheAdvancedMicrowaveScanningRadiometerfortheEarthObservingSystem(AMSR-E),whichstoppedoperationinOctober2011.However,thequalityofAMSR2SWEretrievalshasnotyetbeenevaluatedincomparisonwithitspredecessor.ThisstudycomparedtheweeklymaximumAMSR2andAMSR-ESWEproductsovertwelvewinterseasons(AMSR-Eperiod:2002-2011,AMSR2period:2012-2015)toSSM/ISWEestimatesover1176watershedsintheNorthCentralUnitedStates.Forconsistency,boththeAMSR2andAMSR-EsatelliteSWEproductsusedtheKellyalgorithm(Kellyetal.,2009).Resultsshowthatthetwosatellite-basedSWEretrievalshavetemporallyreasonableagreementwithSSM/ISWEestimates(Changalgorithm;Changetal.,1987).However,yearlybiasmapsbetweenAMSR2andSSM/ISWEareclearlydifferentthanbetweenAMSR-EandSSM/I.Particularlyinforestedareas,themagnitudeofAMSR2SWEestimatesismuchlargerthanSSM/I,unlikeAMSR-E.WhenusingthenormalizedSWEanomaly,thespatialpatternofbiasshowsgoodagreementbetweenAMSR2andAMSR-E.ThedifferingSWEmagnitudesmayberelatedtothecalibrationofAMSR2brightnesstemperatures.


20

Seasonalandinterannualvariabilityinsnowandstreamflowδ18Osignatures

KrystopherJ.Chutko1andAprilL.James2

1DepartmentofGeographyandPlanning,UniversityofSaskatchewan,Saskatoon,SK2DepartmentofGeography,NipissingUniversity,NorthBay,ON

Seasonalsnowpacksoftenplayalargeandimportantroleinhydrologicalprocesses,typicallymanifestedasaspringfreshet.Fromanisotopicstandpoint,thisfreshetmarksthe“lightest”wateroftheyear,beingfedbyisotopicallylightwaterderivedfromspringsnowmelt.Seasonalandinterannualvariationsintheisotopiccompositionofstreamflowarestronglyrelatedtotheisotopicconditionsofthewintersnowpackandhaveimplicationsonhydrologicalanalysesandmodeling.Fouryearsofregionalsnowandstreamflowisotopemeasurements(2013–2016)intheLakeNipissingregionofOntario,Canada,illustratethisvariabilityinsnowpackisotopiccompositionanditsimpactonstreamflowisotopiccomposition.Muchofthisvariabilityisderivedfromwinterairtemperature.Averagewinter(DJFM)airtemperaturehasvariedfrom-5.3°Cin2016to-13.5°Cin2014,avariabilitythatismirroredinthesnowpackisotopicsignatureineachyearaswellasintheisotopicsignatureofstreamflow.Snowpacksignaturesweremeasuredusingbulkcoresamplesandsnowmeltsignaturesweremeasuredwithacombinationofsnowmeltlysimetersandpassivewicks.Theinterannualvariabilityinsnowpackδ18Oisshowntoimpactstreamflowisotopicsignatures.For9catchmentsreportedhereintheLakeNipissingregion(35to6875km2),spring(MAM)streamflowδ18Owas0.67‰lighterandsummer(JAS)streamflowδ18Owas0.82‰lighter,onaveragein2014vs.2013,forexample.However,theseasonalamplitudeofδ18Oremainedconsistentbetweenyears,varyingbyjust0.15‰.


21

AnUpdatedU.S.BlizzardClimatology:1959-2014

JillS.M.Coleman1andRobertM.Schwartz2

1DepartmentofGeography,BallStateUniversity,Muncie,IN2CenterforEmergencyManagementandHomelandSecurityPolicyResearch,Universityof

Akron,OH

Satellite-basedpassivemicrowavesensorsenablespatiallydistributedsnowpackmonitoringataglobalscale.TheAdvancedMicrowaveScanningRadiometer2(AMSR2)isarelativelynewpassivemicrowavesatellitethatprovidesestimatesofsnowdepth(SD)andsnowwaterequivalent(SWE).AMSR2continuesthelegacyoftheAdvancedMicrowaveScanningRadiometerfortheEarthObservingSystem(AMSR-E),whichstoppedoperationinOctober2011.However,thequalityofAMSR2SWEretrievalshasnotyetbeenevaluatedincomparisonwithitspredecessor.ThisstudycomparedtheweeklymaximumAMSR2andAMSR-ESWEproductsovertwelvewinterseasons(AMSR-Eperiod:2002-2011,AMSR2period:2012-2015)toSSM/ISWEestimatesover1176watershedsintheNorthCentralUnitedStates.Forconsistency,boththeAMSR2andAMSR-EsatelliteSWEproductsusedtheKellyalgorithm(Kellyetal.,2009).Resultsshowthatthetwosatellite-basedSWEretrievalshavetemporallyreasonableagreementwithSSM/ISWEestimates(Changalgorithm;Changetal.,1987).However,yearlybiasmapsbetweenAMSR2andSSM/ISWEareclearlydifferentthanbetweenAMSR-EandSSM/I.Particularlyinforestedareas,themagnitudeofAMSR2SWEestimatesismuchlargerthanSSM/I,unlikeAMSR-E.WhenusingthenormalizedSWEanomaly,thespatialpatternofbiasshowsgoodagreementbetweenAMSR2andAMSR-E.ThedifferingSWEmagnitudesmayberelatedtothecalibrationofAMSR2brightnesstemperatures.


22

AnalyzingSeasonalSnowCoverFrequencyUsingtheMODIS/TerraDailySnowCover

ProductwithGoogleEarthEngineinthePacificNorthwestandCalifornia

RyanCrumley,AnneW.Nolin,andEugeneMar

CollegeofEarth,Ocean,andAtmosphericSciences,OregonStateUniversity,Corvallis

Newsnowmetricsareneededtocharacterizechangingsnowcoverinawarmingworld.Forthisproject,wecomputethefrequencyofremotelysensedsnowcoverduringthewinterseason,foreachpixelinourmaritimeWestCoaststudyregionandexplorespatio-temporaltrends.RemotesensingofsnowcoveredareausingtheMODIS/TerraSnowCoverDailyL3500m(MOD10A1)productisnowavailabletoscientistsusingGoogleEarthEngine(GEE).GEEstoresandprovidesaccesstoamulti-petabytecatalogofsatelliteimagesforgeospatialanalysis,employingbothJavascriptandPythonAPIs.TheMOD10A1SnowCoverProductalongwiththeGEEcloudcomputinginfrastructureallowsforregionaltoglobal-scaledataprocessingtobeperformedquicklyandefficiently,withouthavingtodownloadmassiveamountsofdata.Specifically,theobjectivesareto:1)calculateSnowCoverFrequency(SCF)fromOctobertoJulyovera16-yearperiod(2001to2015)fortheCascadesmountainrangeinOregonandWashingtonandtheSierraNevadamountainrangeinCalifornia;2)evaluatemulti-yeartrends;3)disseminatetheGEEscriptsandcodesothatthisprocessingcaneasilybereadilyreproducedforanylocation,geometry,orregiononEarth.SnowCoverFrequencyiscomputedasthenumberoftimesduringthesnowseasonthatapixelissnowcovereddividedbythenumberofvalidobservationsforthatpixel.TrendsarecomputedusingtheMann-Kendallstatisticandareexaminedbyregion.TheresultsofthisresearchserveasavaluabletoolforwatermanagersandpolicymakersthatrelyonsnowmeasurementsforseasonalstreamflowestimatesandwhowouldliketosupplementthetraditionalmetricofApril1SnowWaterEquivalent.


23

Inexpensivein-situsnowpacksensorsfortemperature,densityandgrainsize:Firstlight

RogerDeRoo,EricHaengel,SteveRogacki,AdamSchneider,ChandlerEkinsandSeyedmohammadMousavi

DepartmentofAtmospheric,Oceanic,andSpaceSciences,UniversityofMichigan,AnnArbor

Asuiteofsmall,batteryoperateddevicesforimplantationinasnowpackhasmadeitsfirstmeasurementsintheWinterof2016.Theymeasureandlogsensoroutputsrelatedtosnowpackparametersoftemperature,density,moistureandgrainsize.Thetemperatureisprovidedbyanelectronicthermometer;snowdensityandmoistureaffectanopenresonantcircuitoperatingnear950MHz;grainsizeanddensityaffectthescatteringofanopticallinkoperatingat880nm.Fiveunitsweredeployedintheroughly30cmdeepsnowpackattheUniversityofMichiganBiologicalStationintwosnowpits,wheretheymademeasurementsevery5minutesforapproximately10days.Uponextraction,temperature,densityandgrainsizeofthesnowpackwereobservedmanually.

Threemoreunitswereinvolvedinaninter-comparisonexperimentattheUSArmy'sColdRegionsResearchandEngineeringLaboratory.Twosamplesofoldsnowpackfromtheirarchives,twofreshsnowsamples,andoneartificiallygrownsnowsamplewerealsocharacterizedwithmicro-computedtomography,infraredreflectometry,suchasUniversityofMichigan'sNearInfraredEmittingReflectanceDome(NERD),andmanualmethods.InApril2016,themeasurementsarebeinganalyzedandcalibrated.Wewillreportonresults,andlessonslearned,attheJuneconference.


24

CharacterizingSatellite-BasedPassiveMicrowaveEstimatesofSnowWaterEquivalentatSub-GridResolution

E.J.Deeb,C.A.Hiemstra,S.F.Daly,C.M.Vuyovich,andJ.B.Eylander

USArmyColdRegionsResearchandEngineeringLaboratory(CRREL),Hanover,NH

Snowwaterequivalent(SWE)istheamountofwatercontainedwithinthesnowpackifmelted.Theaccurateassessmentofthissnowparameteriscrucialinestimatingspringrunoffasitrelatestowaterresourcemanagement,floodhazardmitigation,droughtmonitoring,andclimatechangeimpacts.Satellite-basedpassivemicrowaveestimatesofSWEoffertheonlyoperationalplatformforwhichanearreal-time,globalSWEproductisavailable.Ingeneral,satellite-basedpassivemicrowaveSWEestimatesarepossibleduetothenaturallyemittedmicrowavesignalfromthesoilbeingattenuatedbythesnowpack.Thismicrowaveenergyisrelativelysmall;therefore,thesatellite-basedproductsareoftenatverycoarseresolution(tensofkilometers)inordertodetectthesignal.Forhydrologyapplications,passivemicrowaveestimatesofSWEareparticularlydifficulttointerpretwhenonlyahandfulofpixelsrepresentasinglehydrologicbasin.Moreover,passivemicrowaveretrievalalgorithmsaresubjecttodifficultiesinbothdeepandshallowsnow(dependingonthemicrowavefrequenciesavailableonthesatelliteplatform)aswellasuncertaintiesduetoforestfraction,snowmicrostructure,andsnowwetness.Here,aspatially-distributed,snow-evolutionmodelingsystem(SnowModel)isusedtosimulate14years(wateryears2000through2013)ofsnowpropertiesfortheHubbardBrookLongTermEcologicalResearchsite(NewHampshire,USA)atfineresolution(50meters).Thesedataareusedtogeneratesnowdepthclimatologyoverthesatellite-basedpassivemicrowavepixelsthatencompasstheHubbardBrookwatershed.ThisclimatologyisthenusedinconjunctionwiththedailypassivemicrowaveestimateofSWEtoappropriatelydistributethesatellite-basedobservationatcoarseresolutiontoasub-grid,finerresolution.Themethodologyandresultsofthemodeltechniquearepresented;andwhencomparedtoanindependentsnowdepthobservationwithinthebasinshowbetteragreementandimprovedmodelefficiency(R2=0.76andNash-Sutcliffemodelefficiency=0.70)whencomparedtosimplythesatellite-basedpassivemicrowaveestimates(R2=0.61andNash-Sutcliffemodelefficiency=0.40).Potentialbenefitsofusingthismodeltechniqueinsnowhydrologyapplicationsarealsodiscussed.


25

Userrequirements,algorithmreadiness,andmodelingstudiesinsupportofterrestrialsnow

massradarmissionconcepts

ChrisDerksen1,StephaneBelair2,JoshuaKing1,CamilleGarnaud2,LawrenceMudryk3,YvesCrevier4,MelanieLapointe4,andRalphGirard4

1ClimateResearchDivision,EnvironmentandClimateChangeCanada,Toronto2MeteorologicalResearchDivision,EnvironmentandClimateChangeCanada,Montréal,Québec3DepartmentofPhysics,UniversityofToronto4CanadianSpaceAgency,Saint-Hubert

Thesnowremotesensingcommunityhaslonggrappledwithhowtoprioritizeobservationalrequirementsandtechnologicalsolutionsduetodifferingneedsrelatedtosnowextent(SE)versussnowwaterequivalent(SWE),andthetradeoffsbetweenspatialresolutionandrevisittimewhichdifferforalpinehydrologicalapplicationsversushemisphericclimateandoperationallandsurfacemodelingneeds.Asingleobservingstrategysimplycannotmeetalltheserequirements.EnvironmentandClimateChangeCanada(ECCC)recentlyidentifiedmoderateresolution(~1km),dailyhemisphericSWEasapriorityobservationalgapwhichlimitsoperationalenvironmentalmonitoring,services,andprediction.ThispresentationwillprovideanoverviewofcurrentscienceactivitiesatECCCinsupportofthedevelopmentofradarmissionconceptsinpartnershipwiththeCanadianSpaceAgency(CSA)toaddressthisobservationalgap:

1. AnassessmentofcurrentlyavailablegriddedhemisphericSWEproductswasperformedtoestablishthebaselineofcurrentcapabilities.Thesedatasets(frompassivemicrowaveremotesensing,modernreanalysis,andphysicalsnowmodels)areavailableonlyatacoarsespatialresolution(25kmorgreater),exhibitahighdegreeofspreadbetweenproducts,andhavepoorlyconstraineduncertaintyduetosystematic(bias)andrandomerrorswhenevaluatedwithinsituobservations.

2. ExperimentalairbornedatasetsarebeingutilizedtoidentifysnowvolumeandstratigraphicinfluencesonradarsignaturesatX-andKu-band.AnalysisofdatafromexperimentalcampaignsinCanadashowradarsensitivitytoSWE,butfirstguessmodelderivedinformationonsnowmicrostructureisrequiredasaretrievalinput.

3. AnObservingSystemSimulationExperiment(OSSE),performedusingtheCanadianLandDataAssimilationSystem(CaLDAS),isbeingutilizedtoidentifycriticalresolution,revisit,andretrievalaccuracythresholdsinordertoensuretheuserrequirementsoftheoperationallandsurfacemodelingcommunitycanbeaddressedwitharadarconcept.


26

Emerginginternationalpartnershipopportunitieswillalsobepresented,includinghowaspaceborneradardesignedtoaddressneedsrelatedtoterrestrialsnowwouldalsoprovidesuitablemeasurementsforseaice,landice,andoceanvectorwindapplications.


