final report on eo datasets - earth2observe.euearth2observe.eu/files/public deliverables/d3.5 -...

ThisprojecthasreceivedfundingfromtheEuropeanUnion’sSeventhProgrammeforresearch,technologicaldevelopmentanddemonstrationundergrantagreementNo.603608

DGResearch–FP7-ENV-2013-two-stage

GlobalEarthObservationforintegratedwaterresourceassessment

FinalReportonEODatasets

DeliverableNo:D.3.5–DraftReportonEODatasetsRef.:WP3-Task1

Date:November2016

WP3 - Task 1 – D.3.5 Final Report on EO Datasets

DeliverableTitle D.3.5–FinalReportonEODatasetsFilename E2O_D3.5_Final_Report_EO_Datasets_v05.docxAuthors VincenzoLevizzani(CNR-ISAC)

WouterA.Dorigo(TUWien)

Contributors FilipeAires(Estellus)EmmanouilN.Anagnostou(KKTITC)NikolaosBartsotas(KKTITC)LukasBrodsky(GISAT)ElsaCattani(CNR-ISAC)DanielChung(TUWien)ChantalClaud(CNRS)RicharddeJeu(VUAmsterdamandDeltares)GeoffroyDetry(I-MAGE)MikeGrant(PML)SteveGroom(PML)MarketaJindrova(GISAT)LubosKucera(GISAT)SanteLaviola(CNR-ISAC)MichelLambotte(I-MAGE)AnnaCinziaMarra(CNR-ISAC)GianPaoloMarra(CNR-ISAC)FrankS.Marzano(Univ.RomaLaSapienza)IngeMelotte(I-MAGE)ThomasMelzer(TUWien)GonzaloMiguez-Macho(Univ.SantiagodeCompostela)MarioMontopoli(Univ.RomaLaSapienza)SaverioMori(Univ.RomaLaSapienza)EfthymiosNikolopoulos(KKTITC)GiuliaPanegrossi(CNR-ISAC)CathrinePrigent(Estellus)Jean-FrançoisRysman(CNRS)JaapSchellekens(Deltares)StefanSimis(PML)PhilipJ.Ward(VUAmsterdam)ClaudineWenhajiNdomeni(CNR-ISAC)RogierWesterhoff(Deltares)HesselWinsemius(Deltares)

Date 17/11/2016 PreparedundercontractfromtheEuropeanCommissionGrantAgreementNo.603608Directorate-General forResearch& Innovation(DGResearch),Collaborativeproject,FP7-ENV-2013-two-stageStartoftheproject: 01/01/2014Duration: 48monthsProjectcoordinator: StichtingDeltares,NL


Disseminationlevel

X PU Public

PP Restrictedtootherprogrammeparticipants(includingtheCommissionServices)

RE Restrictedtoagroupspecifiedbytheconsortium(includingtheCommissionServices)

CO Confidential,onlyformembersoftheconsortium(includingtheCommissionServices)

Deliverablestatusversioncontrol

Version Date Author

0.1 11/10/2016 WouterDorigo(TUWien)

0.2 04/11/2016 WouterDorigo(TUWien)

0.3 17/11/2016 VincenzoLevizzani(CNR-ISAC)

0.4 18/11/2016 JaapSchellekens(Deltares)-review

0.5 18/11/2016 VincenzoLevizzani(CNR-ISAC)


TableofContents1 Executive Summary ................................................................................................................................. 42 Introduction .............................................................................................................................................. 53 Precipitation ............................................................................................................................................. 6

3.1 CDRD and PNPR ......................................................................................................................... 63.2 183-WSL ....................................................................................................................................... 83.3 PREC_X-SAR ............................................................................................................................. 103.4 PRECIP/MR/DC ......................................................................................................................... 113.5 SM2RAIN_ASCAT ...................................................................................................................... 123.6 3B42 ........................................................................................................................................... 133.7 CMORPH .................................................................................................................................... 143.8 GSMaP_MVK ............................................................................................................................. 153.9 GSMaP_Gauge_RNL ................................................................................................................. 163.10 PERSIANN ................................................................................................................................. 163.11 TAMSAT ..................................................................................................................................... 173.12 RFE ............................................................................................................................................ 193.13 MSWEP ...................................................................................................................................... 20

4 Soil moisture .......................................................................................................................................... 214.1 ESA CCI Soil Moisture ............................................................................................................... 214.2 Sentinel-1A_SM .......................................................................................................................... 22

5 Evaporation and evapotranspiration ...................................................................................................... 235.1 GLEAM ....................................................................................................................................... 235.2 MOD16 ....................................................................................................................................... 24

6 Surface water ......................................................................................................................................... 256.1 GIEMS ........................................................................................................................................ 256.2 Surface water ............................................................................................................................. 27

6.2.1 Envisat ASAR-GM climatology 276.2.2 Landsat based 30-meter surface water mapping 27

6.3 Lake water level .......................................................................................................................... 297 Snow ...................................................................................................................................................... 31

7.1 Snow cover ................................................................................................................................. 317.2 Snowfall ...................................................................................................................................... 31

8 Water quality .......................................................................................................................................... 329 Water table depth in-situ observations .................................................................................................. 3310References ............................................................................................................................................ 3511Glossary ................................................................................................................................................. 38


