AqGSFC AqJPL AqNSIDC SmosBEC Sea Surface Salinity ... · PDF filePathfinder for NASA’s...
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Sea Surface Salinity Distributions in the Arctic from Aquarius and SMOS Josefino C. Comiso, Cynthia Eidell, Emmanuel Dinnat and Ludovic Brucker Cryospheric Sciences Laboratory, Code 615, NASA/GSFC Study of sea surface salinity distributions in the Arctic region is important because of strong relationships with the sea ice cover, ocean circulation and primary productivity. Data from four different sources have been compared and the results show general agreement with some biases associated with differences in retrieval, filtering, masking and smoothing techniques. Despite challenges in obtaining accurate salinities at cold temperatures the products are consistent with in situ data with RMS error as low as 0.3 psu . Overall, the spatial distribution as well as seasonal and inter-annual variations are coherent with expected changes in sea ice cover, precipitation and river runoff. The products show strong potential for many oceanographic applications and climate change studies. Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics Sep 27 to Oct 4, 2012 45E Sep 27 to Oct 4, 2012 45E Sep 27 to Oct 5, 2012 45E Sep 27 to Oct 6, 2012 45E Early Autumn Season AqGSFC AqJPL AqNSIDC SmosBEC 39 38 37 36 35 34 33 32 31 30 29 28 27 26 25 psu A S O N D J F MAM J J A S O N D J F MAM J J A S O N D J F MAM J J A S O N D J F MAM J d 2011 2012 2013 2014 2015 0 10 20 30 40 50 60 70 80 90 100 30.0 30.5 31.0 31.5 32.0 32.5 33.0 33.5 34.0 34.5 35.0 35.5 36.0 A S O N D J F MAM J J A S O N D J F MAM J J A S O N D J F MAM J J A S O N D J F MAM J Axis Title 2011 2012 2013 2014 2015 2011 2012 2013 2014 2015 (c) Western Arctic Basin, lat > 65°N, 0°E < lon < 180°E 0 10 20 30 40 50 60 70 80 90 100 30.0 30.5 31.0 31.5 32.0 32.5 33.0 33.5 34.0 34.5 35.0 35.5 36.0 ASONDJFMAMJJASONDJFMAMJJASONDJFMAMJJASONDJFMAMJ Axis Title 2011 2012 2013 2014 2015 (d) Eastern Arctic Basin, lat > 65°N, 0°W < lon > 180°W Sea Ice Concentration (%) AqGSFC AqJPL AqNSIDC SmosBEC Figure 1 Figure 2
Transcript of AqGSFC AqJPL AqNSIDC SmosBEC Sea Surface Salinity ... · PDF filePathfinder for NASA’s...
SeaSurfaceSalinityDistributionsintheArcticfromAquariusandSMOS
Josefino C.Comiso,CynthiaEidell,EmmanuelDinnat andLudovic BruckerCryospheric SciencesLaboratory,Code615,NASA/GSFC
StudyofseasurfacesalinitydistributionsintheArcticregionisimportantbecauseofstrongrelationshipswiththeseaicecover,oceancirculationandprimaryproductivity.Datafromfourdifferentsourceshavebeencomparedandtheresultsshowgeneralagreementwithsomebiasesassociatedwithdifferencesinretrieval,filtering,maskingandsmoothingtechniques.DespitechallengesinobtainingaccuratesalinitiesatcoldtemperaturestheproductsareconsistentwithinsitudatawithRMSerroraslowas0.3psu .Overall,thespatialdistributionaswellasseasonalandinter-annualvariationsarecoherentwithexpectedchangesinseaicecover,precipitationandriverrunoff.Theproductsshowstrongpotentialformanyoceanographicapplicationsandclimatechangestudies.
Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics
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Figure1 Figure2
Name:Josefino C.Comiso
E-mail:[email protected]:301-614-5708
References:Eidell,C.G.,J.C.Comiso,E.Dinnat,andL.Brucker,(2017)SatelliteobservedsalinitydistributionsathighlatitudesintheNorthern
Hemisphere:Acomparisonoffourproducts,JournalofGeophysicalResearch:Oceans,122,doi :10.1002/2017JC013184.Comiso,J.C.,C.L.Parkinson,R.Gersten,andL.Stock,2008:AccelerateddeclineintheArcticseaicecover,Geophys.Res.Lett., 35,
L01703,doi:10.1029/2007GL031972.Brucker,L.,E.P.Dinnat,andL.S.Koenig.(2014),WeeklygriddedAquariusL-bandradiometer/scatterometer observationsandsalinity
retrievalsoverthepolarregions––Part1:Productdescription,Cryosphere,8,905–913.