27

DetectionofRain-On-SnoweventsintheCanadianArcticArchipelagobetween1980-2014usingPassiveMicrowaveRadiometry

C.Dolant1,2,A.Langlois1,2,L.Brucker3,4,B.Montpetit1andA.Royer1,2

1Centred’ApplicationsetdeRecherchesenTélédétection,UniversitédeSherbrooke,Quebec2Centred’ÉtudesNordiques,Quebec3NASAGoddardSpaceFlightCenter,CryosphericSciencesLaboratory,Greenbelt,MD4UniversitiesSpaceResearchAssociation,GoddardEarthSciencesTechnologyandResearch

StudiesandInvestigations,Columbia,MD

Climatechangeimpactsinnorthernenvironmentsaresignificant,especiallyintundraareas.Risingtemperatures,changesintheprecipitationregimeareamongstthestrongestconsequencesofclimatewarmingandvariabilityintheArcticsincetheearly1980’s(ListonandHiemstra,2011).Ofparticularrelevance,rain-on-snow(ROS)eventsincreasethepresenceofliquidwatercontent(LWC)inthesnowpackandareresponsiblefortheformationoficecrusts(Dolantetal.,2016,HydrologicalProcesses)thathaveastrongimpactonecology,hydrologyandenergybalanceofthesnowpackbychangingtheinternalstructureofthedifferentsnowlayers.

ThespatialandtemporaldistributionofROSacrosstheCanadianArcticArchipelago(CAA)remainspoorlyunderstoodowingtotheirsporadicnatureintimeandspace.Theuseofremotesensing,inparticularpassivemicrowaves(PMW),allowustoobtaininformationonthedifferentlayersofthesnowpack,thusrepresentinganinterestingavenuefortrackingandstudyingROSeventsintheArctic.

Inthisstudy,wehighlightthedistributionandevolutionofROSoccurrencesinventoriedsince1980at14weatherstationsintheCAA,andintroduceanadaptationofthealgorithmofROSdetectionusingpassivemicrowaveradiometryproposedbyDolantetal.2016,inordertoestablishpatternsoftemporalandspatialevolutionofROSevents.Furthermore,simulatingtheeffectsofROSusingaradiativetransfermodel(i.e.MEMLS(WiesmannandMatzlër,1999)drivenwithsnowpitmeasurementsandvariationofLWCthreshold)willimprovetheunderstandingofthiscomplexphenomenon.

Acrossthe14weatherstations,700ROSeventsweresurveyedsince1980,wheremorethan80%occurredduringthespringseason.


28

ABayesianretrievalofGreenlandicesheetinternaltemperaturefromultra-widebandsoftware-definedmicrowaveradiometer

(UWBRAD)measurements

YunaDuan1,MichaelDurand1*,KenJezek1,CaglarYardim2,AlexandraBringer2,MustafaAksoy2,JoelJohnson2

1SchoolofEarthSciencesandByrdPolarandClimateResearchCenter,OhioStateUniversity,

Columbus2ElectroscienceLaboratoryandDepartmentofElectricalEngineering,OhioStateUniversity,

Columbus

Icesheetinternaltemperatureisanimportantfactorinunderstandingglacierdynamics.Theultra-widebandsoftware-definedmicrowaveradiometer(UWBRAD)isdesignedtoprovideicesheetinternaltemperaturebymeasuringlowfrequencymicrowaveemission.Twelvechannelsrangingfrom0.5to2.0GHzarecoveredbytheinstrument.AfourchannelprototypeofUWBRADwascompletedandoperatedinAntarcticicesheetatDome-Cfromatower.ABayesianframeworkisdesignedtoretrievetheicesheetinternaltemperaturefromsimulatedUWBRADbrightnesstemperature(Tb)measurementsfortheGreenlandair-bornedemonstrationscheduledforSeptember2016.

A1-Dheat-flowmodel,theRobinModel,isusedtogeneratetheicesheetinternaltemperatureprofile.Itrequiressurfacetemperatureice,sheetthickness,snowaccumulationrateandgeothermalheatfluxasinputandcalculatessteadystatetemperaturesasafunctionofdepth.Thecoherentradiationtransfermodel,whichneglectsscattering,utilizestheRobinmodeltemperatureprofileandverticaldensityprofileasinputandcalculatesTb.Atlowerfrequencies,deeperandwarmericecontributetotheemissionandhigherbrightnesstemperaturecanbemeasured;Whileathigherfrequencybands,theresultingbrightnesstemperatureislower,thusprovidesthebasisofretrieval.Theeffectivesurfacetemperature,geothermalheatfluxandthevarianceofupperlayericedensityareleast-wellknownandaretreatedasunknownrandomvariableswithintheretrievalframework.

Foreachunknownparameter,arangeofpossiblevalueswasidentified.Thecoherentmodelwasusedtogeneratealook-uptablebetweentheunknownparametersandtheTb.AsetofsyntheticUWBRADobservationswasgeneratedandcorruptedwithwhitenoisetomimictheUWBRADobservationalerror.ABayesianframeworkwasdevelopedtoestimatethethreeunknownparameters,usingtheMetropolisalgorithm,aMarkovChainMonteCarlo(MCMC)approach.Weexaminedtheresultsusingthethreesciencegoals:estimationofthe10-mfirntemperature,theaveragetemperatureintegratedwithdepth,andtheentiretemperatureprofile.Weconductarandomwalkbetweenthesampling


29

spacedefinedbythepriors.Ateachstep,weevaluateeachnewiterationofthethreeunknownparametersbasedonhowwellitexplainsUWBRADdata.Ourgoalsaretoinvestigatewhetherthepriorscanbeimprovedandthetemperaturecanbeestimated.

The10mtemperaturesareallestimatedwithin±1K,andmostlywithin±0.5Kdespitethepriorestimatebeingpreciseto±1.0K.TheRMSerroroftheUWBRADestimatesareallwithin3.3K;28/47pointsshowimprovementovertheprior.Forthe100maveragedtemperatureestimation,theestimationuncertaintyincreaseswithdepthandstaysbelow1Kuptoabout1500m.Alongtheflightline,aconsistenthighcorrelation,over0.75,betweensurfacetemperatureanddensityvariationisobserved,whichmeansthatmultiplecombinationsofdensityvariationsandsurfacetemperaturesinthesamplespacewouldproducetheexactsameTb.Yetthe10mtemperaturecanstillbewellestimated.TheBayesianframeworkiscapableofconstraintheparameterswithinreasonableregionbytradingoffamongtheparameters.


❄Page30

InvestigatingtheinterplaybetweenwarmwinteranomaliesandglacialmeltingintheArctic:doearlywarmingeventsmatter?

ElizabethDyerandJoanRamage

EarthandEnvironmentalScienceDepartment,LehighUniversity,Bethlehem,PA

Thewinterof2015-2016wasthewarmestwinteronrecord,breakingseveralglobaltemperaturerecords.FromtheendofDecember2015tothebeginningofJanuary2015,manyareasintheRussianHighArctic(RHA)andSvalbardexperiencedtemperaturesabove0°C;precipitationfellasrain.Thesetypesofeventscandisrupttheoverallpatternofaccumulationduringwinterandmeltingduringsummer,andtheyarepredictedtoincreaseinfrequencyduetoclimatechange.ThisstudyexaminestheeffectsofunusualwarmwintereventsonthemeltingandmasslossofglaciersandicecapsinSvalbardandtheRHA,particularlyNovayaZemlya.Theeventsduringwhichairtemperaturewasabovefreezingarestudiedindetail;themaindatasetsaremicrowaveremotesensingobservations,includingtheSpecialSensorMicrowaveImager/Sounder(SSMIS)fromtheNationalSnowandIceDataCenter(NSIDC).Usingthe19and37Ghzchannels,theperiodfollowingthewarmeventsisevaluatedtoseewhereandwhenameltingeventwastriggered,andwhataspectofthestormcausedit.Tounderstandthefulldynamicsoftheresponsestothesewarmevents,themicrowaveobservationsarecomparedwithotherdatasets,includingseasurfacetemperaturefromtheModerate-resolutionImagingSpectrometer(MODIS),andseaiceextentfromtheNSIDC.Anomalouswarmwintereventsareexpectedtohaveanimpactonsubsequentglacialmeltingandnegativemassbalance.


❄Page31

Apictorialhistoryofchangesinpolarscienceandtechnology:anexamplefromglaciermeasurementsonAxelHeibergIsland,Nunavut,Canada,1959-2015

PeterAdams,MilesEcclestone,GrahamCogley

DepartmentofGeography,TrentUniversity,Peterborough,Ontario

Changesinmodesoftransportation,instrumentationaswellasinpersonnelmake-uphavedramaticallychangedthenatureofpolarscienceinthehalfcenturysincetheMcGillexpeditionsbeganresearchonAxelHeibergIsland,Nunavut,Canada,in1959.Thesechangeshaveintensifiedandextendedresearchonglaciersandlakesandtheyhavealsoproducedmarkedchangesinthewaypolarscienceisconducted.Duringthissameperiodtherehavebeenequallydramaticchangesintheglaciersoftheregion.Thesethemesarepresentedherethroughaseriesofannotatedimages.


❄Page32

3rdWintercourseforfieldsnowpackmeasurements:NASASnowWorkingGroup-Remotesensing(iSWGR)

K.Elder1andM.Sturm2

1U.S.DepartmentofAgricultureForestService2UniversityofAlaska,Fairbanks

Asourabilitytocharacterizeandmodelthehydrologicregimeinsnow-dominatedecosystemscontinuestoimprove,thereisaparallelneedtomakemeaningfulandaccuratemeasurementsofsnowpackproperties.Practitionersoftencollectandusefielddatafortheirownpurposes.Modelersandremotesensersoftenobtainthesnowpackdatafromfieldpractitionersorotherresearchers,buthavelittleknowledgeofmeaningorrichnessofthedatatheyareusing.Thiscourseisaimedatteachingskillstopractitionersandmodelersinordertoincreasethequalityoftheresultsforallusers.Thecourseintroducedstudentstostandardandspecialized,quantitativeandqualitative,methodsforthecharacterizationofthesnowpack.

The3rdwintercourseforfieldsnowpackmeasurementsfromtheNASAsnowremotesensinggrouptookplaceonJanuary12-142016attheFraserExperimentalForest,Colorado,USA.Numerousinternationalstudentsparticipatedtotheschoolandlecturersprovidedcoursesonremotesensing,andfieldmeasurementsofvarioussnowproperties.Thesestate-of-the-artsnowremotesensingtechniqueswillbetaughtinthe4thiSWGRsnowschoolwhichisexpectedtooccurinFebruary-March2017.


❄Page33

VerticalstructureandcharacterofprecipitationinthetropicalhighAndesof

southernPeruandnorthernBolivia

JasonL.Endries1,L.BakerPerry1,SandraYuter2,AntonSeimon1,3,MarcosAndrade4,GuidoMamani5,MartiBonshoms6,FernandoVelarde4,RonaldWinkelmann4,NiltonMontoya5,Nelson

Quispe6

1DepartmentofGeographyandPlanning,AppalachianStateUniversity,Boone,NC2DepartmentofMarine,Earth,andAtmosphericSciences,NorthCarolinaState

University,Raleigh,NC3ClimateChangeInstitute,UniversityofMaine,Orono4UniversidadMayordeSanAndres,Bolivia5UniversidadNacionaldeSanAntoniodeAbáddeCusco,Perú6ServicioNacionaldeMeteorologíaeHidrología(SENAMHI),Perú

GlaciersthatprovidecriticalfreshwatertothetropicalhighAndesofsouthernPeruandnorthernBoliviaarecurrentlythreatenedbyrisingtemperaturesandchangingprecipitationpatterns.Inthisstudy,weevaluatetheverticalstructure,character,andmeltinglayerheights(snowlevels)duringprecipitationeventsintheregion..AverticallypointingK-bandMicroRainRadar(MRR)inCusco,Peru(3,350masl)andLaPaz,Bolivia(3,440masl)fromAugust2014toFebruary2015andfromOctober2015tothepresent,respectively,providedcontinuous1-minprofilesofreflectivityandDopplervelocity.Verticaldatawerealsocollectedfromseveralmid-precipitationballoonlaunches,collocatedwiththeLaPazMRR.HourlyobservationsofvariousmeteorologicalvariableswerecollectedfromstationsattheCuscoInternationalAirport(3,350masl)andtheUniversidadMayordeSanAndres(3,440masl),ontheQuelccayaIcecap(5,650masl)andNevadoChacaltaya(5,540masl),andfromMurmuraniAlto(5,050masl).MRRsignaturesrevealabimodalprecipitationpattern,withafternoonconvectiveandnighttimestratiformevents.HourlymedianmeltinglayerheightsoverCusco(LaPaz)rangedfrom4,025(4,115)to5,975(5,990)maslwithanoverallmedianvalueof4,775(4,865)masl.ThemeanechotopheightinCusco(LaPaz)was6,773(7,019)masl,wellabovethealtitudeofsurroundingglaciers.Precipitationprocessesintheregionarethereforelikelytoplayanimportantroleindeterminingglacierbehavior;anincreaseinfuturemeltinglayerheightscouldfurtheraccelerateglacierrecession.


❄Page34

Usingcloudbaseheighttodecreasemisclassifiedprecipitationinhydrological

models

JamesMFeiccabrino

DepartmentofWaterResourcesEngineering,LundUniversity,Sweden

Surfaceair(AT),dew-point(DP)andwet-bulb(WB)temperaturethresholdsareusedinhydrologicalmodelstodetermineifprecipitationisrainorsnow.ItispreferentialtouseATthresholdsduetothewidespreadavailabilityofthedatacomparedtoDPorWB.AT,unlikeDPandWB,doesnottakeintoaccounttheimportantsecondaryroleofhumidityinthemelting,evaporation,andsublimationprocesses.However,theheightofacloudbaseabovethegroundcouldbeusedtogivethedepthofanunsaturatedatmosphericlayerwhichhasmuchdifferentmelting,evaporation,andsublimationratesthanasaturatedcloudlayer.CloudbaseheightcouldthereforebeusedasaproxyforatmospherichumiditywhenusingATthresholds.

Usinghourlyobservationsfrom12manuallyaugmentedmeteorologicalstationsinthemid-westernUnitedStates,surfaceATthresholdsforthefollowingcloudbaseswerefound;0.0°Cforunder100m,0.6°Cfor100and200m,1.1°Cfor300and400m,1.7°Cfor500and600m,and2.2°Cfor700-1000m.ThesecloudheightATthresholdsreducedmisclassifiedprecipitationfromasingleATthreshold(1.1°C)by15%from14.0%to11.9%totalerror.CloudheightATthresholdsresultedina1.5%decreaseintotalerrorfromtheDPthreshold(0.0°C),andwaswithin0.2%oftheWBthreshold(0.6°C).ThisindicatescloudheightATthresholdsmaybeusedinplaceofWBandDPthresholdstoimprovesurfacebasedprecipitationphasecategorizationinhydrologicalmodels.