ListofFigureFigure1ExamplesofprecipitationretrievalsoftheCDRD(left,SSMISoverpass,20Jan2012)andofthePNPR(right,Metop-AAMSU/MHSoverpass,6Jan2012)algorithms.ThesquareontheCDRDimageisblownupinthebottomplatewhereacycloneisdetectedbothbythealgorithmandbytheTRMMPrecipitationRadar.....................................7Figure2ExampleofprecipitationretrievaloverEuropewiththe183-WSLalgorithmofCNR.Thealgorithmidentifiesliquidandsolidprecipitation,watervapourcontentandalsosnowcoverattheground.Modulesareunderdevelopmentforsnowfallandhailfallretrievals................................................................................................................................................................9Figure3Top:schematicviewofthemodelusedtocomputethenormalizedradarcrosssection(NRCS)fromahorizontallyvariabletwo-layerprecipitatingcloud.Bottom:X-bandNRCS(indecibels)asafunctionofcross-trackscanningdistancex,showingenhancedvaluesontheleftofthecrossoverpointcausedbyscatteringfromthecloudtopandattenuationfromraininthelowercloudontheright.Thecloudtopisatzt,andthefreezingheightisz0,whereasthecloudwidthisw.Theviewing(incidence)anglewithrespecttonadirisθ,whereasthesurfacebackgroundNRCSisσ0.ΔrindicatesthewidthoftheslantedsliceofatmosphererepresentingtheSARside-lookingresolutionvolume(thisslantedslicealwaysincludestheground-rangesurfacepixel).[Marzanoetal.,2015;courtesyofIEEE]..........................................................................................................................10Figure44November2011.1strow:estimatedrainrate[mmh-1]byX-SAR(left)andWR(right).2ndrow:cumulatedprecipitation1h[mm]fromraingaugesandtheirposition.VerticalprofilesofX-SARattenuation[dB](3rdrow)andWRreflectivity[dBZ](4throw)forthetwocross-tracksectionsindicatedontoppanel(abscissasindicatelongitude[deg]).....................................................................................................................................................................11Figure515-16October2012.BrightnesstemperaturefromMSG-SEVIRI10.8μmchannelisshownasgreyshadingandsuperimposedarethepixelclassifiedasdeepconvection(green)andconvectiveovershooting(red).[Rysmanetal.,2015;courtesyofWiley]....................................................................................................................................................................12Figure6FlowchartoftheSM2RAINrainretrievalalgorithm.[Broccaetal.,2014;courtesyofWiley]............................................................................................................................................13Figure7Meanseasonalrainrate(mmday-1)averagedbetween1998and2010:JJA(a)andDJF(b).[Liu,2015;courtesyofElsevier].....................................................................................14Figure8CMORPH-derivedweeklyrainratefortheweekbetween3and10November2015.......................................................................................................................................................................15Figure9RainfallglobalmapfromGSMaP_MVKat0.1×0.1degresolutionon1July2005...................................................................................................................................................................................16Figure103-hourlyaccumulatedrainfallglobalmapfromPERSIANNon18November2015.......................................................................................................................................................................17Figure11TAMSAT daily rainfall estimates for 10 January 2013 (left) and 21 August 2013 (right). The development of the daily dataset provides valuable full spatial coverage historic (1983 – present) and near real-time estimates on daily rainfall occurrence and amount..........18Figure12NOAA CPC RFE rainfall estimate over Africa (mm) for 16 November 2015.......19Figure13Relative contributions of the gauge, satellite, and reanalysis components of MSWEP on an arbitrarily chosen day (25 April, 2006).......................................................................20Figure14Soil moisture climatology from the ECV_SM passive and active sensor dataset for the month of June over the period 1979-2010.........................................................................................21Figure15RepeatabilitytestsofSentinel1-Asoilmoisturealgorithmforavegetatedsurface(LAI>0.5)...........................................................................................................................................22Figure16Schematics of the GLEAM model (left) and annual evaporation for the year 2000 (right).....................................................................................................................................................................24Figure17MeanannualPenmanPET,compiledtomeanannualvalues(Westerhoff,2015).....................................................................................................................................................................24


Figure18Uncertainty(asastandarddeviation)ofmeanannualPenmanPET(Westerhoff,2015)..........................................................................................................................................25Figure19Globalsatellite-derivedinundationresultsoverthe1993–2000periodwitha773km2spatialresolution(i.e.,equalareagridof0.25°×0.25°attheequator).Frombottomtotop:(a)theannualmaximumfractionalinundationaveragedoverthe8years,(b)thevariabilityoftheannualmaximumfractionalinundation(standarddeviationofthemaximumoverthe8years),(c)themeanannualnumberofinundatedmonths,and(d)themostprobablemonthofmaximuminundation.[Prigentetal.,2007;courtesyofWiley]....................................................................................................................................................................26Figure20Comparisonofhistogrambuiltwiththeoldcode(left)andnewcode(right)overa1×1degreesavannahareainWestAfrica.Thenewcodereproducesthelowincidenceangles...............................................................................................................................................27Figure21ProcessingchainofLandsatandSRTMdataintoSurfaceWaterExtentmaps...................................................................................................................................................................................28Figure22LakewaterlevelretrievalmethodologybyGISAT.......................................................30Figure23ExamplesofsnowcovermappingthroughMODIS(left)andMSGSEVIRI(right)opticalsensors...................................................................................................................................31Figure24SnowstormasobservedbytheN18-MHSon30March2010,0312UTC.Fromtheupper-leftpanelclockwise:theNIMRODradarprecipitationrates,theNOAASF,the183-WSLSF,andthe183-SCM,whichisthemoduleofthe183-WSLmethodforcomputingthesnowcovermask..............................................................................................................32Figure25ImageofsimulateddepthtowatertableforAfrica.[courtesyofY.Fan,RutgersUniv.].....................................................................................................................................................................34