DataSources:AquariusdatawereprovidedbyNationalSnowandIceDataCenter(NSIDC),JPL/PODAAC,andGSFC/DAACwhileSMOSdatawereprovidedbytheESA/SMOSBarcelonaExpertCentre.AncillarydatawereprovidedbytheCopernicusMarineEnvironmentMonitoringService(CMEMS)andtheCarbonDioxideInformationAnalysisCenter(CDIAC).
TechnicalDescriptionofFigures:TheimagesandplotsarepartsofFigures10and11ofEidell etal(2017)paper.
Figure1:Seasurfacesalinitydistributions(SSS)fromfourproducts:AqGSFC,AqJPL,AqNSIDC andSmosBEC.ThedistributionsaregenerallyconsistentwiththeAtlanticOceanregionmoresalinethanthePacificOceanregionasexpectedbecauseofobservedprecipitationpatterns. IntheArctic,thesurfacesalinityisconsiderablylowerbecauseoffresheningprimarilyfromseaicemeltandriverrun-off.AqJPL andSmosBEC showslessopenoceanareabecauseoftheuseoftheAquariusicemaskwhichdetectsmoreicecoverthanthevalidatedseaicedataprovidedmySSM/IandAMSR2data(seeredlineforiceedge)usedbyAqGSFC andAqNSIDC products.Thegreencontourlinesfor32psu areshowntoillustratethedifferencesofthedifferentproductsintheopenocean.
Figure2:MonthlyaveragesofsurfacesalinityintheWesternandEasternpartoftheArcticbasinfrom2011to2015areshowntobeseasonalwithsalinitiesrangingfromabout30psu to35psu.Theunusuallylowsalinityin2012isassociatedwiththerecordlowperennialicecoverobservedduringtheyear.TheuseofinaccurateseaicemaskcausessignificantdifferenceinthelowvaluesandabnormalvaluefortheAqJPL dataduringthesummerandautumnin2014.
Scientificsignificance,societalrelevance,andrelationshipstofuturemissions:Salinityisakeygeophysicalparameterthataffectsoceancirculation,hydrologicalcycle,oceanecologyandclimate.Togetherwithtemperature,itdrivesthethermohalinecirculationoftheocean,whichinfluencesthetransportofheat,energyandhumiditytherebymodulatingclimate.ThisstudydemonstratesthatthesatellitesalinityproductsintheArcticregionthatarecurrentlyavailablehavesignificantdifferencesbutthedifferencescanbeminimizedthroughimplementationofthesamesea icemask,filteringandsmoothingtechniquesaswellasoverallqualitycontrol.ThespatialdistributionofSSSintheArcticasderivedfromtheproductsarecredibleandconsistentwithinsitudataaswellaswithprecipitationpatterns,riverrun-offandthemeltoftheseaicecover.
Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics
Quantifying Ice Sheet Surface MeltingD Harding, P Dabney
NASA GSFC, Biospheric Sciences Laboratory
Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics
TheextentandrateofGreenlandIceSheetsurfacemeltingisincreasinginresponsetowarmingtemperatures.Passivemicrowave,thermalandreflectancesatelliteremotesensinghavetraditionallybeenusedtomonitortheextentofsurfacemelt,butcalibratingthoseobservationstodeterminetheamountofliquidwaterpresentatthesurfacehasbeenchallenging.Anairbornemulti-channellaseraltimeter,SIMPL,acquiresauniquesetofmeasurementsthatcanserveasacalibrationmethod.InthisexampleacrossthemeltzoneinnorthwesternGreenland,theratiobetweentwopolarizationstatesofreflectednear-infraredlaserenergydecreasesasthefractionalcoverofliquidwaterincreases.Thisisduetochangesinlightscatteringpropertiesaswaterconvertsfromthefrozentoliquidstate.