❄Page35

SeeingandFeelingSnowfromSpace:AUnifiedRadiometricandGravimetric

Approach

BartonA.Forman

DepartmentofCivilandEnvironmentalEngineering,UniversityofMaryland,CollegePark

TheGravityandRecoveryClimateExperiment(GRACE)hasrevolutionizedlarge-scaleremotesensingoftheEarth’shydrologiccycle.However,GRACEisavertically-integratedmeasureofterrestrialwaterstorage(TWS)andprovidesnodirectmechanismforstatingthatagivenportionofTWSisassociatedwithsnow,orthatagivenportionofTWSisassociatedwithsoilmoisture,orthatagivenportionofTWSisassociatedwithgroundwater.ItishypothesizedherethatGRACEinformationcanbeeffectivelydownscaledintoitsconstituentcomponents(e.g.,snow,soilmoisture,groundwater)viaBayesianmergerwithanadvancedlandsurfacemodelaspartofamulti-variate,multi-sensordataassimilationframework.Thisstudyintroducesanovelapproachtomergepassivemicrowave(PMW)measurementsofbrightnesstemperature(Tb)oversnow-coveredterrainwithGRACE-basedgravimetricretrievalsofTWSacrossregionalandcontinentalscales.ThesimultaneousPMWTb+GRACETWSassimilationframeworkwillemploytheNASAGoddardEarthObservingSystemVersion5(GEOS-5)landsurfacemodelandleverageasuiteofmeasurementsfrompastandon-goingsatellitemissions.Asetofboth“synthetic”and“real”experimentshavebeendesignedtoquantifytheaddedutilitytoSWEestimationusingthemulti-sensor,multi-variateassimilationapproach.ItishypothesizedthatthisnewassimilationframeworkwillimproveestimatesofglobalSWEaswellashelpbridgethegapbetweenthetemporalandspatialresolutionsofPMWTbobservationsandGRACE-basedTWSretrievals.


❄Page36

ALandsat-era(1985-2015)SierraNevada(USA)SnowReanalysisDataset

ManuelaGirotto1,StevenA.Margulis2,GonzaloCortés2,LaurieS.Huning2,DongyueLi3,MichaelDurand3

1CryosphericSciencesLaboratory,NASAGoddardSpaceFlightCenter,Greenbelt,MD2DepartmentofCivilandEnvironmentalEngineering,UniversityofCalifornia,Los

Angeles3SchoolofEarthSciencesandByrdPolar&ClimateResearchCenter,TheOhioState

University,Columbus

Thisworkpresentsanewlydevelopedstate-of-the-artsnowwaterequivalent(SWE)reanalysisdatasetovertheSierraNevada(USA)basedontheassimilationofremotelysensedfractionalsnowcoveredareadataovertheLandsat5-8record(1985-2015).Themethod(fullyBayesian),resolution(daily,90-meter),temporalextent(31years),andaccuracyprovideauniquedatasetforinvestigatingsnowprocessestoultimatelyimprovereal-timestreamflowpredictionsofsnow-dominatedregions.ThereanalysisdatasetwasusedtocharacterizeSWEclimatologytoprovideabasicaccountingofthestoredsnowpackwaterintheSierraNevadaoverthelast31years.TheongoingCaliforniadroughtcontainsthelowestsnowpackyears(wateryears2014and2015)andthreeofthefourdriestyearsoverthereanalysisrecord.Inparticular,wateryear2015wasatrulyextreme(dry)year,withrange-widepeaksnowvolumecharacterizedbyareturnperiodofover600years.


❄Page37

ComparisonofMODISandVIIRSsnow-coverproductstostudydata-productcontinuityintheCatskillMountain

watersheds,NewYork

DorothyK.Hall1,AllanFrei2,GeorgeA.Riggs3,NicoloE.Digirolamo3,JamesH.Porter4,andMiguelO.Román5

1UndercontracttoNASAGoddardSpaceFlightCenter,Greenbelt,MD2InstituteforSustainableCities,HunterCollege,CityUniversityOfNewYork,NY3SSAI,Lanham,MD4NYCEnvironmentalProtection,BureauOfWaterSupply,ReservoirOperations,

Grahmsville,NY5TerrestrialInformationSystemsLaboratory,NasaGoddardSpaceFlightCenter,

Greenbelt,MD

RunoffemanatingfromtheCatskillMountainssupplieswatertoapproximatelyninemillionpeopleinNewYorkCityandtoothermunicipalitiesinNewYorkState.TheNYCWaterSupplySystemconsistsofthreesubsystems:theCatskill,theDelaware,andtheCroton.NYCreliesheavilyonthesixbasinsoftheCatskill/Delawaresubsystems:Ashokan,Schoharie,Rondout,Neversink,CannonsvilleandPepacton.ThegoalofthisworkistoinvestigatethecontinuityoftheModerate-resolutionImagingSpectroradiometer(MODIS)andSuomi-NationalPolarPartnership(NPP)VisibleInfraredImagerRadiometerSuite(VIIRS)NASAsnow-coverproductsfordevelopmentofasnow-coverclimate-datarecord(CDR)andtostudysnowmelttiminginconcertwithmeteorologicalandstreamflowdata.WeusethetwotypesofNASAsnowmapstodevelopsnowpackbuild-upanddepletioncurvesforthesixCatskill/Delawarewatershedstoenablecomparisonofresultsofthetwoindependently-createdsnowmaps.TheseincludedailyCollection5MODISstandardsnow-coverproductsat500-mresolution,andthenewNASAVIIRSsnow-coverproductsat375-mresolutionalongwithairtemperature,precipitationandstreamflowdata.Wefocusourevaluationonsimilaritiesanddifferencesinsnow-coverdepletiontiminginthesixCatskill/Delawarewatershedsusingthetwosnow-coverproductsduringtheMODIS-VIIRSoverlapperiodfrom2011–2015,toincludethefourwateryears:2011-12,2012-13,2013-14and2014-15.


❄Page38

EvaluationofAlgorithmAlternativesforBlendedSnowDepthintheIMS

SeanR.Helfrich1,CezarKongoli,LawrenceVulis3,MiltonMartinez4,ChristopherGrassotti2,andNareshDevineni3

1NOAA/NESDIS/OSPO/NIC,Suitland,MD2NOAA/NESDIS/STAR,CollegePark,MD3EnvironmentalEngineering,CityCollegeofNewYork,NewYork4UniversityofPuertoRico,Mayaguez,PR

SinceDecember2014,theInteractiveMultisensorSnowandIceMappingSystem(IMS)hasgeneratedsnowdepthestimatesovertheNorthernHemisphereata4kmresolution.Thealgorithmappliesoptimalinterpolationwithanelevationnudgingtechniquetogenerateasnowdepthoverlocationswithin800kmofthesnowobservingsite.ThisdataisfurtherblendedusingaweightingschemawithpassivemicrowavebasedestimatesfromtheAdvancedTechnologyMicrowaveSounder(ATMS)instrumentandasnowdepthelevationclimatology.Improvementsintheblendedsnowdepthweresoughttoimproveperformance.Severalmethodsweretestedtoimprovesnowdepthestimatesbyrefiningmicrowaveestimateofsnowdepth,promotingapplicationofpriordayestimates,developingregionalsnowdepth/elevationrelationships,alteringthesourceofsnowdepthin-situobservations,andadjustingtheweightingschemabasedonelevationranges.Testingofthesealgorithmenhancementsarepresentedinthispostertodemonstratethemethodologyoftheenhancementsandprovideanevaluationofalgorithmperformancecomparedtothecurrentalgorithmbaseline.


❄Page39

Comparisonofhigh-elevationLiDARsnowmeasurementswithdistributedstreamflow

observations

BrianHenn1,ThomasH.Painter2,BruceMcGurk3,GregStock4,NicoletaCristea1andJessicaD.Lundquist1

1CivilandEnvironmentalEngineering,UniversityofWashington,Seattle2NASA/JPL,Pasadena,CA3McGurkHydrologic4NationalParkService,YosemiteNationalPark

High-elevationspatialandtemporaldistributionsofsnowwaterequivalent(SWE)andprecipitationaredifficulttodetectduetotherelativelysparsecoverageofexistingmeteorologicalstations.AirborneLiDARprovidesremotelysensed,high-resolutionobservationsofsnowdepththatarecapableofresolvingthesepatterns.However,thereareuncertaintiesintheestimationofSWEfromLiDARduetouncertainsnowdensity,theeffectsofforestcanopycoverageonsnowdepthestimatesanduncertainbaselinesinareaswithglaciersandpermanentsnowfields.StreamflowobservationsofferanotherperspectiveonthedistributionsofSWE,asstreamflowintegratesthebasin’ssnowmeltresponse.BycomparingdistributedstreamflowobservationsfrommultiplenestedandadjacentbasinswithLiDAR-basedSWEestimates,wecanidentifyplacesandtimeswherethesetwoestimatesofthebasins’waterbudgetsagreeordisagree.Inthisstudy,weuseLiDARobservationsfromtheNASAAirborneSnowObservatory(ASO)overtheupperTuolumneRiverbasininYosemiteNationalPark,overwateryears2013-2015.Streamflowtimeseriesfrommultiplesub-basinsareavailablefromtheYosemiteHydroclimateNetwork.Foreachsub-basinintheTuolumnedomain,wecompareASOSWEvolumesfromeachLiDARflightwithstreamflowvolumesfortheremainderofthesnowmeltseason.ThisallowsforanevaluationoftheeffectivenessofLiDARSWEestimatesinstreamflowforecasting.Wealsoconsiderhowevapotranspirationandrainfall–basinwaterbalancecomponentsthatarereflectedinstreamflowbutnotinSWEvolumes–influenceskillinsnowmelt-drivenstreamflowforecasting.


❄Page40

Evaluating50yearsoftropicalPeruvianglaciervolumechangefrommulti-temporaldigitalelevationmodels(DEMs)andglacierflowandhydrologyintheCordilleraBlanca,

Peru

KyungInHuh1,BryanG.Mark2,MicheleBaraer3,YushinAhn4,ChrisHopkinson5

1DepartmentofGeographyandAnthropology,CaliforniaStatePolytechnicUniversity,Pomona

2DepartmentofGeography,TheOhioStateUniversity,Columbus3Départementdegéniedelaconstruction,Écoledetechnologiesupérieure(ÉTS),

Montréal,Québec4SchoolofTechnology,MichiganTechnologicalUniversity,Houghton5DepartmentofGeography,UniversityofLethbridge,Water&Environmental,Alberta

Althoughfarsmallerthanlargepolaricecaps,mountainglaciersaresignificantcontributorstosealevelriseandtropicalglaciersinparticulararesourcesofcriticalwaterresourcestoregionalsocieties.TheglaciersinCordilleraBlanca,Peru,haveenvironmentalandeconomicimportanceasregionalwatersuppliestocommunitiesinthearidwesternpartofthecountryundercontinuedglobalclimatechange.

WequantifyglaciervolumechangeintheCordilleraBlancabyintercomparingdigitalsurfaceelevationsderivedfromthreesourcesofremotelysensedimagedataspanningalmost50years:ASTER(AdvancedSpaceborneThermalEmissionandReflectionRadiometer,2000-08);airborneLiDAR(LightDetectionandRanging,2008);andstereoaerialphotography(1962).WecharacterizethelimitationsinherentinprocessinghistoricaerialphotographywithdifferentviewinggeometriesoverhighlyruggedterrainreliefanduncertaintiesintheprocessingstageaswellasDEMcomparisonbyanalyzingDEMovernon-glacierizedterrain.WeconfirmvolumechangesfrompreviousstudiesintheCordilleraBlancaandextendtemporalresolutionintimeseriesbyaddingthefirstacquisitionofhigh-resolutionairborneLiDARachievedin2008.

WeassessthehistoricalcontributionofglaciericevolumelosstostreamflowbasedonreconstructedvolumechangesthroughLittleIceAge(LIA)canbedirectlyrelatedtotheunderstandingofglacier-hydrologyinthecurrentepochofrapidglaciericelossthathasdisquietingimplicationsforwaterresourcesintheCordilleraBlancaofthePeruvianAndes.WecomputetherateandmagnitudeofglaciervolumechangesforYanamareyandQueshque


❄Page41

glaciersbetweentheLIAandmoderndefinedby2011ASTERGlobalDigitalElevationModelVersion2(GDEMV2)fromtheCordilleraBlanca.


❄Page42

Evaluationofsatellite-basedobservationsforcapturingearlywintersnowmeltwithin

mid-latitudebasins

AdamHunsaker2,CarrieM.Vuyovich1,DouglasOsborne2,JenniferM.Jacobs2

1ColdRegionsResearchandEngineeringLaboratory,Hanover,NH

2UniversityofNewHampshire,Durham,NH

Overthepastfiftyyearsglobalclimatechangehasalteredvariousenvironmentalprocesses.Duetoglobalclimatechangeearlysnowmeltisoccurringmuchmorefrequentlythroughoutmuchoftheworld(Semmens,Ramage,Bartsch,&Liston,2013).Theincreasingfrequencyoftheseeventsisarelativelynewphenomenaanditischallengingtheeffectivenessofcurrentwaterresourcemanagementandfloodforecastingbestpractices.Earlysnowmelteventsarecausedbyabriefperiodofunusuallyhighairtemperature,highhumidity,orrain-on-snow(Semmens,Ramage,Bartsch,&Liston,2013).Thisresearchfocusesonthedetectionanddistributionofrain-on-snoweventsusingremotesensingapproachestoidentifyandquantifythefrequency,extentandmagnitudeofearlymeltevents.TheanalysishighlightsseveralrecentfloodeventsoccurringinNorthAmerica.Earlymeltevents,drivenbyheavyrainfallwiththepresenceofsnow,areidentifiedfromtheDartmouthFloodObservatoryarchives.PassivemicrowavedatafromtheAMSR-EandSSMIinstrumentsarecomparedwithMODISimageryandfieldobservationstoassessthemicrowaveproducts’reliabilityincapturingtheseevents.Earlymeltdetectionalgorithmsthatusepassivemicrowaveretrievalsfornorthernlatitudeareas,primarilyCanadawereevaluatedinthecontinentalUnitedStates.Thesealgorithmsfailedtocapturemidlatitudeearlysnowmelteventsprimarilyduetoclimatologicaldifferencesbetweennorthernandmidlatitudeareas.Thisresearchdevelopedanalternative,morereliablealgorithmusingthepassivemicrowavesignaturethatreflectstheinherentcharacteristicsofmidlatituderain-on-snowevents.Thetwoalgorithmsareusedtocomparetheirrelativevaluefordetectingmidlatituderain-on-snoweventsascomparedtonorthernlatitudeeventsforseveraldifferentfrequencyandlinkingperformancetoclimatologicalsignaturesofobservedrain-on-snowevents.