1 ExecutiveSummary

D3.5“FinalReportonEODatasets”asanoutputofTask3.1isthefinalversionofthecomprehensivereportonthedescriptionofremotesensingdatasetsavailablethroughtheprojectdatarepository.DataareavailableunderthefollowingmaincategoriesofobservationsoftheEarth’swatercycle:

• Evapotranspiration

• Sub-surfacewater(GroundwaterandSoilMoisture)

• Inundationextentanddynamics(currentlybeinguploaded)

• Precipitation

• Statevariables(Snowcover)

• Surfacewater

• WaterqualityDatasets are meant to support the activities of the various work packages of theproject, includingWP4,WP5,WP6.Theyare also available topotential external endusers since the project data portal is a one-stop repository, which can reveal verymuchinstrumentalforuserswhoneedvariousdatasetsatoncefortheirmodelruns,dataverificationdatavalidation,casestudiesonspecificcatchmentsorglobalstudies.


2 Introduction

Earth Observation (EO) datasets are available for relatively long time spans on thedifferent aspects of the terrestrial water cycle. Several satellite missions werelaunched that observe precipitation, soil moisture, evaporation, evapotranspiration,inundatedareas,seaandlakelevels,surfacewater,snowcoverandmuchmore.WhiletheLevel2geophysicaldatasetscreatedbythesemissionsarerelevantpersefortheadvancement of knowledge on the Earth water cycle, their usage has been all butmassivetodate.Thereisanoveralltendencytolimittheuseofthedatasetsinsidethescientificcommunitythatproducesthemandlittleefforthasbeendedicatedtomakeotherenduserawareoftheirpotential.The eartH2Observe project is gathering together a very diversified community ofscientists and enduserswhoare interested in the exploitationofEarthobservation(EO)datasets for theireverydayactivities suchas forexamplemeteorologists, earthmodellers, hydrologists, remote sensing specialists,watermanagement experts. Theidea is to come out with indications on the use of EO data, their potential andlimitations,butfromahand-onpointofviewwhichstemsfromtheactualuseofthedatasets.Thedatasetsareeither1)existingandwellknowndatasets,or2)newproductsbeingdevelopedwithintheproject.Thelatterareproducedbynewalgorithmsoverlimitedregions and case studies to improve the resolution and the reliability ofmonitoringtechniques of regional and global water resources. In some cases, the datasetsrepresentunprecedentednewandupdatedretrievalsofgeophysicalvariables.The delivery of datasets is part of the mandate of WP3 “Earth Observations -Combining and improving EO processing techniques”, which focuses on theimprovement of existing water cycle datasets and on the combination of multiplevariablesofthesameindicatorinasynergeticway.Datasets are available on the following water cycle variables: evaporation, groundwater, lake water level, precipitation (liquid and solid), snow cover, soil moisture,surfacewater,waterquality(lakes).The following satellite and sensors are used: GRACE, Cryosat-2, SMOS, ASCAT,EUMETSAT Polar System, Envisat ASAR Full Mission Archive, Envisat MERIS fullresolutionarchive,ERS-1/2FullMissionArchive,Sentinel-1NRTdata(whenready),COSMO-SkyMed X-SAR, TerraSAR-X and Tandem-X, Meteosat SEVIRI, MODIS FullMission Archive, LandsatTM, GPM, TRMM, Megha-Tropiques, AMSU-A/B, AMSR-E,AMSR-2, SSMIS, ATMS, MetOp-B, AVHRR, Topex/Poseidon, Jason-1 and 2, VIIRS,AATSRandSentinel-1,-2,-3satellites(ifavailable).Non-Europeansatelliteproductsare included intheprojectespecially in thosecaseswhere they improve the overall quality and accuracy of the end-products bycontributingtothecontinuouslyevolvingobservingconstellation.D3.5, asoutputofTask3.1, is the firstdraft versionof the reportdescribing theEOdatasetsoftheproject.Asuccessivereleaseonmonth34willincludearefinedversionofthedescriptionalsoincludingadvancesmadeavailableduringtheprojectwork.In the followingadescriptionof thevariousdatasets isprovideddivided inproductcategories.


3 Precipitation

Datasetsonprecipitationbelongtotwomaincategories:a)datasetsmadeavailablebyinstitutions not directly participating to the project, and b) datasets produced as adirecteffortoftheproject.Moreover,theyareeitherlocalorglobaldatasetsandtheirtimespanisofvariablelengthdependingontheavailabilityofthesatellitesensorsonwhichtheyarebased.