Name:DavidHarding,Biospheric SciencesLaboratory,NASAGSFC
E-mail:[email protected]:301-614-6503
References:Brunt, K.M, T.A.Neumann, and T. Markus, 2015, SIMPL/AVIRIS-NG Greenland 2015 Flight Report, NASA/TM–2015-217544, 23 pp.Yu, A. W., D. J. Harding and P. W. Dabney, 2016, Laser transmitter design and performance for the Slope Imaging Multi-Polarization Photon-Counting Lidar (SIMPL) instrument, Solid State Lasers XXV: Technology and Devices, Proceedings of SPIE 9726 [10.1117/12.2213005].Harding, D. J., P. Dabney, S. Valett, et al. 2011. Airborne Polarimetric, Two-Color Laser Altimeter Measurements of Lake Ice Cover: A Pathfinder for NASA’s ICESat-2 Spaceflight Mission. 2011 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 3598-3601.Dabney, P., D. Harding, and 13 others, 2010, The Slope Imaging Multi-polarization Photon- counting Lidar: Development and performance results, Geoscience and Remote Sensing Symposium (IGARSS), IEEE International (25-30 July 2010): 653-656, [10.1109/IGARSS.2010.5650862]
DataSources:AirborneSlopeImagingMulti-polarizationPhoton-countingLidar(SIMPL)(D.Harding,PI;P.Dabney,InstrumentScientist)andvisibleframecameraimagesacquiredonAugust3,2015duringthe2015SIMPL/AVIRIS-NGGreenlandCampaign,sponsoredbytheICESat-2Project ScienceOffice,andaLandsat8naturalcolorvisibleimage(OLIBands4,3and2asRGB)acquiredonthesameday.
Figure1: Laserreflectancedepolarizationsensitivitytotheamountoficesheetsurfacemeltwater.
TechnicalDescriptionofFigures:Thetopleftpanelshowsa20kmlongSIMPLflightpath(redline)andmosaicofframecameraimagesinNorthwestGreenlandsuperimposedonaLandsatimagewhichhasbeenscaledtoemphasizethedistributionofsurfacemelt(darkerblues).Thepathcrossesareasofstandingwater,runoffandchannelizedflowandameltlake.ThebottomleftpanelshowsSIMPLsignalamplitudesfor1064nm(NIR)laserretro-reflectanceforphotonswithpolarizationstatesparallelandperpendiculartotheplane-polarizedtransmitenergy.Italsoshowstheratiobetweentheseamplitudes.Photonsreflectedfromwaterretainthesamepolarizationstateasthetransmittedenergy(partallel)whereasreflectionsfromsnowandiceundergomultiplescatteringwhichconvertssomeoftheenergytotheperpendicularstate.Therefore,asthefractionalcover of wateratthesurfaceincreasesthedepolarizationratiodecreases.Therightpanelsareenlargementsofa3kmsegment(thepathcurvatureisdue to aircraftroll).
Scientificsignificance,societalrelevance,andrelationshipstofuturemissions:TheincreasingmeltingoftheGreenlandicesheetsurfaceisacceleratingtheamountofwaterdischargetotheoceanandtheresultingincreaseinsealevelrise.Inaddition,thepresenceofsurfacewaterdarkensthealbedocausingincreasedabsorptionofsolarenergyandaconsequentwarmingofthesurface,establishingapositivemeltfeedbackloop.Thisworkisestablishingamethodtocalibratesatelliteremotesensingmeasurementsofmeltamount.ItalsoprovidesafoundationforinterpretingthelidarmeasurementsoftheGreenlandandAntarcticicesheetmeltingtobemadebytheICESat-2missionbeginningin2018.
Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics
EvidenceforaLowBulkCrustalDensityforMarsS.Goossens1,2,T.J.Sabaka3,A.Genova1,4,E.Mazarico1,J.B.Nicholas3,5,G.A.Neumann1
1PlanetaryGeology,GeophysicsandGeochemistryLab,NASAGSFC,2Univ.ofMaryland,BaltimoreCounty,3GeodesyandGeophysicsLab,NASAGSFC,4MIT,5Emergent
Theeffectivedensityspectrum(definedastheratioofmeasuredgravityandgravityascomputedfromtopography)forvariousMarsgravitymodels,usingastandardconstraint(GMM-3)orournewconstraint(RM1models)(figure1),andanexampleofhowdensitymayvarylaterallyintheMartiancrust,obtainedbylocalizationofournewgravitymodel(figure2).Ourconstraintresultsinastablespectrumathighdegreesfromwhichtheaveragebulkcrustaldensitycanbedeterminedforthefirsttimedirectlyfromthesatellitetrackingdata.
Figure 1 Figure 2
Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics
Name:TerenceSabaka,GeodesyandGeophysicsLab,NASAGSFC
E-mail:[email protected]:301-614-6493
References:Goossens,S.,T.J.Sabaka,A.Genova,E.Mazarico,J.Nicholas,G.Neumann,2017.EvidenceforalowbulkcrustaldensityforMarsfromgravityand
topography.GeophysicalResearchLetters,44,pp.7686–7694,doi:10.1002/2017GL074172.