❄Page43

TheGCOM-W1Satellite-basedMicrowaveSnowAlgorithm(SMSA)

RichardKelly,NastaranSaberiandQinghuanLi

InterdisciplinaryCentreonClimateChangeandDepartmentofGeographyandEnvironmentManagement,UniversityofWaterloo,Waterloo,ON

TheSatellite-basedMicrowaveSnowAlgorithm(SMSA)forestimatingsnowdepth(SD)andsnowwaterequivalent(SWE)isdescribed.CalibratedforusewiththeAdvancedMicrowaveScanningRadiometer–2(AMSR2)aboardtheGlobalChangeObservationMission–Water,theSMSAstandardSDproductforAMSR2hasbeenupdatedintwoways,fromtheexistingalgorithm.First,thedetectionalgorithmscreensvariousnon-snowsurfacetargets(waterbodies[includingfreeze/thawstate],rainfall,highaltitudeplateauregions[e.g.Tibetanplateau])beforedetectingmoderateandshallowsnow.Second,theimplementationoftheDenseMediaRadiativeTransfermodel(DMRT)originallydevelopedbyTsangetal.(2000)andmorerecentlyadaptedbyPicardetal.(2011)isusedtoestimateSWEandSD.TheimplementationcombinesaparsimonioussnowgrainsizeanddensityapproachoriginallydevelopedbyKellyetal.(2003).Snowgrainsizeisestimatedfromthetrackingofestimatedairtemperaturesthatareusedtodriveanempiricalgraingrowthmodel.SnowdensityisestimatedfromtheSturmetal.(2010)scheme.Resultsarepresentedfromrecentwinterseasonssince2012toillustratetheperformanceofthenewapproachincomparisonwiththeinitialAMSR2algorithm.


❄Page44

TheNASASnowExairbornesnowcampaign

EdwardKim1,CharlesGatebe1,DorothyHall1,MatthewSturm2andmanyothers

1NASAGoddardSpaceFlightCenter,Greenbelt,MD

2UniversityofAlaska,Fairbanks

NASAisplanningamulti-yearairbornesnowcampaigncalled“SnowEx,”beginningthenorthernhemispherewintersof2016-2017.TheprimarygoalofSnowExYear1isthecollectionofcoincidentobservationswithasuiteofsensortypesincludingactiveandpassiveopticalandactiveandpassivemicrowavesensors.Detailedgroundtruthwillalsobecollectedforalgorithmdevelopment.

TheobjectiveofthispresentationistoupdatethesnowcommunityonSnowExYear1plans,andtoprovideanopportunityforcommunityinputtohelpdesignthecampaigntowardtheultimategoalofdefiningfutureglobal-scalesnowsatellitemeasurementsystems.


❄Page45

Spectralanalysisofairbornepassivemicrowavemeasurementsforclassification

ofalpinesnowpack

RhaeSungKimandMichaelDurand

SchoolofEarthSciencesandByrdPolar&ClimateResearchCenter,TheOhioStateUniversity,Columbus

Passivemicrowavemeasurementshavebeenwidelyusedandinvestedinordertoobtaininformationaboutsnowpackproperties.Accurateknowledgeandunderstandingthesignaturesofthisremotesensingdatafromlandsurfacesarecriticaltostudysnowdistributionoveralpinemountainousarea.However,thistaskoftenambiguousduetothelargevariabilityofphysicalconditionsandsurfaceobjecttypes.Basedontheliterature,itwashypothesizedthatsnowdepth,forestfraction,andliquidwaterwouldresultindistinctmicrowavespectra.Inthisstudy,wediscussandanalyzethespectraofmeasuredbrightnesstemperatures(Tb)andemissivitiesforthefrequencyrangeof10.7to89GHz.100mresolutionoftheMultibandpolarimetricScanningRadiometer(PSR)imagerywasusedoverNASAColdLandProcessesFieldExperiment(CLPX)studyareawithground-basedmeasurementsofsnowdepthandwetnessinformation.Atotalof900gridcells,eachonehectareinsizewereanalyzed,utilizingbothatotalof144snowpitsandatotalof900snowdepthtransects.Inaddition,twoobservationtimesinFebruary2003andMarch2003wereconsideredfornormalwintersnowpackandspringsnowmelt.VegetationinterfereswiththesignalthatwasreceivedbyPSRandtherefore,NLCD2001percenttreecanopydatasetwasusedforconsideringthevegetationinfluence.Snowclasseswithdifferentsnowdepthandwetnessconditionswerecreatedtodeterminewhethermicrowavespectrabearone-to-onecorrespondencewithsnowandlandscapepropertiestoenablesnowclassification.StatisticaltestsshowthatsnowdepthcanbedistinguishedevenwhenthepixelsarevegetatedwhenusingallPMfrequenciesinsteadofusingsingle37GHzfrequency.Inaddition,emissivityspectraandTbspectrawerequalitativelysimilar,thisenablingustoanalyzetheTbspectra.Supervisedclassificationschemewithusingderivedsnowclassesfromthisanalysiswillbeusedtoclassifyalpinesnowpackundervariousconditions.


❄Page46

LargeprecipitationeventsatSNOTELsitesandstreamflowvariabilityintheUpper

ColoradoRiverBasin

JohnathanKirk

DepartmentofGeography,KentStateUniversity,Kent,OH

DecliningannualmountainsnowpackacrossthewesternUnitedStatesisplacingunprecedentedstrainsonregionalwatersupplies.Furthercomplicatingseasonalwatersupplyforecastingistheemergingprospectthatinterannualvariationinalpinesnowconditionsisgreatlyinfluencedbytheoccurrenceandmagnitudeoflargeprecipitationevents(LPEs)eachyear.TheoccurrenceofLPEscandictatewhetherayearproducesaboveorbelowaveragerunoff,underscoringtheneedformoretargetedinvestigation.

Usingobservationalprecipitationdatarecordedatasampleofsnowtelemetry(SNOTEL)monitoringstationslocatedamongsignificantrunoff-producingheadwaterregionsoftheUpperColoradoRiverBasin(UCRB)inColoradoandWyomingfrom1981-2014;thisstudydefines“largeprecipitationevents”andexaminestheirrelativeinfluenceonyearlystreamflowandreservoirinflow,asmeasuredthroughouttheUCRB.ResultsindicatethatinterannualprecipitationvariabilityattheSNOTELsitesissignificantlycorrelatedwithstreamflowvariability,asarethefrequencyandmagnitudeofLPEs.

Thisstudythenincorporatesasynopticclassificationofmid-troposphericcirculationpatternsassociatedwithLPEstoinvestigatepotentialpredictivesignals.ResultssuggestthatalatitudinalvariationexistsinthetypesofcirculationpatternswhichcoincidewithLPEsbetweenheadwaterregions,reinforcinganecdotalknowledgeofthevariablelocalresponsesattheSNOTELsitestosynoptic-scaleforcings.Suchrelationships,inadditiontotheoverallcharacteristicsofLPEsintheUCRB,maybefurtherintegratedintoactionableimprovementstowardsmoreaccurateandrepresentativeseasonalwatersupplyforecasts.


❄Page47

DailysnowdepthatPalmerStation,Antarctica,2007-2014:aninitialanalysis

AndrewG.Klein

DepartmentofGeography,TexasA&MUniversity,CollegeStation

DailysnowdepthmeasurementmadeatPalmerStation,Antarctica,areavailablebeginninginDecember2006.Thestation’ssnowmeasurementboardiscurrentlylocatedjustoffaboardwalksurroundingthemainstationbuildings.BecauseitisnotpositionedasrecommendedbytheNationalWeatherServicedefiniteerrorsareevidentinthetimeseries.However,thesemeasurementsdoallowdetailedanalysisofsnowaccumulationpatternsatPalmerStationforthe2007-2014period.SnowdepthsfromJanuarytoearlytomid-Apriltoearly/midMayaretypicallylessthan10cmwithmanydaysbeingsnowfree.SnowdepthstypicallyincreaseirregularlyovertheaustralwinterreachingmaximumthicknessfromlateSeptembertothefirstweekofNovember.Considerablyvariabilityexistsinthisrelativelyshortrecordin(1)maximumsnowdepths,(2)thedateofmaximumaccumulationand(3)thefirstsnowfreedayinsummer.Maximumannualsnowdepthsvarybyafactoroftworangingfrom55to109cm.Inlowaccumulationyears(maximumdepthlessthan90cm),thedateofmaximumdepthoccursfrommid-AugusttothelastweekinSeptemberandthestationbecomessnowfreebyNovember23rd.Inhighaccumulationyears(maximumdepthinexcessof90cm),thedateofmaximumaccumulationisdelayedfromearlyOctobertoearlyNovemberandsnowpersistsintoDecember.TobetterunderstandtheclimaticcontrolsonsnowdepthatPalmerStation,thissnowaccumulationrecordwillbeanalyzedinrelationtoothermeteorologicalvariableswhicharerecordedatPalmerStationat2minuteintervals.ThistimeserieswillalsobecomparedtosnowobservationsmadeatotherscientificstationsalongtheWesternAntarcticPeninsula.TheworkisthefirststepinbetterunderstandingpatternsandpersistenceofsnowcovernearPalmerStationanditspossibleinfluencesonthespatialdistributionoflocalflora.


❄Page48

Canassimilationofmicrowaveradiancedataimprovecontinental-scalesnowwater

storageestimates?

YonghwanKwon1,Zong-LiangYang1,LongZhao1,TimothyJ.Hoar2,AllyM.Toure3,andMatthewRodell3

1DepartmentofGeologicalSciences,JacksonSchoolofGeosciences,TheUniversityof

TexasatAustin2NationalCenterforAtmosphericResearch,Boulder,CO3HydrologicalScienceslaboratory,NASAGoddardSpaceFlightCenter,Greenbelt,MD

Understandingspatialandtemporalvariationsinsnowpackiscrucialforclimatestudiesandwaterresourcemanagement.Towardsthisgoal,theclimateandhydrologicalresearchcommunitieshavebeenworkingtogethertoimprovelarge-scalesnowestimates.Thisstudyaimstoaddressthefeasibilityofusingmicrowaveradianceassimilation(RA)methodstoestimatecontinental-scalesnowwaterstorage.TheRAsystemusedinthisstudyiscomprisedoftheCommunityLandModelversion4(CLM4)(forsnowenergyandmassbalancemodeling),radiativetransfermodels(RTMs)(forbrightnesstemperature(TB)estimates),andtheDataAssimilationResearchTestbed(DART)(forensemble-baseddataassimilation).TwosnowpackRTMs,theMicrowaveEmissionModelforLayeredSnowpacks(MEMLS)andtheDenseMediaRadiativeTransfer–MultiLayersmodel(DMRT-ML),areusedtosimulatethesnowpackTB.Itishypothesizedthatthecontinental-scaleRAperformanceinestimatingsnowwaterstoragecanbeimprovedbysimultaneouslyupdatingallmodelphysicalstatesandparametersdeterminingTBusingarule-basedapproach,inwhichpriorestimatesareupdateddependingontheircorrelationswithapriorTB.ThishypothesishasbeentestedthroughanalysisofresultsfromaseriesofRAexperiments.OurresultsalsoshowthattheperformanceoftheRAsystemcanbeimprovedfurther,especiallyforvegetatedareas,byassimilatingthebest-performingfrequencychannels(i.e.,18.7and23.8GHz)andbyconsideringthevegetationsinglescatteringalbedotorepresentthevegetationeffectonTBatthetopoftheatmosphere.


❄Page49

Rain-on-snowandicelayerformationdetectionusingpassivemicrowaveradiometry:Anarcticperspective

A.Langlois1,2,B.Montpetit1,C.Dolant1,2,L.Brucker3,F.Ouellet1,2,C.A.Johnson4,A.Richards5,A.Roy1,2,andA.Royer1,2

1Centred’ApplicationsetdeRecherchesenTélédétection(CARTEL),UniversitédeSherbrooke,Quebec

2Centred’étudenordiques,Quebec3NASAGoddardSpaceFlightCenter,CryosphericSciencesLaboratory,Greenbelt,MD4CanadianWildlifeService,EnvironmentCanada,Ottawa,ON5ClimateResearchDivision,EnvironmentCanada,Toronto,ON

WiththecurrentchangesobservedintheArctic,anincreaseinoccurrenceofrain-on-snow(ROS)eventshasbeenreportedintheArctic(land)overthepastfewdecades.Severalstudieshaveestablishedthatstronglinkagesbetweensurfacetemperaturesandpassivemicrowavesdoexist,butthecontributionofsnowpropertiesunderwinterextremeeventssuchasrain-on-snowevents(ROS)andassociatedicelayerformationneedtobebetterunderstoodthatbothhaveasignificantimpactonecosystemprocesses.Inparticular,icelayerformationisknowntoaffectthesurvivalofungulatesbyblockingtheiraccesstofood.Giventhecurrentpronouncedwarminginnorthernregions,morefrequentROScanbeexpected.However,oneofthemainchallengesinthestudyofROSinnorthernregionsisthelackofmeteorologicalinformationandin-situmeasurements.TheretrievalofROSoccurrenceintheArcticusingsatelliteremotesensingtoolsthusrepresentsthemostviableapproach.

Here,wepresenthereresultsfrom1)ROSoccurrenceformationinthePearycaribouhabitatusinganempiricallydevelopedROSalgorithmbyourgroupbasedonthegradientratio,2)icelayerformationacrossthesameareausingasemi-empiricaldetectionapproachbasedonthepolarizationratiospanningbetween1978and2013.Adetectionthresholdwasadjustedgiventheplatformused(SMMR,SSM/IandAMSR-E),andinitialresultssuggesthigh-occurrenceyearsas:1981-1982,1992-1993;1994-1995;1999-2000;2001-2002;2002-2003;2003-2004;2006-2007;2007-2008.AtrendinoccurrenceforBanksIslandandNWVictoriaIslandandlinkagestocariboupopulationispresented.


❄Page50

EstimatingsnowwaterequivalentinamountainousSierraNevadawatershed

withspaceborneradiancedataassimilation

DongyueLi1,MichaelDurand1,StevenA.Margulis2

1SchoolofEarthSciencesandByrdPolar&ClimateResearchCenter,TheOhioState

University,Columbus2DepartmentofCivilandEnvironmentalEngineering,UniversityofCaliforniaLos

Angeles

GiventhecriticalroleoftheSierraNevadamountainsnowinthewatersupplyandtheecologicalsysteminthewesternU.S.,beingabletoimprovetheestimateofsnowwaterequivalent(SWE)intheSierraNevadahassocietalandnaturalmerit.Inthisstudy,wedemonstratetheaccurateretrievalofSWEfromspacebornepassivemicrowavemeasurementsforthesparselyforestedUpperKernwatershed(511km2)inthesouthernSierraNevada.ThisisaccomplishedbyassimilatingAMSR-E36.5GHzmeasurementsintomodelpredictionsofSWEat90-mspatialresolutionusingtheEnsembleBatchSmoother(EnBS)dataassimilationframework.Foreachwateryear(WY)from2003to2008,SWEwasestimatedfortheaccumulationseason,fromOctober1sttoApril1standvalidatedagainstsnowcoursesandsnowpillows.Onaverage,theEnBSaccumulationseasonSWERMSEwas77.4mm,despiteaveragepeakSWEof~556mm;thepriormodelestimatewithoutassimilationhadanaccumulationseasonaverageRMSEof119.7mm.Afterassimilation,theoverallbiasoftheaccumulationseasonSWEestimateswasreducedby84.2%,andtheirRMSEreducedby35.4%.TheassimilationalsoreducedthebiasandtheRMSEoftheApril1stSWEestimatesby80.9%and45.4%,respectively.Sensitivityexperimentsindicatedoptimalresultswhentherawobservationsareassimilated,ratherthanfirstaveragingoverthewatershed.Thismethodisexpectedtoworkwellabovetreeline,andfordrysnow.


❄Page51

HowmuchwesternUnitedStatesstreamfloworiginatesassnow?

DongyueLi1,MelissaWrzesien1,MichaelDurand1,JenniferAdam2,DennisLettenmaier3

1SchoolofEarthSciencesandByrdPolar&ClimateResearchCenter,TheOhioState

University2DepartmentofCivilandEnvironmentalEngineering,WashingtonStateUniversity3DepartmentofGeography,UniversityofCalifornia,LosAngeles

SnowisavitalcomponentofthewatersupplyinthewesternUnitedState.Quantifyingthefractionofstreamflowthatoriginatesassnowiscriticalforassessingtheavailabilityandvulnerabilityofwaterresources,particularlyinachangingclimate.Althoughmanyestimatesofthisfundamentalquantityhavebeensuggested,noneofthem(toourknowledge)hasbeenbaseduponasystematicstudy.Here,weexaminetheratioofthesnow-derivedstreamflowtothetotalstreamflowoverthewesternUnitedStatesfortheperiodof1950to2100.Byusinganewmethodfortracingsnowmeltfatewithinamacroscalehydrologicalmodel,weshowthatsnowaccountsfor53%ofthetotalstreamflowinthewesternUnitedStates,despiteonly37%ofthetotalprecipitationbeingsnowfall.Inthemountainrangesofthewest,71%ofthestreamflowcomesfromsnow,andthesnowmeltcharges66%ofthemajorreservoirsinthewesternUnitedStates;suchreservoirstorageiscriticaltomeetthepeakwaterdemandsinthesummerandfall.Further,wedemonstratethatthecontributionofsnowmelttostreamflowwilllikelydecreaseinawarmerclimate,especiallyintheCascadesandtheSierraNevadawheretheratiocoulddeclineby33%by2100incomparisonwiththehistoricalrecord.


❄Page52

Terrestriallaserscanningobservationsoftreecanopyinterceptedsnow

QinghuanLi,RichardKelly

DepartmentofGeographyandEnvironmentalManagement,UniversityofWaterloo,ON

Thedistributionofsnowinforestcanopiesisimportantforboththewatermassandenergybudgetsofforestedenvironments.Snowaccumulationinforestcanopiescanbesignificantforthetreewaterdemandwhilstcanopysnowcanalsoactasabuffertotheunderstorywiththrough-fallduringthewinterseasonoccurringsporadically.Moreover,significantamountsofwaterequivalentcanalsobelostthroughsublimationfromthecanopysnow.

Understandingcanopysnowdynamicsisimportantforunderstandingforesthydrologybutalsoforunderstandingtheremotesensingresponseofforestcanopies,especiallyatmicrowavewavelengthswhicharesensitivetoforestcanopyvolumescatteringprocesses.Theoverallgoalofthestudywastoestimatethesnowvolumeinterceptedinaconiferouscanopyusingaterrestriallaserscanner(TLS).ThestudywasperformedontwoconiferoustreesinsouthernOntarioontheUniversityofWaterloocampus.Thelaserscanner,aLeicaMS50multi-station,wasusedtoscanthetreewhensnowwaspresentandthenwhensnowwasremoved.Snowpropertiesinthecanopyandonthegroundwereevaluatedusingtraditionalmeasurementsofgrainsizeandbulkproperties.ThepaperdemonstratestheutilityofhighresolutionTLSandshowshowthesimplicityoftime-differencingTLSmeasurementapproachesarecomplicatedbytheneedtoaccountforthemechanicsofsnowloadingandunloadingwhichareafunctionofthetreebiophysicalproperties(e.g.elasticity).


❄Page53

DevelopmentofUniversalRelationshipsbetweenSnowDepth,SnowCoveredAreaandTerrainRoughnessfromNASAAirborne

SnowObservatorydata

NoahMolotchandDominikSchneider

DepartmentofGeography,INSTAAR,andCWEST,UniversityofColoradoBoulder

SnowmeltistheprimarywatersourceintheWesternUnitedStatesandmountainousregionsglobally.Forecastsofstreamflowandwatersupplyrelyheavilyonsnowmeasurementsfromsparseobservationnetworksthatmaynotprovideadequateinformationduringabnormalclimaticconditions.UsingobservationsLiDARandHyperspectralobservationsfromtheNASAAirborneSnowObservatory,wehavedevelopedtransferablefunctionalrelationshipsbetweenterrainroughness,snowcoveredarea,andsnowdepth.Weshowthattherelationshipbetweensnowcoveredareaandsnowdepthvariessystematicallyasafunctionofterrainroughness.Regressionanalysesthatusefractionalsnowcoveredastheindependentvariabletoestimatesnowdepthresultinrelativemeansquarederrorsbetween39%and58%ofmeasuredsnowdepthfordifferentroughnessclassifications.Futureworkwilllookatthechangesintherelationshipbetweensnowdepthandsnowcoveredareathroughtheablationseasontodeterminetherelationship’sutilitytowatersupplyforecasting.Theimportanceofthisworkisillustratedthroughexamplesthatestimatesnowdepthforselectalpineregions.


❄Page54

ElevationAngularDependenceofWidebandAutocorrelationRadiometric

(WiBAR)RemoteSensingofDrySnowpackandLakeIcepack

SeyedmohammadMousavi1,RogerDeRoo2,KamalSarabandi1,andAnthonyW.England3

1ElectricalEngineeringandComputerScienceDepartment,UniversityofMichigan,AnnArbor

2ClimateandSpaceSciencesandEngineeringDepartment,UniversityofMichigan,AnnArbor

3CollegeofEngineeringandComputerScience,UniversityofMichigan,Dearborn

Inmostremotesensingapplications,thegrossparameterofthetarget,suchassnowdepthandsnowwaterequivalent(SWE),areoftentheparametersofinterest.Anovelandrecentlydevelopedmicrowaveradiometrictechnique,knownaswidebandautocorrelationradiometry(WiBAR),offersadeterministicmethodtoremotelysensethemicrowavepropagationtimeofmulti-pathmicrowaveemissionoflowlossterraincoversandotherlayeredsurfacessuchasdrysnowpackandfreshwaterlakeicepack.Themicrowavepropagationtimethroughthepackyieldsameasureofitsverticalextent;thus,thistechniqueisadirectmeasurementofdepth.Thistechniqueisinherentlylow-powersincethereisnotransmitterasopposedtoactiveremotesensingtechniques.Italsoworksatanglesawayfromnadir.

Wehaveconfirmedtheexpectedsimpledependenceofthemicrowavepropagationtimeontheelevationanglewithground-basedWiBARmeasurementsoftheicepackonDouglasLakeinMichiganinearlyMarch2016.TheobservationsaredoneintheX-bandfortheicepack.Atthesefrequencies,thevolumeandsurfacescatteringaresmallinthepack.ThesystemdesignparametersandphysicsofoperationoftheWiBARisdiscussedanditisshownthatthemicrowavepropagationtimecanbereadilymeasuredfordrysnowpackandlakeicepackforobservationanglesawayfromnadirtoatleast70◦.


❄Page55

FormulationofaBayesianSWEretrievalalgorithmusingX-andKu-measurements

JinmeiPan,MichaelDurand

SchoolofEarthScienceandByrdPolar&ClimateResearchCenter,TheOhioStateUniversity,Columbus

Whenthesnowradarwasappliedforthesnowwaterequivalentretrieval,anadvancedalgorithmisrequiredtoseparatetheinfluenceoftheunderlyingsoil,andtakingthepenetrationdepthandthestratigraphyofthenaturalsnowpitintoconsideration.Inthisstudy,theBayesian-basedMarkovChainMonteCarlomethodisappliedtoestimateSWEbasedonactivebackscatteringcoefficientmeasurementsatX-andKu-bandsfortaigasnowpitsatSodankyla(Lemmentyinenetal.,2013).ThisalgorithmsamplestheSWEaswellasthesnowandsoilpropertiesthatcanreproducetheradarmeasurementsfromasetofglobally-availablepriordistributionsoftheseparameters.TheactiveMicrowaveEmissionModelofLayeredSnowpacks(MEMLS)convertedfromthepassiveMEMLSisusedastheobservationmodel.Thismodelseparatedtheequivalentreflectivity(1-emissivitiy)atthesnowsurfaceintoaspecularscatteringpartandadiffusescatteringpart,andlatersemi-empiricallyconvertedthemintothecorrespondingcontributionstothebackscatteringcoefficient.Therefore,thecomputationcostofactiveMEMLSissimilartopassiveMEMLS,andthusissuitablefortheMCMCapplication.BasedonpreviousMCMCretrievalstudiesusingpassivebrightnesstemperature(TB)asobservations,atthistime,theobservationmodelwillberevisedasactiveMEMLSforSWEestimation,andtheretrievalsystemwillbeformulated.BesidestheparametersalreadyincludedinpassiveMEMLS,theactiveMEMLSintroducedthreeempiricalparameters,whicharethecoefficienttosplitthecross-andlike-pol.backscatteringcoefficients,theratioofthespecularpartintheroughsoilreflectivity,andtheroughnessoftheair-snowinterface.HowtheseadditionalparameterswillinfluencetheMCMCretrievalperformancewillbestudied.


❄Page56

In-situLightEmittingDiodeDetectionandRangingfortheMappingofSnowSurface

TopographyandDepth

N.ReedParsons,ChristopherHopkinson

DepartmentofGeography,UniversityofLethbridge,AB

TheWestCastlecatchmentstudysite,amountainoussub-basinoftheOldmanRiverBasin,isavitalhydrologicalresourceaswellasanequallyecologicallyandgeomorphologicallydiverseregioninsouthwestAlberta.TheARTeMiSResearchTeamhaveinstalledthreemeteorologicalstationsatthreeelevations:valley(1415mASL);treeline(1850mASL);andalpineridge(2130mASL)withintheboundariesoftheWestCastleMountainSkiResort.Currentacceptedmethodsofin-situsnowdepthmonitoring,suchasultrasonicrangedetectionsensors,areonlycapableofmeasuringanaverageaccumulationoverasmallfootprintleavingsnowsurfaceprofilemappingtobeconductedmanually.Furthermore,inareasinwhichtheprimarysnowtransportationprocessisaeolian,thedepositionalanderosionalfeaturesarenotaccuratelyestimated.Thus,underthecurrentlyacceptedin-situsnowdepthmeasurementregime,theresultsareoftenoverorunderestimated.LeveragingtheMeteorologicaltowerinfrastructure,aconventionalSR50Asonicrangingdepthsenorisco-locatedwithaLightEmittingDiodeDetectionandRanging(LEDDAR)solutionprovidedbyCanadiantechstart-up,LeddarTech.InthisstudywemapsnowaccumulationandsnowsurfacetopographyusingLEDDAR,andcomparetheaccuracy,precision,andsusceptibilitytoextremealpineconditionstothatoftheSR50A.


❄Page57

MeltontheMargins:CalibratedEnhanced-ResolutionBrightnessTemperaturesto

MapMeltOnsetNearGlacierMarginsandTransitionZones

JoanRamage,1MaryJ.Brodzik2andMollyHardman2

1EarthandEnvironmentalSciencesDepartment,LehighUniversity,Bethlehem,PA2UniversityofColorado/NSIDC/CIRES,Boulder

Passivemicrowave(PM)observationsfromSpecialSensorMicrowaveImager/Sounder(SSMIandSSMIS),andAdvancedMicrowaveScanningRadiometerforEOS(AMSR-E)at18-19GHzand36-37GHzchannelshavebeenimportantsourcesofinformationaboutsnowmeltstatusinglacialenvironments,particularlyathigherlatitudes.PMdataaresensitivetothechangesinnear-surfaceliquidwaterthataccompanymeltonset,meltintensification,andrefreezing.Overpassesarefrequentenoughthatinmostareasmultiple(2-8)observationsperdayarepossible,yieldingthepotentialfordeterminingthedynamicstateofthesnowpackduringtransitionseasons.Limitationstothisapproachincludeglacier-marginalzoneswherepixelsmaybeonlyfractionallysnow/icecovered,andareaswheretheglacierisnearlargebodiesofwater:evensmallregionsofopenwaterinapixelseverelyimpactthemicrowavesignal.Weusetheenhanced-resolutionprototypeCalibratedPassiveMicrowaveDailyEASE-Grid2.0BrightnessTemperatureEarthSystemDataRecord(CETB)producttoevaluatemeltcharacteristicsalongglaciermarginsandmeltzoneboundariesduringthemeltseasonsin2003-2004fortheAlaskanCoastRangeandAkademiiNaukIceCap,SevernayaZemlya,locationswherelegacymethodsweresuccessfulthatspanawiderangeofmeltscenarios.Sitesincludepixelsthatwerepreviouslyexcludedduetomixedpixeleffects.Weanticipatethatimprovementfromtheoriginal25km-scaleEASE-Gridpixelstotheenhancedresolutionof6.25kmwilldramaticallyimprovetheabilitytoevaluatemelttimingacrossgradientsinglaciermarginsandtransitionzonesinglacialenvironments.


❄Page58

StatusoftheMODISC6SnowCoverandNASASuomi-NPPVIIRSSnowCoverData

Products

GeorgeA.Riggs1,DorothyK.Hall2andMiguelO.Román2

1SSai,Lanham,MD2NASAGoddardSpaceFlightCenter,Greenbelt,MD

Anupdatedsynopsisofthesoon-to-be-releasedNASASuomi-NPP(S-NPP)VisibleInfraredImagerRadiometerSuite(VIIRS)snowcoverdataproductsproducedintheLandScienceInvestigator-ledProcessingSystem(LSIPS)andtherecentlyreleasedMODISCollection6(C6)dataproductsispresented.TheVIIRSsnowcoveralgorithmanddataproductcontentarethesameaspresentedatthe72ndESChoweverthedataproductformathaschangedtoHDF5andNetCDFClimateForecast(CF)conventionshavebeenadoptedfortheattributes.ForwardprocessingandreprocessingoftheMODISC6dataproductsbeganinApril2016andproductshavebeenreleased.NotablerevisionsmadeintheMODISC6snowcoveralgorithmarethechangetonormalizeddifferencesnowindex(NDSI)outputsreplacingthethematicandthefractionalsnowcovermaps,changesindatascreenstoreducesnowcommissionerrorsandoutputofaqualityassessmentarrayofbitflagsreportingdatascreenresults.UsersthushaveincreaseddataandinformationcontentascomparedtoMODISC5products.


❄Page59

50YearsofSatelliteSnowCoverExtent

MappingOverNorthernHemisphereLands

DavidA.Robinson

GlobalSnowLab,DepartmentofGeography,RutgersUniversity

Thisfallmarksahalf-centuryofcontinuoussatellitemappingofsnowcoverextent(SCE)overNorthernHemispherelands.NOAAhasproducedtheprimarydatasetthroughoutthistime,recentlyincooperationwiththeUSNavyandCoastGuardattheNationalIceCenter.Throughoutthe50years,trainedanalystshaveprimarilyemployedvisiblesatelliteimageryandinteractivemeansofmappingtheSCEonaweekly(1966-1999)anddaily(1999-present)basis.Thedatasethasbeencarefullyevaluatedovertheyearstoensurethebestpossiblecontinuityinwhathasemergedasaprimarysatelliteclimatedatarecord(CDR).Infact,thisCDRisthelongest,continuoussatellite-derivedenvironmentalrecordinexistence.Thispresentationwilldiscussthehistoryofthemappingprogram,trendsandvariabilityinSCEoverthedecadesgleanedfromthemaps,andtheutilizationofthisCDRinnumerousclimatestudies.SpecialattentionwillbepaidtoeasternNorthAmerica.Thiswillincludethefirstpresentationofashort-termclimatology(1999-present)basedonthe24kmresolutionInteractiveMultisensorSnowandIceMappingSystemproduct.Acomparisonofthisproductoverthecoarserspatialresolutiononethatextendsbackto1966willbeincludedinthediscussion.


❄Page60

Comparisonofthreemicrowaveradiativetransfermodelsforsimulatingsnow

brightnesstemperature

AlainRoyer1,2,AlexandreRoy1,2,BenoitMontpetit1,OlivierSt-Jean-Rondeau1,2,GhislainPicard4,LudovicBrucker5andAlexandreLanglois1,2

1CARTEL,UniversitédeSherbrooke,Québec2Centred'ÉtudesNordiques,Québec3LGGE,CNRS-UJF,Grenoble,France4NASAGSFC,Greenbelt,MD

Thispresentationcomparesthreemicrowaveradiativetransfermodelscommonlyusedforsnowbrightnesstemperature(TB)simulations,namely:DMRT-ML,MEMLSandHUTn-layersmodels.Usingthesamenewcomprehensivesetsofground-basedmeasureddetailedsnowpackphysicalproperties,wecomparedsimulationsofTBsat11,19and37GHzfromthese3modelsbasedondifferentelectromagneticapproachesusingthreedifferentsnowgrainmetrics,i.e.respectivelymeasuredspecificsurfacearea(SSA),calculatedcorrelationlengthusingtheDebyrelationshipandmeasuredmaximumdiameterextent.Comparisonwithsurface-basedradiometricmeasurementsfordifferenttypesofsnow(insouthernQuébec,andinsubarcticandarcticareas)showssimilaraveragedrootmeansquareerrorsintherangeof10KorlessbetweenmeasuredandsimulatedTBswhensimulationsareoptimizedusingscalingfactorsappliedonthesemetrics.Thismeansthat,inpractice,thedifferentapproachesofthesemodels(physicaltoempirical)convergetosimilarresultswhendrivenbyappropriatescaledin-situmeasurements.Wediscussedtheresultsrelativelytotheuncertaintiesinsnowmicrostructuremeasurements.Inparticular,weshowthatthescalingfactortobeappliedontheSSAmeasurementsinordertominimisedtheDMRT-MLsimulatedTBscomparedtomeasuredTBsisnotduetouncertaintyinSSAdata.


❄Page61

SnowPropertiesRetrievalusingDMRT-MLinaStatisticalFrameworkUsingPassiveMicrowaveAirborneObservations

NastaranSaberiandRichardKelly

InterdisciplinaryCentreonClimateChange,andDepartmentofGeographyandEnvironmentalManagement,UniversityofWaterloo,Waterloo,ON

Forwardradiativetransfermodelstoestimatethepassivemicrowavebrightnesstemperaturefrommulti-layeredsnowareincreasinginmaturity.Thechallengenowisintheretrievalsbecauseaninversemodelingapproachshouldbeemployed.Inverseapproachesincludestatisticalmethodsandtechniquesbasedonmachinelearningoptimization,whereacostfunction(afunctionofdifferencebetweenobservedandmodeleddata)isminimizedusinglinearornon-linearoptimizationapproaches.InthisstudyusingtheDenseMediaRadiativeTransfer-MultiLayered(DMRT-ML)model,amodel-basedinversionalgorithmisusedtoretrievesnowdepthwithpassivemicrowaveobservationsfromairborneradiometermeasurementsalignedwithground-basedsnow-surveysintheArcticEurekaregionduringApril2011.Theacknowledgedchallengeinpassivemicrowaveinversion,thatofdealingwithunderdeterminedsetofequations,isaddressedbyexploringtheparameterizationofphysicalquantitiesrequiredtoconstraintinputvariablessuchasgrainsize,density,physicaltemperatureandstratigraphyalsoknownasaprioriinformation.Basedonknownemissionsensitivity(capturedbythemodels),grainsizeasanunknownquantityisoftenusedinthecostfunctionminimizingprocesswhilesnowdepth,thevariabletobeestimated,maybeknownatsomeplacesfrominsitumeasurementsandcanbeusedinthecostfunctionapproach,perhapsthroughamaximumlikelihoodsolutiontothesimulation.ThisgeneralretrievalapproachisusedintheGlobsnowapproachthatemploysemissionmodelofHelsinkiUniversityofTechnology(HUT)whichisitselfbasedonPulliainen’s(2011)method.

IncontrastwithGlobsnow,thisexperimentalstudyemploysmoredetailedcharacteristicsofthesnowpackfromin-situmeasurementsandunlikeGlobsnow,whereabackgroundsnowdepthmapisassimilatedintotheretrievalprocesstomitigateerrors,arangeofacceptablesnowdepthvaluesareconsidered.Insitusnowdepthmeasurementsareusedtoprovideinsightintotheplausibilityofthesnowdepthsused.Moreover,grainsizeisestimatedasanopticalsizeofgrains(asrequiredbytheDMRT-ML).Surfacephysicaltemperatureestimatedfromairborneobservationsisusedasatuningparametertoupdatetheacceptablerangeforretrievedsnowdepth.Theapproachprovidesinsightintothefeasibilityandapplicabilityofthe


❄Page62

proposedmethodologygloballyforspaceborneretrievalssinceitisafairlyfaststatistics-basedframeworkthatleveragesaphysicsbasedmodelsnowradiativetransfermodelinaparsimoniousmanner.


❄Page63

Parameterizationofsnowmicrostructureforpassivemicrowaveradiometry

OlivierSaint-Jean-Rondeau1,2,AlainRoyer1,2,AlexandreRoy1,2,AlexandreLanglois1,2,Jean-BenoîtMadore1,2

1CARTEL,UniversitédeSherbrooke,Sherbrooke,Québec2Centred'ÉtudesNordiques,Québec,Canada

Passivemicrowave(PMW)remotesensinghasprovedtobethemostpracticalapproachincharacterizingtheseasonalsnowpackofremotenorthernregionsatthesynopticscale.Thisisattributedtotheavailabilityofadailysurfacecoveragesince1978andthesensitivityofPMWtothedielectricpropertiesofsnow.Thepolarizedthermalmicrowaveradiationemittedbythegroundistransmitted,absorbedandscattered,becomingsensitivetotheverticalprofileofsnowmicrostructure.Radiativetransfermodelsareusedtocalculatethebrightnesstemperatureasafunctionofmicrostructuralproperties:snowdensity,grainsize,and3-Dgrainstructure.

However,microstructureisdifficulttodescribewithaquantifiablemetric;itcanbeassesseddirectlyorindirectlybyvariousmethodsandinstruments,whichprovidecomplementaryinformation.Thesemethodsincludesnowdensitycuttermeasurement,infraredreflectometryforspecificsurfacearea(SSA)retrieval,micropenetrometry(SMP),thermalconductivity,andvisualgrainsizeandclassification.

Thisstudyaimstoassessthevalueofeachofthesemeasurementsasproxiesformicrostructuralparametersinaphysically-basedmodel,namelytheDenseMediaRadiativeTransfer–Multi-Layer(DMRT-ML).Forthispurpose,measurementcampaignswereconductedduringthewintersof2015and2016inSouthernandNorthernQuébec.In-situmeasurementsarecomparedtoDMRT-MLbrightnesstemperaturesusingeitherinfraredreflectometryorSMPderivedSSAassnowgrainmetric,aswellasvariousdensityandstratificationmetrics.Furthermore,anexperimentrelatingtheabsorptionanddiffusioncoefficientsofsampledhomogeneouslayersofsnowtomicrostructuralpropertieswasrealised.


❄Page64

Energybalanceandmeltoverapatchysnowcover

SebastianSchlögl1,2,RebeccaMott1andMichaelLehning1,2

1WSLInstituteforsnowandavalancheresearchSLF,Davos,Switzerland2SchoolofArchitecture,CivilandEnvironmentalEngineering,ÉcolePolytechnique

FédéraledeLausanne,Lausanne,Switzerland

Apatchysnowcoversignificantlyaltersthesnowsurfaceenergyexchangeandthereforesnowmeltespeciallydueto(i)horizontaladvectionofwarmairfromthebaregroundtothesnowpatchand(ii)thedevelopmentofstrongstabilityclosetotheground,whichareopposingeffects.Assnowandhydrologicalmodelsaretypicallylimitedtosimulatingpointwiseverticalexchangebetweenthegroundandtheatmosphereanddonotincludelateraltransport,meltingratesaresufficientlyrepresentedexclusivelyforhomogeneoussnowcovers.Forapatchysnowcover,modelledmeltingratesofsnowpatchesareunderestimatedattheupwindedge.Inthisstudyweassesstherelativecontributionoftheadvectiveheatfluxtothetotalsurfaceenergybalanceandthereforesnowmeltusing(i)high-resolutionmeasurementsofsnowdepthchangesobtainedfromTerrestrialLaserScanning,(ii)theatmosphericmodelAdvancedRegionalPredictionSystemARPSand(iii)thedistributedandphysics-basedsnowmodelAlpine3D.WeforceAlpine3Dwithairtemperatureandwindvelocityfieldscalculatedfromthenon-hydrostaticatmosphericmodelARPS.

Analysisofmeasuredmeltrateshaveshowna5%increaseinsnowmeltingduetotheeffectoftheadvectiveheatfluxforatypicalspringsnowdistribution.WenumericallyinvestigatetheeffectofatmosphericflowfielddynamicsoverapatchysnowcoveronthetotalsurfaceenergybalancebyforcingAlpine3Dwithfullyresolvedmeteorologicalfields(airtemperatureandwindvelocity)obtainedfromARPSclosetothesurface.Asareferenceandforcomparison,themodelisforcedwithairtemperatureandwindvelocityfieldsabovetheblendingheight.Wepresentquantitativeexperimentalandnumericalresultsthatshowhowthesnowmeltratechangeswithsnowcoverfraction(SCF)andthemeanperimeterofthesnowpatchesandincreaseswithdecreasingSCFanddecreasingperimeter.


❄Page65

Howdostabilitycorrectionsperformoversnow?

SebastianSchlögl1,2,RebeccaMott1andMichaelLehning1,2

1WSLInstituteforsnowandavalancheresearchSLF,Davos,Switzerland2SchoolofArchitecture,CivilandEnvironmentalEngineering,ÉcolePolytechnique

FédéraledeLausanne,Lausanne,Switzerland

Modellingturbulentheatfluxesoversnowisachallengingissue.Onespecificcomplicationisthatstabilitycorrectionsaretypicallydeterminedovernon-snowsurfacesbutoftenappliedoversnow.Thisstudyfocusesonsensibleheatfluxparametrizationsinstableconditionsbytestingfivewell-establishedanddevelopingtwonewstabilitycorrectionfunctionsfortwoalpineandtwopolartestsites.

Theperformancetestofdifferentstabilitycorrectionsrevealsanoverestimationoftheturbulentsensibleheatfluxforhighwindvelocitiesandagenerallypoorperformanceofallinvestigatedfunctionsforlargetemperaturegradients.Thestabilityparametrizationsproduceanerrorbetween7and12Wm-2onaverage.ThesmallesterrorofpublishedstabilitycorrectionsisfoundfortheHoltslagscheme,whichisrecommendedforverystableconditions.Thenewlydevelopedunivariateparametrization(classicallydependentonthestabilityparameter)hasitsstrengthforatmosphericconditionsnearneutralandformoderatewindvelocities(2-5m/s).Ournewlydevelopedbivariateparametrizationbasedonasimplelinearcombinationofbuoyancyandsheartermswasfoundbetoaviablealternativeespeciallyinregionswithlargewindvelocities.Thebivariateparametrizationalsoavoidsknowndifficultiesforlargevaluesofζ.


❄Page66

2ndEuropeanSnowScienceWinterSchool

Schneebeli,M.1,Lemmetyinen,J.21WSLInstituteforSnowandAvalancheResearchSLF,Switzerland2FinnishMeteorologicalInstituteFMI,Finland

ThecryosphereformsanintegralpartoftheEarth.Theseasonalsnowcoverextendsto49%ofthetotallandsurfaceinmidwinterinthenorthernhemisphere.Monitoringofseasonalsnowcoverpropertiesisthereforeessentialinunderstandinginteractionsandfeedbackmechanismsrelatedtothecryosphere,butalsotoecosystems.However,asacomplexandhighlyvariablemedium,manyessentialpropertiesofseasonalsnowcoverhavetraditionallybeendifficulttomeasure.Thepast10yearssnowsciencehasseenarapidchangefromasemi-quantitativetoaquantitativescience;especiallythenewmethodsallowimprovedquantificationofthesnowmicrostructure.Understandingphysicalandchemicalprocessesinthesnowpackrequiresdetailedmeasurementsofthemicrostructure.TheSnowGrainSizeIntercomparisonWorkshop2014recentlysolidifiedtheprogressinquantitativemeasurements.

The2ndEuropeanSnowScienceWinterSchoolinPreda,Switzerland,inFebruary2016aimedatteachinggraduatestudentsinmodernsnowmeasurementtechniques.Inadditiontothelectures,differentmeasuringinstrumentswereavailableforthestudentstogethands-onexperienceinthefield.Thelistofinstrumentswaslong,rangingfromhandlensesandcrystalplatesfortraditionalsnowpitsuptohigh-resolutionlasersandpenetrometers.Fieldmeasurementsoccurredinsmallgroupsandareportisprepareddescribingthemethods,resultsandinterpretation.

Thesestate-of-the-artsnowmeasurementtechniqueswillbetaughtinfutureinanannualsnowschoolheldinvariousplacesinEurope.The2017willoccurinSodankyla,Finland.


❄Page67

Singleandmulti-sensorsnowwetnessmappingbySentinel-1andMODISdata

RuneSolberg1,ØysteinRudjord1,ØivindDueTrier1,GheorgheStancalie2,AndreiDiamandi2andAnisoaraIrimescu2

1NorwegianComputingCenter,Oslo,Norway2RomanianNationalMeteorologicalAdministration,Bucharest,Romania

Snowmonitoringisessentialforpredictionoffloodingduetorapidsnowmelt,toprovidesnowavalancheriskforecastsandforwaterresourcemanagement–includinghydropowerproduction,agriculture,groundwateranddrinkingwater.Snowwetnessandsnowliquidwaterareessentialvariablesformonitoringthesnowstateandprovidingearlywarningoffloodriskandsnowavalanchesduringthemeltingseason.ThepresentationshowsthefirstresultsfromtheSnowBallprojectofsingle-sensorandfusionalgorithmsappliedonSentinel-1SARandMODISdataforfrequentmonitoringofthesnowwetnessduringthemeltingseasonsinNorwayandRomania.

Sentinel-1C-bandSARissensitivetopresenceofwetsnow.Wetsnowcanbedetectedsincetheradarbackscatterdropssignificantly.However,withC-bandSARitisdifficulttoquantifyhowwetthesnowis.WetsnowmappingintoasetoffivecategoriesofwetnesshasbeendemonstratedinthepastbyNRusingMODISdata.Thecombinationofsurfacetemperatureandthetemporaldevelopmentoftheeffectivesnowgrainsizeareusedtoinferapproximatelyhowwetthesnowis.IntheSnowBallprojectthisapproachisnowportedtothecombineduseoftheSentinel-3OLCIandSLSTRsensors.Thepreviousalgorithmisalsoadvancedtoenablefurtherdiscriminationofsnowwetnessclassesquantitativelyrelatedtothesnowliquidwater(volumeofliquidwaterpervolumeofsnow)forthesnowsurface.Fieldmeasurementshavebeenaccomplishedusingspectroradiometermeasurementsanddirectmeasurementsofsnowliquidwaterwithadielectricprobetodeveloptheretrievalmodel.TheretrievalmodelwillalsobeadaptedtoSentinel-3dataandappliedinthenewalgorithm.

Furthermore,toutilisethecombinedcapabilityofSentinel-1andMODIS/Sentinel-3formoreaccurateretrievalandimprovedtemporalcoverage–giventhatopticalsensorsarelimitedbycloudcoverandSARonlydetectswetsnow–wedevelopasensor-fusionapproach.ThealgorithmappliesahiddenMarkovmodel(HMM)tosimulatethesnowwetnessstatesthesnowsurfacegothrough,giventhetemporalobservationsofthesurfaceconditions.Themostlikelycurrentsnowstateisestimated,givingthecurrentsnowliquidwatercategory.

ThesnowproductsfromSAR,opticalandthemulti-sensorapproacharevalidatedagainstcal/valsitesprovidingfrequentsnowmeasurementsinRomaniaandNorway,andadditionalfieldcampaignswhereasignificantterrainreliefispresentprovidingcorrespondingsignificant


❄Page68

gradientsinsnowwetnessduringthesnowmeltseason.Successfulalgorithmsareimplementedanddemonstratedinaprototypesystemproducingdailywet-snowmapsofRomaniaandNorway.Whenthesystemisoperationalised,theproductswillbeusedinoperationalhydrologicalmodelsassistingfloodpredictionforissuingfloodwarnings.Similarly,theproductswillbeusedbythesnowavalancheserviceprovidingavalanchewarnings.


❄Page69

Modelingpolaricesheetemissionfrom0.5-2.0GHzwithapartiallycoherentmodel

oflayeredmediawithrandompermittivitiesandroughness

ShurunTan1,LeungTsang1,TianlinWang1,MohammadrezaSanamzadeh1,JoelJohnson2,andKennethJezek3

1RadiationLaboratory,DepartmentofElectricalEngineeringandComputerScience,

UniversityofMichigan,AnnArbor2ElectroScienceLaboratory,TheOhioStateUniversity,Columbus3SchoolofEarthSciences&ByrdPolarResearchCenter,TheOhioStateUniversity,

Columbus

Thesurfaceofthepolaricesheetischaracterizedbyrapiddensityvariationsoncentimeterscalesduetotheaccumulationprocess.Thefluctuationformslayersnearthetopoftheicesheetaswellasintroducinginterfaceroughness.Thefluctuatingpermittivitiesamonglayersasaresultofdensityvariationcausereflectionsandmodulatetheicesheetemission.Interfaceroughness,ontheotherhand,cancauseangularandpolarizationcoupling.Ourinterestsarethebrightnesstemperaturesbetween0.5to2.0GHzfortheUltra-wideBandSoftwareDefinedRadiometer(UWBRAD)project.TheUWBRADgoalistosensetheinternaltemperatureprofileoftheicesheetusinglowfrequencyultra-widebandradiometry.Previouslyincoherentmodelsandcoherentmodelswereusedtocalculatethebrightnesstemperaturesofmultilayeredmediaconsistingofthousandsoflayers.Inthispaper,weuseapartiallycoherentapproach.

Whenthecorrelationlengthsofthedensityfluctuationsarewithinawavelengthinsidetheicesheet,thecoherentinterferenceduetoreflectionsremainsevenafterstatisticalaveragesoverdensityprofiles.Thecoherentwaveeffectsare“localized”inrandomlayeredmediatospatialscaleswithinafewwavelengths.Thuswecandividetheentireicesheetintoblocks,witheachblockontheorderofafewwavelengths,andapplyfullycoherentscatteringmodelswithinasingleblock.Theblocksarealsosizedtocorrespondtothebandwidthofthemicrowavechannelsothatinterferenceeffectswithinachannelcanbecaptured.Wethenincoherentlycascadetheintensitiesamongdifferentblocks.AsmallernumberofrealizationsisthenrequiredintheMonteCarloaveragingprocessforeachblockduetothesmallernumberofinterfaces.Thispartiallycoherentapproachhasprovedtobemuchmoreefficientthanapplyingthefullycoherentmodeltotheentireicesheet,andtoproduceresultsinagreementwiththefullycoherentapproach.


❄Page70

Thepartiallycoherentapproachalsoenablesustoexamineinterfaceroughnesseffectsbyapplyingafullwavesmallperturbationmethoduptosecondorder(SPM2)tothemulti-layeredroughnessscatteringproblemwithinthesameblock.TheSPM2hastheadvantageofconservingenergy.Wereportnumericalresultsincheckingenergyconservationandillustratetheangularandpolarizationcouplingeffectsarisingduetointerfaceroughness.


❄Page71

SpatialvariabilityofsnowatTrailValleyCreek,NWT

AaronThompson1,RichardKelly1,PhilipMarsh1,TylerdeJong2

1InterdisciplinaryCentreonClimateChangeandDepartmentofGeographyand

EnvironmentalManagement,UniversityofWaterloo,ON2WilfridLaurierUniversity,Waterloo,ON

Witharenewedfocusonlargescale,globalremotesensingofsnow,bolsteredbyupcomingprojectslikeNASA’sSnowExcampaign,theimportanceofgroundreferencingthroughinsitumeasurementsisemphasized.RecentstudieshavesuggestedthatmicrostructuralelementsofthesnowpackmaybeacriticaldriveroftheradarresponseatKu-andX-bandfrequenciesfurtherhighlightingtheimportanceofacomprehensivefielddataset(Thompsonetal.,inpreparation).

Afieldcampaign,inApril2016,locatedatEnvironmentCanada’sTrailValleyCreekresearchbasinintheNorthwestTerritoriesfocusedoninsitusnowpackmeasurements,andlaythefoundationfora3-yearstudythatwillcombineground-basedradarobservationsatKu-andX-bandfrequenciesusingUW-SCAT,withdifferentialinterferometricSARtechniquesaimedatextractingsnowvolumeinformation,andwillthereforerequirearobustsuiteoffieldmeasurements.

Theseobservationsallowedustoexplorethespatialvariabilityofsnowmicrostructureinavarietyofseasonalarcticaccumulationenvironmentsincludingaforestedsite,wind-swepttundra,anddriftedsnow.Measurementsincludedsnowdepth,densityandtemperatureprofiles,alongwithsnowgrainandstratigraphyobservationsaugmentedbyNIRphotography.Employingaseriesof5mby5morthogonalsnowtrenchesateachsite,weinvestigatedthespatialvariabilityofthesesnowpackcharacteristicsovershortdistancescales.Localmeteorologicaldata,collectedattwoofthesites,providedevidenceoftheprocessesthatcontrolledthesnowpackdevelopmentandmetamorphosis.Collectively,thesemeasurementsnotonlyprovidedinsightintothenatureofsnowmicrosctructurevariabilityintheseenvironments,butalsohelpedtoidentifyoptimalsiteswithinTrailValleyCreekforfutureradaracquisitions.


❄Page72

Long-termtrendsandvariabilityofwintersnowaccumulationatWhiteGlacier,

Nunavut,Canada

LauraThomsonandLukeCopland

DepartmentofGeography,UniversityofOttawa,Ontario

ThemeasurementofwintersnowaccumulationhascontinuedaspartoftheglaciologicalmassbalanceobservationsatWhiteGlacier(90°47’W,79°29’N,100-1780ma.s.l.)sinceglacierresearchbeganonAxelHeibergIslandin1959.Inthisstudyweexaminethevariabilityofsnowaccumulationwithelevationover55yearsofobservationsandconsidertrendsinaccumulationoverthistimeperiod.Decliningseaiceextentanddurationoverthepasttwodecadesareexpectedtoleadtocorrespondingincreasesinoceantemperatures,evaporation,andprecipitationovertheCanadianArctic.ThishaspromptedpredictionsthatsnowaccumulationwillincreaseovertheQueenElizabethIslands,asobservedattheEurekaWeatherStation(85°56’W,79°59’N,10ma.s.l.).However,todatenostatisticallysignificanttrendofincreasingsnowaccumulationhasbeenobservedintheaccumulationareaofWhiteGlacier.Inadditiontoconductinganalysisofspatialandtemporalvariabilityinsnowfallovertheglacier,weconsidertheimpactsonglaciermassbalance,whichhasshownasignificantdecreaseinthepastdecade,andicedynamics.Sincethemassimbalancebetweentheaccumulationandablationareasofaglacieristheprimarydrivingforceforicemotion,weintegratesnowaccumulationandiceablationobservationsatWhiteGlaciertomodelmasstransferthroughcross-sectionalfluxgatesat370,580,and870ma.s.l.Comparisonofthesemodelledicefluxeswithobservationsoficemotion,whichindicatethatvelocitieshavedecreasedontheorderof45%,15%,and5%attheserespectivelocationssince1960,enablesustoestimatethedynamicresponsetimeofWhiteGlacier.


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SnowMicrostructureCharacterizationandNumericalSimulationofMaxwell’s

Equationin3DAppliedtoSnowMicrowaveRemoteSensing

LeungTsang1,ShurunTan1,JiyueZhu1,andXiaolanXu2

1RadiationLaboratory,DepartmentofElectricalEngineeringandComputerScience,TheUniversityofMichigan,AnnArbor

2JetPropulsionLaboratory,Pasadena,CA

Inthispaper,wereviewourrecentresearchresultsonsnowmicrostructurecharacterizationandphysicalmodelsofmicrowaveremotesensingofterrestrialsnow.ThestudydomainisfocusedontheSnowColdLandProcessexperiment(SCLP)thatisintheDecadalStudy.TheSCLPconsistsofradarbackscatteringatX-andKubandandradiometricbrightnesstemperaturesatKu-andKaband.

Insnowmicrostructure,weusecorrelationfunctiontocharacterizethesnow.Weusethebicontinuousmediamodeltogeneratecomputersnow.Thebicontinuousmediahascorrelationfunctionsdependentontheinputparameters.Fordenselydiscretescatterers,weusethepairdistributionfunctionsofstickyspheresandmultiplesizespheres.Recently,weshowthatthecorrelationfunctionscanbederivedfromthepairdistributionfunctions.Thusthecorrelationfunctionbecomesthebasisofcomparisonsofbicontinuousmediaofcomputersnow,denselypackedspheres,andrealsnow.Thederivedcorrelationfunctionsaredistinctlydifferentfromthetraditionalexponentialcorrelationfunctions.Theyareexponentialneartheoriginbuthavetailsforlongerdistances.Thusatleasttwoparametersareneededtocharacterizethecorrelationfunctioninsteadofone.Fieldmeasurementsofsnowmicrostructuretypicallyprovideavisualgrainsize,whichisthemaximumextentofthedominantsnowgrains.Ontheotherhand,theemergingmeasurementsofthespecificsurfacearea(SSA)ismoresensitivetofinesnowgrains.TheSSAcanbeconvertedtoanequivalentopticalgrainsize.Knowingtheopticalgrainsizeandthevisualgrainsize,wewillapproximatethecorrelationfunctionofsnowmicrostructurefromthepairdistributionfunctionsoftwo-sizesphereswithvaryingnumberdensities.

Thebicontinuousmediahasbeencombinedwiththepartiallycoherentapproachofdensemediaradiativetransfer(DMRT)toprovidelookuptablesofbackscatterandbrightnesstemperaturesofsnowpackundervariousconditions.InDMRT,Maxwell’sequationissolvedwithinseveralcubicwavelengthsofstatisticallyhomogeneoussnowvolumetocomputethe


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phasematrix.Thephasematrix,accountingforthecoherentnearfieldandintermediatefieldinteractions,isthensubstitutedintotheradiativetransferequationtopropagatetheintensityoverthesnowvolume,accountingfortheincoherentfarfieldandvolume/surfaceinteractions.Suchforwardsnowpackscatteringmodelhasbeenappliedtodevelopsnowwaterequivalent(SWE)retrievalalgorithmsandshowntobesuccessfulwhentestedovertheFinlandSnowScatandSnowSARdataset.

Afullycoherentsnowpackscatteringmodelisalsodevelopedtocomputethebackscatteringcoefficientsandthebrightnesstemperaturesofasnowpack.ThemodelisbasedonnumericallysolvingtheMaxwell’sequationin3D(NMM3D)directlyovertheentiredomainofsnowpack.Weuseahalf-spacetorepresentthesoilorseaiceunderthesnowpack,andusethebicontinuousmediatorepresentthesnowvolume.Thefullycoherentapproachpredictsthecomplexscatteringmatrixfromthesnowpack,includingbothmagnitudeandphase.Inpassiveremotesensing,thisapproachallowsarbitrarytemperatureandlayerprofilesofthesnowpack.ThebrightnesstemperaturesandbackscattersoutofthefullycoherentmodelarecomparedagainsttheresultsofDMRTforvarioussnowpackconfigurations.Wealsoillustratetheco-polarizationphasedifferenceofananisotropicsnowlayerextractedfromfullwavesimulations.