3.1 CDRDandPNPR

Datasetsofnewdesignweregeneratedbyexploitingnewsatellite algorithmsbasedonpassiveMWdevelopedatCNR,i.e.a)theCloudDynamicsandRadiationDatabase(CDRD),basedonaphysically-basedBayesianapproachappliedtoconicallyscanningradiometers(SSMIS)(Casellaetal.,2013;Sanòetal.,2013),b)thePassive-microwaveNeural-networkPrecipitationRetrieval(PNPR),designedtobeappliedtoPMWcross-track scanning radiometers AMSU-A andMHS (or AMSU-B) and exploiting a neuralnetworkapproach(Sanòetal.,2015).The CDRD and PNPR algorithms, originally created for application over Europe andtheMediterraneanbasin,wererecentlymodifiedandextendedtoAfricaandSouthernAtlantic for application to the MSG full disk area. This implied an upgrade of thesynthetic a-priori (or training) cloud-radiation database built from cloudmicrophysical profiles coupled to a radiative transfer model and dynamic-thermodynamic-hydrologic (DTH) model-derived variables, taking into accountAfricanprecipitatingevents,whichisusedinbothalgorithms,asaprioriinformationin the CDRD and as training dataset in the PNPR. Applied to different weatherconditions inEuropeandAfrica, thealgorithms showgoodperformanceboth in theidentificationof precipitation areas and in the retrieval of precipitation, particularlyvaluableovertheextremelyvariableenvironmentalandmeteorologicalconditionsoftheregion.Somecriticalissuesofthealgorithmsareconsidered.Anewsurfaceemissivityschemein the generation of the synthetic cloud-radiation database has been introduced:FASTEM-4(Liuetal.,2011)forocean,andTELSEM(Airesetal.,2011)forland.ThisismainlyrelatedtotheancillarydatausedinCDRDderivedfromtheECMWFanalysisat0.125° resolution. Moreover, a more sophisticated scheme for the treatment of theSSMIS pixels affected by land/sea transition has been introduced. A new detectionschemeofprecipitationoversemi-aridland(forAfrica)basedonCasellaetal.(2015)hasbeenimplementedinbothalgorithms.After thisanalysis, a completedatasetofprecipitation retrievals for theyears2011-2012-2013-2014 has been produced using both CDRD and PNPR algorithms. TheCDRDalgorithmreceivesas input theSSMISbrightness temperatureTBs(SDRdata)archived and publicly available from NOAA’s Comprehensive Large Array-DataStewardship System (CLASS - http://www.class.ngdc.noaa.gov), while the PNPRalgorithm receives as input theAMSU-A/MHSbrightness temperatureTBs (level 1Bdata converted to level 1C) archived and publicly available from NOAA’sComprehensive Large Array-Data Stewardship System (CLASS -http://www.class.ngdc.noaa.gov).Instantaneousprecipitation ratesare retrieved forawidearea coveringEuropeandAfrica(60°Sto75°Nlatitude,60°Wto60°Elongitude),foreachoverpassofSSMISandAMSU-A/MHS radiometers, respectively. The data are provided in netCDF 4 format.Eachfilecontainsdataforonefullorbit:rainfallrate(unitsmmh-1),phase,andqualityindexretrievedbyCDRDandPNPR.Outsidetheareaofinterest(60°Sto75°Nlatitude,


60°Wto60°Elongitude),thevaluesofrainfallrate,phaseandqualityindexaresettomissingvalue.Thespatialresolutionof theCDRDproduct isaround13.2×15.5km2whileforPNPRitvariesalongthescanoftheradiometerrangingfrom16×16km2atnadir to 26 × 52 km2. The dataset format and product characteristics are fullydescribedbyatextdescriptionfileprovidedwiththedata.Figure1showsanexampleofretrievalsusingthetwoalgorithmsfortwooverpassesoftheSSMISandMHSoverEuropeandAfrica.

Figure1ExamplesofprecipitationretrievalsoftheCDRD(left,SSMISoverpass,20Jan2012)andofthePNPR(right,Metop-AAMSU/MHSoverpass,6Jan2012)algorithms. The square on the CDRD image is blown up in the bottom platewhere a cyclone is detected both by the algorithm and by the TRMMPrecipitationRadar.

Thedataset created in the frameworkofE2Ohasbeenused for a verification studycarried out over the African region using the Tropical Rainfall Measuring Mission(TRMM) Precipitation Radar (PR) precipitation estimates (TRMM product 2A25) asground-truth. The goal of the study was the verification of the ability of the twoalgorithms to retrieve precipitation over Africa and the Southern Atlantic, and toverify the consistencyof theprecipitationestimates andpatternsderived fromverydifferent sensors, taking into account the different viewing geometry, spatialresolution, and channel frequency assortment of SSMIS and AMSU/MHS. All theanalyses were performed using coincident observations (within a 15-min time