DataSources:RadiotrackingdatafromtheMarsGlobalSurveyor,MarsOdyssey,andMarsReconnaissanceOrbitermissions,aswellasatopographymodelderivedfromaltimetrydatafromtheMarsOrbiterLaserAltimeter.
TechnicalDescriptionofFigures:Theimagesshowtheeffectivedensityspectrum,andamap(inMollweide projectioncenteredontheprimemeridian)thatshowshowdensitiesinthecrustmayvarylaterally.Thismaphasbeenobtainedthroughlocalizationofournewlyderivedgravityfieldmodel.
Figure1:VariousgravitymodelsusingMarssatellitetrackingdata,withastandardconstraint(labelledGMM-3),orwithournewconstraint(labelledRM1)usingdifferentconstraintfactorsλ.
Figure2:Usingthe“RM1,λ=1”model,weappliedlocalizationusingsphericalSlepian functionstodeterminetheeffectivedensityspectrumatdifferentlocations,fromwhichweestimatedthecrustaldensityatthatparticularlocation.
Figure1istheratiobetweenagravitymodeldeterminedfromtrackingdataandagravitymodelcomputedfromagiventopography model,usingaunitdensity.Thisratiopersphericalharmonicdegreeindicatesthedensityinthecrust.Amodelwithastandardconstraint,suchastheGMM-3model,showsanunstableeffectivedensityspectrumathigherdegrees,andthecrustaldensitycannotbedeterminedreliably.Usingour newconstraint,thespectrumbecomesstable.Astheconstraintfactorλ increases,variationsintheeffectivedensitybecomelessuntil,inthelimit,onlytheaveragecrustaldensityvalueisestimated.Variationsintheeffectivedensityspectrumindicatelateraldensityvariations.ThroughlocalizationwithsphericalSlepianfunctionswedeterminedeffectivedensityspectraandestimatedlocalcrustaldensities.
Scientificsignificance,societalrelevance,andrelationshipstofuturemissions:Thecrustaldensityisanimportantgeophysicalparameterinstudiesofthesupportoftopography,thethermo-chemicalevolutionoftheplanet,andcrustalthickness.Thedensityofthecrustcanbeestimatedfromitseffectsonthegravityfield,butatthehigherdegrees(smallscalefeatures),modelsofthegravityareoftenpoorlydeterminedbecauseofnoiseandastandardspectralconstraintthatsuppressesthepower.Ournewconstraintusesinformationfromtopography,andresultsinamuchmorestableeffectivedensityspectrumfromwhichthecrustaldensitycanbedetermined.Thisconstraintcanbeappliedtoanybodyforwhichwehaveknowledgeofthegravityfieldandtopography,toimprovethedeterminationofcrustalproperties.
Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics
SMAP Improves Weather and Streamflow Prediction in North AmericaSMAP Early Adopter: Environment and Climate Change Canada, Stephane Belair, Marco Carrera
SMAP Applications Team: Peggy O’Neill /617, Vanessa Escobar /HQ BAH, Chalita Forgotson /SSAI
Assimilation of SMAP brightness temperature leads to substantial improvements in root zone soil moisture (RZSM) estimates compared to the use of screen-level data alone in the Canadian Land Data Assimilation System (CaLDAS) used for numerical weather prediction (NWP) over North America by Environment and Climate Change Canada (ECCC). Use of SMAP data in CaLDAS has resulted in a significant positive impact onNWP in North America and streamflow forecasts for the Lake Ontario basin.
The operational implementation of CaLDAS-SMAP is targeted for Spring 2018.
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Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics
ObservedModel w/o SMAPModel with SMAP
MEAN ERROR OF DEW POINT TEMPERATURE FORECASTS
CaLDAS-SVS-SMAPCaLDAS-screen
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2CROWE RIVER STREAMFLOW [GAUGE 02HK003]
Technical Point of Contact: Stephane Belair ([email protected])NASA contact: SMAP Applications Team (Peggy O’Neill /617, [email protected], 301-614-5773;
Vanessa Escobar /HQ, [email protected], 602-697-0832)
References: 1. SMAP Weather Focus Session Workshop, Moss Landing, CA (July 18, 2017). 2. Communication via E-mail
Data Sources: SMAP brightness temperature data; soil moisture data from the Alberta Ground Drought Monitoring Network (AGDMN) (17 sites), the US Climate Reference Network (USCRN) (38 sites), and the USDA Soil Climate Analysis Network (SCAN) (58 sites) for June-August, 2015. Data from streamflow experiment: streamflow estimates based on land surface analyses from CaLDAS (Canadian Land Data Assimilation System) with and without SMAP for June-August 2015; streamflow observations for the Lake Ontario basin.
