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73rdEasternSnowConferenceScientificProgram&Abstracts
❄ Airborneandspaceborneremotesensingofsnowandice ❄
HighbanksMetroPark,Columbus,Ohio,February2015.
❄June14-16,2016,attheByrdPolar&ClimateResearchCenterandtheWexnerCenterfortheArts,TheOhioStateUniversity ❄
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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.
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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‰.
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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.
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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.
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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.
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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.
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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.
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Emerginginternationalpartnershipopportunitieswillalsobepresented,includinghowaspaceborneradardesignedtoaddressneedsrelatedtoterrestrialsnowwouldalsoprovidesuitablemeasurementsforseaice,landice,andoceanvectorwindapplications.
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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.
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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
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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.
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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.
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Apictorialhistoryofchangesinpolarscienceandtechnology:anexamplefromglaciermeasurementsonAxelHeibergIsland,Nunavut,Canada,1959-2015
PeterAdams,MilesEcclestone,GrahamCogley
DepartmentofGeography,TrentUniversity,Peterborough,Ontario
Changesinmodesoftransportation,instrumentationaswellasinpersonnelmake-uphavedramaticallychangedthenatureofpolarscienceinthehalfcenturysincetheMcGillexpeditionsbeganresearchonAxelHeibergIsland,Nunavut,Canada,in1959.Thesechangeshaveintensifiedandextendedresearchonglaciersandlakesandtheyhavealsoproducedmarkedchangesinthewaypolarscienceisconducted.Duringthissameperiodtherehavebeenequallydramaticchangesintheglaciersoftheregion.Thesethemesarepresentedherethroughaseriesofannotatedimages.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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
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glaciersbetweentheLIAandmoderndefinedby2011ASTERGlobalDigitalElevationModelVersion2(GDEMV2)fromtheCordilleraBlanca.
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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.
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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.
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TheNASASnowExairbornesnowcampaign
EdwardKim1,CharlesGatebe1,DorothyHall1,MatthewSturm2andmanyothers
1NASAGoddardSpaceFlightCenter,Greenbelt,MD
2UniversityofAlaska,Fairbanks
NASAisplanningamulti-yearairbornesnowcampaigncalled“SnowEx,”beginningthenorthernhemispherewintersof2016-2017.TheprimarygoalofSnowExYear1isthecollectionofcoincidentobservationswithasuiteofsensortypesincludingactiveandpassiveopticalandactiveandpassivemicrowavesensors.Detailedgroundtruthwillalsobecollectedforalgorithmdevelopment.
TheobjectiveofthispresentationistoupdatethesnowcommunityonSnowExYear1plans,andtoprovideanopportunityforcommunityinputtohelpdesignthecampaigntowardtheultimategoalofdefiningfutureglobal-scalesnowsatellitemeasurementsystems.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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).
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DevelopmentofUniversalRelationshipsbetweenSnowDepth,SnowCoveredAreaandTerrainRoughnessfromNASAAirborne
SnowObservatorydata
NoahMolotchandDominikSchneider
DepartmentofGeography,INSTAAR,andCWEST,UniversityofColoradoBoulder
SnowmeltistheprimarywatersourceintheWesternUnitedStatesandmountainousregionsglobally.Forecastsofstreamflowandwatersupplyrelyheavilyonsnowmeasurementsfromsparseobservationnetworksthatmaynotprovideadequateinformationduringabnormalclimaticconditions.UsingobservationsLiDARandHyperspectralobservationsfromtheNASAAirborneSnowObservatory,wehavedevelopedtransferablefunctionalrelationshipsbetweenterrainroughness,snowcoveredarea,andsnowdepth.Weshowthattherelationshipbetweensnowcoveredareaandsnowdepthvariessystematicallyasafunctionofterrainroughness.Regressionanalysesthatusefractionalsnowcoveredastheindependentvariabletoestimatesnowdepthresultinrelativemeansquarederrorsbetween39%and58%ofmeasuredsnowdepthfordifferentroughnessclassifications.Futureworkwilllookatthechangesintherelationshipbetweensnowdepthandsnowcoveredareathroughtheablationseasontodeterminetherelationship’sutilitytowatersupplyforecasting.Theimportanceofthisworkisillustratedthroughexamplesthatestimatesnowdepthforselectalpineregions.
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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◦.
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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.
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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.
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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.
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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.
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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.
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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.
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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
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proposedmethodologygloballyforspaceborneretrievalssinceitisafairlyfaststatistics-basedframeworkthatleveragesaphysicsbasedmodelsnowradiativetransfermodelinaparsimoniousmanner.
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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.
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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.
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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.
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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.
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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
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gradientsinsnowwetnessduringthesnowmeltseason.Successfulalgorithmsareimplementedanddemonstratedinaprototypesystemproducingdailywet-snowmapsofRomaniaandNorway.Whenthesystemisoperationalised,theproductswillbeusedinoperationalhydrologicalmodelsassistingfloodpredictionforissuingfloodwarnings.Similarly,theproductswillbeusedbythesnowavalancheserviceprovidingavalanchewarnings.
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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.
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Thepartiallycoherentapproachalsoenablesustoexamineinterfaceroughnesseffectsbyapplyingafullwavesmallperturbationmethoduptosecondorder(SPM2)tothemulti-layeredroughnessscatteringproblemwithinthesameblock.TheSPM2hastheadvantageofconservingenergy.Wereportnumericalresultsincheckingenergyconservationandillustratetheangularandpolarizationcouplingeffectsarisingduetointerfaceroughness.
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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.
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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.