RainRate

Rain

RainRate(m

m/h)


window)oftheTRMMPRwithobservationsfromSSMISandAMSU/MHSradiometersfor the three-year period 2011-2013. In this study, the co-located datasets ofradiometers/PR observations were divided into three classes depending on thebackgroundsurface–land,ocean,andcoast-andthelandclasswassubdividedintovegetated land and semi-arid land. The study has shown good abilities of thealgorithms to screen out the not precipitating areas over the different types ofbackgroundsurface,goodabilitytoretrievelighttomoderateprecipitationandheavyprecipitation, while there is a tendency not to correctly identify very lightprecipitation. Moreover, while both CDRD and PNPR have shown good correlationwith PR estimates, CDRD has quite similar performance for all the backgroundsurfaces,whilePNPRshowsadifferentbehaviourbetweenvegetatedlandandocean.With the aim of enlarging the precipitation product portfolio available in theframeworkofEartH2Observeproject,Version2.0(gridded)PMWprecipitationCDRDandPNPRdatasetsarealsoprovided,i.e.asagriddedinstantaneousprecipitationratebasedonPMWorbitalinstantaneousprecipitationrateestimates.Gridded PMW precipitation rates are obtained from CDRD algorithm for conicalscanningradiometersandPNPRalgorithmforcross-trackscanningradiometersfromall the available DMSP SSMIS and MetOp/NOAA AMSU/MHS overpasses. The newversionisprovidedonaregulargridat0.25°x0.25°resolutionovertheMSGfulldiskarea(LAT60°S-75°N,LON60°W-60°E).The data format is NetCDF. Each output file contains latitude, longitude andinstantaneousprecipitationrate.Thedimensiondefinitionisthefollowing:

• nlat:540(Numberof0.25°gridintervalsoflatitudefrom75°Nto60°S).

• nlon:480(Numberof0.25°gridintervalsoflongitudefrom60°Wto60°E).Theadvantageofdeliveringagridded(Version2.0)PMWprecipitationproductwithinEartH2Observeistwofold:

1. the regular grid helps using the dataset for hydrological applications and fordataassimilationinhydrologicalmodels;

2. theproductcanbeeasilycomparedtootherglobalrainfallgriddeddatasets.

3.2 183-WSL

The Water vapour Strong Lines at 183 GHz (183-WSL) algorithm conceived anddesignedatCNRexploits the threewatervapourabsorption frequenciesaround thebandat183.31GHzoftheAMSU-B/MHSsensorstoretrieveprecipitation.


Figure 2 Example of precipitation retrieval over Europe with the 183-WSLalgorithmofCNR.Thealgorithmidentifiesliquidandsolidprecipitation,watervapour content and also snow cover at the ground. Modules are underdevelopmentforsnowfallandhailfallretrievals.

The 183-WSL method is still under development even though the algorithmperformances in terms of precipitation detection skills and rain rate estimationaccuracy are by now documented in the literature (Laviola et al., 2013). Recentimprovements to theoriginalretrievalschemeof the183-WSLhavebeenappliedasresultof researchactivitycarriedoutat theNOAA’sCenter forSatelliteApplicationsand Research (STAR) and detailed in Laviola (2015). The investigation of varioushailstormeventswiththeMicroWaveCloudClassification(MWCC)methodhighlightsthe high sensitivity of themethod in identifying the large hail particles aloft clouds.TheMWCCisapassivemicrowavemethoddevelopedtocharacterizethecloudfieldintwomaincategories,stratiformandconvective,bydistinguishingtheobservedcloudsinthreedifferentevolutionlevelsforeachcategory.Theclassificationiscarriedoutbyanalysingtheperturbationinducedbycloudsonthenominalsignal(unperturbed)oftheMHSwater vapour channels. To approach the problem of hailstorm detection arigorousselectionofdeepconvectionpixelsasclassifiedby theMWCCwasmatchedwith hailstorm events. The results of the study allowed us to develop a prototypemethod for hailstorm based on a model of growth improved with the dynamiccarryingcapacity,whichcalculatestheprobabilityoccurrenceofhail intheobservedcloud.Theprototypehailmodel isalsonow included in the183-WSLcomputationalschemeandithasbeenvalidatedovertheUSbyusingtheNOAAofficialhailreportsasgroundtruth.Amethodfordetectionofsnowfallbasedonthefrequencyrange90-190GHzoftheAdvancedMicrowave Sounding Unit-B (AMSU-B) andMicrowaveHumidity Sounder(MHS)isnowunderimprovementandtestingasamoduleofthealgorithm.Thenewalgorithm is a prototype module of the 183-WSL retrieval scheme (Laviola andLevizzani, 2011) originally proposed in (Laviola et al., 2010). The previous studieshave shown the skills of the 183-WSL algorithm to discern different types ofprecipitation via the identification of various hydrometeor phases as themethod ishighlysensitivebothtoliquidandsolidhydrometeors(Laviolaetal.,2013).Although


theseparationbetweendifferentprecipitatingiceparticlesisdifficultespeciallyoverfrozensoils,thecurrentversionofthe183-WSLisimprovedwithaprototypesnowfallprocessor (183-WSLSF)working for land and openwater. Recently, the 183-WSLSFperformanceshavebeencomparedwithresultsoftheNOAASFmethod(Kongolietal.,2015) showing high capabilities in detecting snowfall areas. The 183-WSLSFprototypefromonehandrevealsasystematicoverestimationinthequantificationofverylightsnowfallrates(<0.5÷1.0mmh-1)fromtheotheritshowshighsensitivityinreproducingthepatternsofsnowcloudsasobservedbyradar(Laviolaetal.,2015).AnexampleofretrievalisprovidedinFigure2.