Technical Description of Figures:Graphic 1: Unbiased Root-Mean-Square Error (ubRMSE) between in situ root zone soil moisture observations and CaLDASestimates from three different data assimilation experiments; (i) SCREEN -- assimilating only screen-level temperature and dew point temperature and SMAP brightness temperature (L1B_TB) data, (ii) ISBA: SMAP -- assimilating screen-level temperatures along with SMAP L1B_TB brightness temperatures with the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model, and (iii) SVS: SMAP, as in ISBA: SMAP but with the Soil, Vegetation, and Snow (SVS) land surface model. The three in situ networks used are the AGDMN, the USDA SCAN, and the USCRN.Graphic 2: Bias scores showing mean error as a function of forecast range for a series of 48-hour near surface dew point temperature forecasts for the July-August 2015 period over Canada. Experiment SCREEN is shown in blue, while SVS: SMAP is shown in red. Integrations were performed with initial conditions at 0000 UTC, corresponding to late afternoon and early evening over North America. The inclusion of SMAP TBs in the SVS model resulted in an overall warming and drying of the near-surface temperature, which improved (closer to 0) daytime bias scores for both temperature and dew point temperature.Graphic 3: Comparison of model estimates of streamflow for the Crowe River station within the Lake Ontario basin with and without SMAP data. The black line is for observed streamflow (Observed), the pale blue line is for results based on land surface analyses from CaLDAS but without SMAP (Model w/o SMAP), and the red line is for CaLDAS with SMAP (Model w/SMAP).
Scientific significance and societal relevance: Assimilation of SMAP L1B_TBs into the CaLDAS numerical weather prediction (NWP) system has been shown to improve soil moisture estimates with positive impacts on both temperature forecasts and hydrological (streamflow) forecasts. With further improvement of model physics and CaLDAS, the operational implementation of CaLDAS-SMAP will lead to improved and more reliable weather forecasts which help governments, businesses, and individuals to make better daily to long-term decisions.
Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics
AtmosphericCorrectionofHyperspectralOceanColorSensors
ApplicationtoHICOandEyesonPACE
AmirIbrahim,BryanFranz,ZiaAhmad,andKirkKnobelspiesse,OceanEcologyLaboratory,NASAGSFC
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Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics
Name:AmirIbrahim,OceanEcologyLab,NASAGSFC;USRAE-mail:[email protected]:301-286-2371
References:2017Ibrahim,A.,B.Franz,Z.Ahmad,R.Healy,K.Knobelspiesse,etal.(2017).Atmosphericcorrectionforhyperspectral
oceancolorretrievalwithapplicationtotheHyperspectralImagerfortheCoastalOcean(HICO),RemoteSensingofEnvironment.https://doi.org/10.1016/j.rse.2017.10.041
DataSources:MODISAquaandHICOprocessedbytheOceanBiologyProcessingGroup,OceanEcologyLaboratory(616),NASAGoddardSpaceFlightCenter.
TechnicalDescriptionofFigures:Figures(a)and(b)showthechlorophyllconcentrationmapintheChesapeakeBayretrievedusingtheoceancolorsoftware(SeaDAS)forHICOandMODIS-Aqua,respectively,whileFigure(c)isthetruecolorimage.Figure(d)showsthehyperspectralmarinereflectanceretrievedfromHICOandcomparedtothemulti-spectralMODIS-AquaretrievalsforthreestationsintheChesapeakeBay.Figure(e)showstheHICOinstrumentinstalledon-boardtheISS.
Scientificsignificance,societalrelevance,andrelationshipstofuturemissions:Sincespacebornemeasurementsoftheoceanareinterferedbytheatmosphereduetoscatteringandabsorptionbymolecules(gas)andaerosolswithinthebroadvisiblespectrumoflight,theatmosphericcorrectionprocessisnecessarytoaccuratelydeterminetheoceanreflectance.Thisworkdescribesthefirstoperationallyviablehyperspectraloceancolorremotesensingprocessingchainfromthetop-of-atmospheretooceanreflectanceandoceancolorproducts.ThenewlydevelopedhyperspectralatmosphericcorrectionalgorithmprovidesthebasisofoperationalcapabilityforNASA’sfuturePACEmission.Additionally,theHICOdataservesasaproxyinstrumenttotheOceancolorInstrument(OCI)ofPACEwhichallowsthedevelopmentoffutureoceancoloralgorithms.Thehyperspectralinformationinthemarinereflectanceisexpectedtoresolvethephytoplanktondiversityandrefineourknowledgeofthecarboncycleintheglobalocean.
Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics