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ComparisonofSatellitePassiveMicrowave,AirborneGammaRadiationSurvey,andGroundSurveySnowWaterEquivalentEstimatesintheNorthernGreatPlains

SamuelTuttle1,EunsangCho1,CarrieM.Vuyovich1,2,CarrieOlheiser3,JenniferM.Jacobs1

1UniversityofNewHampshire,Durham,NH2U.S.ArmyCorpsofEngineersColdRegionsResearchandEngineeringLaboratory,

Hanover,NH3NationalWeatherServiceNationalOperationalHydrologicRemoteSensingCenter,

Chanhassan,MN

Remotesensinghasthepotentialtoenhanceoperationalriverflowforecastingbyhelpingtoconstrainestimatesofsnowwaterequivalent(SWE).SnowmeltcontributessignificantlytorunoffinnorthernandmountainousareasofNorthAmerica.InthenorthernGreatPlains,meltingsnowisaprimarydriverofspringflooding,soknowledgeofthemagnitudeandspatialdistributionofSWEisnecessaryforaccuratefloodforecasting.However,groundsurveysarerelativelysparseintheregionandprovideonlypointestimates.AirbornegammaradiationsurveysfromtheU.S.NationalWeatherService(NWS)provideSWEestimatesatlargerresolution(approximately5-7km2),butareavailableonly1-4timesperwinter.Thus,satelliteremotesensingcanincreasethespatiotemporalcoverageofSWEobservationsavailableforforecastingpurposes.WecomparesatellitepassivemicrowaveestimatestoNWSairbornegammaradiationsnowsurveyandU.S.ArmyCorpsofEngineers(USACE)groundsnowsurveySWEestimatesinthenorthernGreatPlains.ThethreeSWEdatasetscomparefavorablyinthelowrelief,lowvegetationstudyarea,butthedifferentspatialextentsofeachmeasurementcomplicatesthecomparison.Additionally,theeffectofsnowgrainsizechangesandwetsnowonthesatelliteSWEestimatesremainlimitationsofthepassivemicrowavemethod.AwarenessofwhenandhowsnowpackphysicalconditionsimpactretrievalscanoptimizetheusefulinformationprovidedbypassivemicrowaveSWEobservationsforoperationalflowforecasting.


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Sensitivityanalysisofpassivemicrowavebrightnesstemperaturestodistributed

snowmelt

C.M.Vuyovich1,J.M.Jacobs2,C.A.Hiemstra3,E.J.Deeb1,J.B.Eylander

1ColdRegionsResearchandEngineeringLaboratory,Hanover,NewHampshire2CivilandEnvironmentalEngineering,UniversityofNewHampshire,Durham3ColdRegionsResearchandEngineeringLaboratory,Fairbanks,Alaska4HQAFWeatherAgency,OffuttAFB,Nebraska

Globaldatasetsofrecordedpassivemicrowaveemissionsprovidenon-destructive,dailyinformationonsnowprocesses,andthemicrowavesignalishighlyresponsivetosnowwetnessduetothesensitivityoftheradiancetochangesinthedielectricconstant.Akeychallengetousingthemicrowavemeltsignalisthatitsspatialresolutionisquitecoarseandnotabletoexplicitlycharacterizesub-gridscalevariationsneededformostwaterresourceapplications.Theobjectiveofthisresearchistotestthesensitivityofbrightnesstemperatureswithinamicrowavepixelasitrelatestospatiallydistributedliquidwatercontentofthesnowpack.Dailysnowstatesweresimulatedfora14-yearperiodusingahigh-resolution(50m)energybalancesnowmodelovera34x34kmpixel.Thesedatawerefedintoamicrowaveemissionmodeltosimulatebrightnesstemperaturesduringwetsnowevents.Asensitivityanalysiswasconductedtodeveloparelationshipbetweenthechangeinmicrowavebrightnesstemperatureandthepercentareaaffectedbyliquidwatercontentinthesnowpack.ThemodeloutputwasalsocomparedtoAMSR-Epassivemicrowavesatellitedataanddischargedataatabasinoutletwithinthestudyarea.Theresultsareusedtohelpunderstandthehydrologicalimpactoflarge-scalesnowmelteventsasdetectedbypassivemicrowavedata.


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UAVMappingofDebrisCoveredGlacierChange,LlacaGlacier,CordilleraBlanca,

Peru

OliverWigmoreandBryanMark

DepartmentofGeographyandByrdPolar&ClimateResearchCenter,TheOhioStateUniversity,Columbus

TheglaciersoftheCordilleraBlancaPeruarerapidlyretreatingasaresultofclimatechange,alteringtiming,quantityandqualityofwateravailabletodownstreamusers.Furthermore,increasesinthenumberandsizeofproglaciallakesassociatedwiththesemeltingglaciersisincreasingpotentialexposuretoglacierlakeoutburstfloods(GLOFs).Understandinghowtheseglaciersarechangingandtheirconnectiontoproglaciallakesystemsisthusofcriticalimportance.Mostsatellitedataaretoocoarseforstudyingsmallmountainglaciersandareoftenaffectedbycloudcover,whiletraditionalairbornephotogrammetryandLiDARarecostly.RecentdevelopmentshavemadeUnmannedAerialVehicles(UAVs)viableandpotentiallytransformativemethodforstudyingglacierchangeathighspatialresolution,ondemandandatrelativelylowcost.

Usingacustomdesignedhighaltitudehexacopterwehavecompletedrepeataerialsurveys(2014and2015)ofthedebriscoveredLlacaglaciertongueandproglaciallakesystem.Analysisofhighlyaccurate10cmDEM'sandorthomosaicsrevealshighlyheterogeneouschangesintheglaciersurface.Themostrapidareasoficelosswereassociatedwithexposedicecliffsandmeltwaterpondsontheglaciersurface.Significantsubsidenceandlowsurfacevelocitieswerealsomeasuredonthesedimentswithinthepro-glaciallake,indicatingthepresenceofextensiveregionsofburiediceandcontinuedconnectiontotheglaciertongue.Onlylimitedhorizontalretreatoftheglaciertonguewasrecorded,indicatingthatsimplemeasurementsofchangesinaerialextentareinadequateforunderstandingactualchangesinglaciericequantity.


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ImprovingatmosphericcirculationandturbulentheatfluxeswiththeArctic

SystemReanalysis

AaronB.Wilson1,DavidH.Bromwich1,2,Le-ShengBai1,G.W.KentMoore3,FlavioJustino1,4

1PolarMeteorologyGroup,ByrdPolarandClimateResearchCenter,TheOhioStateUniversity,Columbus

2AtmosphericSciencesProgram,DepartmentofGeography,TheOhioStateUniversity,Columbus

3DepartmentofPhysics,UniversityofToronto,Toronto,Ontario4DepartmentofAgriculturalEngineering,UniversidadeFederaldeViçosa,Viçosa,Brazil

TheArcticSystemReanalysis(ASR),ahigh-resolutionregionalassimilationofmodeloutput,observations,andsatellitedataacrossthemid-andhighlatitudesoftheNorthernHemispherefortheperiod2000–2012hasbeenperformedat30km(ASRv1)and15km(ASRv2)horizontalresolution.AcomparisonbetweentheadvancedASRv2andtheglobalEuropeanCentreforMediumRangeForecastingInterimReanalysis(ERAI)showsthetropospheretobewellrepresentedintheASRv2.Monthlyandannualtemperature,humidity,pressure,andwinddifferencescomparedtosurfaceandupper-airobservationsaresmall.Thehigh-resolutionlandsurfacedescriptioninASRv2leadstomoreaccuraterepresentationoftopographically-forcedwindevents,suchastipjetsandbarrierwindsalongthesoutheastcoastofGreenland,aswellasatmosphericcirculationthroughouttheArctic.Withsensibleandlatentheatfluxesstronglylinkedtowindspeedandland-surfacechange,ASR’shighresolutionandweekly-updatedvegetationfromtheMODISleadtomuchimprovedturbulentheatfluxescomparedtoglobalreanalyses.Analysisofsurfaceevaporationshowsthatwhileglobalreanalysesexhibitweakintraseasonalvariability,weeklychangesinthesnow-albedofeedbackandassociatedchangesintheleafareaindexproduceabetterdepictionoftheseasonalityofsurfaceheatfluxesoverland.Therefore,theASRhasproventobeanimportantresourceformanyArcticstudiesincludinginvestigationsofmesoscalephenomenaaswellasthediagnosisofchangeinthecoupledArcticclimatesystem.


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ConsiderationofMountainSnowStoragefromGlobalDataProducts

MelissaL.Wrzesien1,MichaelT.Durand1,andTamlinM.Pavelsky2

1SchoolofEarthSciencesandByrdPolarandClimateResearchCenter,theOhioState

University2DepartmentofGeologicalSciences,UniversityofNorthCarolinaatChapelHill

Seasonalsnowaccumulationandablationareimportantcomponentsinnotonlytheglobalwaterbalance,butalsotheenergybudget.Despiteitsimportance,webelieveanestimateofglobalsnowstorage–particularlyinmontaneregions–isnotwellconstrainedbycurrentdatasets,whetherobservationalormodel-based.Herewepresentestimatesofsnowstorage,bothgloballyandforonlyregionsofcomplextopography,frommultipleglobaldatasets,includingsatelliteproductsandreanalyses.GlobalproductsincludeAMSR-E,GLDAS,MERRA,andERA-Interim,allofwhichhavespatialresolutionof~25kmorlarger.WeconsiderbothApril1andpeaksnowwaterequivalent(SWE)overtheperiodof1980-2010,orwherethedataisavailable.Mostproductsestimate~2000-4000km3ofsnowstorage,globally,whenaveragedovertheperiodofrecord,with30-50%ofthesnowstorageexistinginmountains.However,regionalclimatemodelsimulationsforahandfulofNorthAmericamountainranges(withspatialresolutionof3-9km),whicharealsopresentedhere,suggest>500km3ofsnowaccumulatesannuallyintheSierraNevadaofCaliforniaandtheCoastMountainsofBritishColumbiaalone.Wefurtherdiscussthepossibilityofbiasesincurrently-availableglobalproductsandwhetherregionalclimatemodelresultsmaypresentamorereliableglobalSWEestimate.


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Canregional-scalesnowwaterequivalentestimatesbeenhancedthroughtheintegrationofamachinelearning

algorithm,passivemicrowavebrightnesstemperatureobservations,andaland

surfacemodel?

YuanXue,BartonA.Forman

UniversityofMarylandCollegePark,DepartmentofCivilandEnvironmentalEngineering

Toaccuratelyestimatethemassofwaterwithinasnowpack(a.k.a.,snowwaterequivalent(SWE))acrossregionalorcontinentalscalesisachallenge,especiallyinthepresenceofdensevegetation.InordertoovercomesomeofthelimitationsimposedbytraditionalSWEretrievalalgorithmsandradiativetransfer-basedsnowemissionmodelsinforestedregions,thisstudyexplorestheuseofawell-trainedsupportvectormachine(SVM)enroutetomerginganadvancedlandsurfacemodelwithinaradianceemission(i.e.,brightnesstemperature(Tb))assimilationframeworkinordertoimprovemodel-basedSWE(andsnowdepth)estimates.Inanassimilationcontext,thegoalofdirectTbassimilationispreferableasitavoidsinconsistenciesintheuseofancillarydatabetweentheassimilationsystemandtheindependently-generatedgeophysicalretrieval.ExistingstudiesalsosuggestthataSVM-basedobservationoperatorismorereliablewithinanassimilationframework(relativetoasnowemissionmodel)withouttheneedtoassumeauniformsnowpackorfixedsnowdensityorfixedsnowgrainsize.However,itiswidely-acknowledgedthatsatellite-basedpassivemicrowave(PMW)Tbobservationsareoftencontaminatedbyoverlyingatmosphericandforestrelatedemissionsignals.Therefore,theutilizationofaSVM-basedPMWTbpredictionmodeltrainedondecoupled,satellite-basedTbestimatesforintegrationintoanexistinglanddataassimilationsystemisexploredinthisstudy.Theperformanceoftheoriginal(i.e.,coupled)Tbassimilation,anddecoupledTbassimilationproceduresareevaluatedviacomparisonstostate-of-the-artSWE(orsnowdepth)productsaswellasavailableground-basedobservations.ItisshownthatSVMperformanceimproveswhenintegratingatmosphericandforestdecouplingprocedures.


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Decouplingatmospheric-andforest-relatedradianceemissionsfromsatellite-basedpassivemicrowaveobservationsover

forestedandsnow-coveredlandinNorthAmerica

YuanXue,BartonA.Forman

UniversityofMarylandCollegePark,DepartmentofCivilandEnvironmentalEngineering

Thisstudyaddressestwosignificantsourcesofuncertaintyprevalentinsnowwaterequivalent(SWE)retrievalsderivedfromAdvancedMicrowaveScanningRadiometer(AMSR-E)passivemicrowave(PMW)brightnesstemperature(Tb)observationsat18.7GHzand36.5GHz.Namely,atmosphericandoverlyingforesteffectsaredecoupledfromtheoriginalAMSR-EPMWTbobservationsusingrelativelysimple,first-orderradiativetransfermodels.ComparisonsagainstindependentTbmeasurementscollectedduringairbornePMWTbsurveyshighlighttheeffectivenessoftheproposedAMSR-Eatmosphericdecouplingprocedure.Theatmospherically-contributedTbrangesfrom1Kto3KdependingonthefrequencyandpolarizationmeasuredaswellasmeteorologicalconditionsatthetimeofAMSR-Eoverpasses.Itisfurthershownthatforestdecouplingshouldbeconductedasafunctionofbothlandcovertypeandsnowcoverclass.Theexponentialdecayrelationshipbetweentheforeststructureparameter,namelysatellite-scaleleafareaindex(LAI),andsatellite-scaleforesttransmissivityisfittedacrosssnow-coveredterraininNorthAmerica.Thefittedexponentialfunctioncanbeutilizedduringforestdecouplingactivitiesforevergreenneedleleavedforestandwoodysavannaregions,butremainsuncertaininotherforesttypesduetosparsecoverageinsnow-coveredregions.Byremovingforest-relatedTbcontributionsfromtheoriginalAMSR-Eobservations,theresultsshowthatTbspectraldifferencebetween18.7GHzand36.5GHzincreasesacrossthinly-vegetatedtoheavily-vegetatedregions,whichcanbebeneficialwhenusingwithtraditionalSWEretrievalalgorithms.ComparisonsaremadebetweensnowdepthandSWEestimates,state-of-the-artretrievalproducts,andindependentground-basedobservations.WhenusingthedecoupledPMWTbestimates(relativetousingtheoriginal,coupledAMSR-ETbobservations),snowdepthbiasisreducedby60%andSWEbiasisreducedby55%.However,computedRMSEvaluessuggestrandomerrorsinthesnowdepthandSWEretrievals(withorwithoutapplicationofthedecouplingprocedures)aresignificantandremainsanissueforfurtherstudy.

73rd Eastern Snow Conference Scientific Program & Abstracts

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