3.3 PREC_X-SAR

The group of the University of Roma “La Sapienza” (SUR) has devised a retrievalalgorithm from synthetic aperture radar (SAR) observations from the TerraSAR-X(TSX) and COSMOSkyMed (CSK) sensors. Archives are available for the 2009-2013periodovertheMediterraneanbasinandotherregionsofinterestoftheprojectupto2015.Marzano et al. (2010) have demonstrated the capability of X-band SAR (X-SAR)sensorstodetectrainfalloverbothseaandland,whichcanalsobeexploitedtocorrectSARimageryforrainfallattenuationeffects.TheoverallschemeofthemodelisshowninFigure3.

Figure 3 Top: schematic view of the model used to compute the normalizedradarcrosssection(NRCS)fromahorizontallyvariabletwo-layerprecipitatingcloud.Bottom:X-bandNRCS (indecibels)asa functionof cross-trackscanningdistancex,showingenhancedvaluesontheleftofthecrossoverpointcausedbyscattering from the cloud topandattenuation fromrain in the lower cloudontheright.Thecloudtopisatzt,andthefreezingheightisz0,whereasthecloudwidthisw.Theviewing(incidence)anglewithrespecttonadirisθ,whereasthesurface background NRCS is σ0. Δr indicates the width of the slanted slice ofatmosphererepresenting theSARside-lookingresolutionvolume(thisslantedslice always includes the ground-range surface pixel). [Marzano et al., 2015;courtesyofIEEE]

Afterthedatacollectionandprocessingforafistrelease,therefinementofaforwardmodel of SAR response to improve X-SAR retrieval algorithms for detection andestimationofrainfalloverland/seaispursued.Inparticular,theSARresponsemodel


hasbeenextendedtopolarimetricobservationsby includinga fulldescriptionofthecopolar correlation coefficient and the differential phase. These variables can inprinciple help for the background discrimination and improve the rainfall retrievalalgorithms.Thebackgroundofthescenecanincludenotonlyalandsurface(baresoil,vegetation), but also a roughwind-driven surface and this situationmakes the rainretrievalmorechallenging.Figure4showsanexampleofrainfallretrievalon4November2011.

Figure44November2011.1strow:estimatedrainrate[mmh-1]byX-SAR(left)andWR(right).2ndrow:cumulatedprecipitation1h[mm]fromraingaugesandtheir position. Vertical profiles of X-SAR attenuation [dB](3rd row) and WRreflectivity [dBZ] (4th row) for the two cross-track sections indicated on toppanel(abscissasindicatelongitude[deg]).

3.4 PRECIP/MR/DC

CNRSmakes use of the data provided by theAdvancedMicrowave SoundingUnit-B(AMSU-B) radiometer, replaced on recent platforms by the Microwave HumiditySounder(MHS),whichpermitaconcomitantobservationofconvection/precipitationoccurrence over land and sea at a temporal resolution higher than that ofmeteorological analyses and with a fine-scale spatial resolution (roughly 20 km).Brightness temperaturesmeasured since1999by channelsof theAMSU-BandMHSsensorsinthewatervapourabsorptionline(183-191GHz)indeedallowascreeningof precipitation over sea, land and coastal regions. For each satellite, the datasetconsists of twice-daily (a.m. and p.m.) and monthly maps of precipitation andconvectionoccurrenceonaglobal0.2°lat×0.2°longridfortheMediterraneanarea,i.e.25–60N,10W–50E.Theperiodcoveredsofargoesfrom2000to2012,makinguse of NOAA-15 to -19 and Metop-A and B satellites. Precipitation and convectionoccurrences are available separately for the different sensors. Compared to a firstversion developed following Funatsu et al. (2009), some changes have beenintroduced, with in particular the withdrawal of a test which was supposed to


discriminateprecipitatingsnowfromsnowonthegroundandwhichhasbeenfoundtoo conservative. Efforts are still in progress to improve the database for suchconditions.Lately, special emphasis has been put on the HyMeX SOP1 period for which a vastamount of observations are available (September-October 2012). A new diagnostichas been developed, COV (Convective Overshooting), which discriminates amongconvectivecloudsthose,whicheachthe lowerstratosphere(Figure5;Rysmanetal.,2015).In parallel, the dataset is gradually extended to the tropical regions, and theconsistencybetween thedifferent radiometers/satellites investigated.Theextensionof the database to ATMS (Advanced Technology Microwave Sounder) aboard NPPsatellites,whichwillreplaceMHSonsomeplatforms,isalsoinprogress.

Figure515-16October2012.BrightnesstemperaturefromMSG-SEVIRI10.8μmchannel isshownasgreyshadingandsuperimposedare thepixelclassifiedasdeep convection (green) and convective overshooting (red). [Rysman et al.,2015;courtesyofWiley]

3.5 SM2RAIN_ASCAT

SM2RAINisanovel“bottom-up”approachtorainfallestimationfromspace,whichbydoing “hydrology backward,” uses variations in soil moisture (SM) sensed bymicrowave satellite sensors to infer preceding rainfall amounts. It is developed byCNRandTUWieninajointeffort.TheSM2RAINalgorithmisbasedontheinversionofthesoilwaterbalanceequationfor retrieving rainfall from SM data. The soil is assumed to work as a natural raingaugeformeasuringtheamountofrainfallfallenintotheground.Specifically,thesoilwater balance equation for a layer depth Z [L] can be described by the followingexpression:Zds(t)/dt=p(t)_-r(t)–e(t)–g(t)wheres(t)istherelativesaturationofthesoilorrelativeSM,t[T]isthetimeandp(t),r(t),e(t),andg(t)[L/T]aretheprecipitation,runoff,evapotranspiration,anddrainagerate,respectively(Broccaetal.2014).Wheneveritrains,theevaporationratecanbesafely assumedasnegligible (e(t) =0).Moreover, by assuming that all precipitationinfiltrates into the soil, the runoff rate is zero (r(t) = 0). For the drainage rate, thefollowing relation may be adopted, g(t) = as(t)b, where a [L/T] and b [_] are twoparameters expressing the nonlinearity between drainage rate and soil saturation.Therearrangementofequation(1),withthedescribedassumptions,yieldsto


p(t)≅Zds(t)/dt+as(t)bThisequationcanbeusedforestimatingtheprecipitationratefromtheknowledgeofrelativeSM,s(t),itsfluctuationsintime,ds(t)/dt,andthreeparameters(Z,a,andb)tobe estimated through calibration. Even though the assumptions made for derivingequation (2) might introduce some errors, they allow obtaining a simple (buteffective)method for theretrievalof rainfall fromSMdata (Broccaetal.,2013) thatcanbeeasilyappliedonaglobalscale.The dataset is provided in kind to the project and makes use of the AdvancedSCATterometer(ASCAT).

Figure6FlowchartoftheSM2RAINrainretrievalalgorithm.[Broccaetal.,2014;courtesyofWiley]

3.6 3B42

Thepurposeofthe3B42algorithmistoproduceTropicalRainfallMeasuringMission(TRMM)mergedhighquality(HQ)/infrared(IR)precipitationandroot-mean-square(RMS) precipitation-error estimates. These gridded estimates are on a 3-hourtemporal resolution and a 0.25-degree by 0.25-degree spatial resolution in a globalbeltextendingfrom50degreesSouthto50degreesNorthlatitude.The 3B42 estimates are produced in four stages; (1) the microwave precipitationestimates are calibrated and combined, (2) infrared precipitation estimates arecreated using the calibrated microwave precipitation, (3) the microwave and IRestimates are combined, and (4) rescaling to monthly data is applied. Eachprecipitationfieldisbestinterpretedastheprecipitationrateeffectiveatthenominalobservationtime(Huffmanetal.2007).Thedatasetintheprojectrepositoryconsistsof3-hourlyanddailyrainfallestimatesfromv7ofthealgorithm.Foranoverviewofthedifferencesbetweenv6andv7ofthealgorithmseeLiu(2015)(sealsoFigure7).


Figure7Meanseasonalrainrate(mmday-1)averagedbetween1998and2010:JJA(a)andDJF(b).[Liu,2015;courtesyofElsevier]

3.7 CMORPH

The Climate Prediction Center (CPC) MORPHing technique (CMORPH; Joyce et al.,2004) produces global precipitation analyses at very high spatial and temporalresolution. This technique uses precipitation estimates that have beenderived fromlow orbiter satellite microwave observations exclusively, and whose features aretransportedviaspatialpropagationinformationentirelyobtainedfromgeostationarysatellite IR data. Precipitation estimates derived from passive microwave sensorsaboard the DMSP 13, 14 & 15 (SSM/I), the NOAA-15, 16, 17 & 18 (AMSU-B), andAMSR-EandTMIaboardNASA'sAquaandTRMMspacecraft, respectively.Note thatthis technique is not a precipitation estimation algorithm, but a means by whichestimates from existing microwave rainfall algorithms can be combined. Therefore,this method is extremely flexible such that any precipitation estimate from anymicrowavesatellitesourcecanbeincorporated.Withregardtospatialresolution,althoughtheprecipitationestimatesareavailableonagridwithaspacingof8km(attheequator),theresolutionoftheindividualsatellite-derivedestimates iscoarserthanthat-moreontheorderof12x15km2orso.Thefiner"resolution"isobtainedviainterpolation.In effect, IR data are used as a means to transport the microwave-derivedprecipitation features during periods when microwave data are not available at alocation. Propagation vector matrices are produced by computing spatial lagcorrelationsonsuccessiveimagesofgeostationarysatelliteIRwhicharethenusedtopropagate themicrowave derived precipitation estimates. This process governs the


movement of the precipitation features only. At a given location, the shape andintensity of the precipitation features in the intervening half hour periods betweenmicrowave scans are determined by performing a time-weighting interpolationbetweenmicrowave-derivedfeaturesthathavebeenpropagatedforwardintimefromthepreviousmicrowaveobservationandthosethathavebeenpropagatedbackwardintimefromthefollowingmicrowavescan.Werefertothislatterstepas"morphing"ofthefeatures.Figure8showsanexampleofweeklyprecipitationintensityderivedfromCMORPH.

Figure 8 CMORPH-derived weekly rain rate for the week between 3 and 10November2015.

TwoCMORPHproductswereprovided to theEOdataset catalogueof theproject innetCDFformat:

1. CMORPHdailyrainfallratesat0.25°spatialresolution(release1);

2. CMORPH3-hourlyrainfallratesat0.25°spatialresolution(release2).BothproductsweregeneratedatCPCwithCMORPHv1.0algorithmwith raingaugebiascorrection.

3.8 GSMaP_MVK

TheGlobalSatelliteMappingofPrecipitation(GSMaP)MVKproductproducesglobalprecipitation distribution with high temporal and spatial resolution. The techniqueusestheKalmanfiltertocomputetheestimatesofthecurrentsurfacerainfallratesateach0.1degreepixeloftheinfraredbrightnesstemperaturebytheGEO-IRsatellites.The filter predict the precipitation rate from the microwave radiometer and itsmorphedproductobtainedinasimilarwayastheJoyceetal.(2004),andthenrefinethepredictionbasedontherelationshipbetweentheIRbrightnesstemperatureandsurface rainfall rate. In No.3 DVD product, the backward process is introduced toproducetheglobalprecipitationmapas is thesame inNo.2.Therainrates fromthepassivemicrowaveradiometersaregeneratedbyAonashiandLiu(2000).SeeUshioetal.(2009)fordetails.


Figure9RainfallglobalmapfromGSMaP_MVKat0.1×0.1degresolutionon1July2005.

3.9 GSMaP_Gauge_RNL

A new version of the GSMaP hourly data set was provided during the EO datasetrelease2,computedatJAXA/EORCthroughtheversion6ofthealgorithm,i.e.thenewGPM-GSMaPalgorithm.Theprovideddata set is a reanalysis data set (not near realtime),where the rainfall estimates are adjustedwith the global gauge analysis (CPCUnified Gauge-Based Analysis of Global Daily Precipitation) supplied by NOAA.Further details about the GSMaP version 6 can be foundathttp://www.eorc.jaxa.jp/GPM/doc/algorithm/GSMaPforGPM_20140902_E.pdf.

3.10 PERSIANN

The Precipitation Estimation from Remotely Sensed Information using ArtificialNeuralNetworks(PERSIANN;Hsuetal.1997;Sorooshianetal.,2000)algorithmusesneural network function classification/approximation procedures to compute anestimateof rainfall rate at each0.25° x0.25°pixelof the IRbrightness temperatureimage provided by geostationary satellites. An adaptive training feature facilitatesupdatingof thenetworkparameterswhenever independentestimatesof rainfallareavailable. The PERSIANN systemwas based on geostationary infrared imagery andlater extended to include the use of both infrared and daytime visible imagery. ThePERSIANN algorithm used here is based on the geostationary longwave infraredimagerytogenerateglobalrainfall.Rainfallproductcovers50°Sto50°Nglobally.ThesystemusesgridIRimagesofglobalgeosynchronoussatellites(GOES-8,GOES-10,GMS-5,Metsat-6, andMetsat-7) provided by CPC,NOAA to generate 30-minute rainratesareaggregated to6-houraccumulatedrainfall.Modelparametersareregularlyupdatedusing rainfall estimates from low-orbital satellites, includingTRMM,NOAA-15,-16,-17,DMSPF13,F14,F15.TheprocessingstepsareasfollowsGlobal full resolution IR composites aredownloaded from theNCEPServerondailybasis,theseare15minute


1. TRMMlevel2A(TRMM-2A25)microwaveimagerdataaredownloadedtwiceadayalsowithtwodaysdelay.

2. The NOAA Satellite Active Archive system is polled every 30 minutes toacquire the most recent microwave-based precipitation estimate from 6additionalmicrowaveinstrumentsonboardNOAAK,L,andM(15,16and17,respectively) and DMSP 7,8 and 9. Because these data are available in real-near time, they are archived for use whenever the corresponding global IRcompositesbecomeavailable.

3. Each day, CHRS’s the Global IR data is used to produce the intermediate 30minutes4kmprecipitationproductwiththeneuralnetworkmodelistrainedusingallmicrowave-basedprecipitationestimatesavailableforthegivenday.

4. Theintermediateproduct isthenaggregatedto0.25°sixhourlyprecipitationmaps 3. And the product is released as provisional PERSIANN precipitationestimates.Atthesametime,24hr,3,5,7,10,15,and30-daytotalprecipitationproducts are also created for distribution over the HyDIS-GWADI (Link)Server.

Anexampleof3-hourlyaccumulatedrainfallfromPERSIANNfor18November2015isgiveninFigure10.

Figure 10 3-hourly accumulated rainfall global map from PERSIANN on 18November2015.

3.11 TAMSAT

TAMSAT stands for Tropical Applications of Meteorology using SATellite data andground-based observations, a dataset provided and maintained by the Dept. ofMeteorologyattheUniversityofReading.The TAMSAT research group have provided 10-day (decade) rainfall estimatesroutinelysincethelate1980sforAfricausingMeteosatthermalinfraredobservations.Rainfall is estimated under the assumption that the length of time the cloud toptemperature is below a predetermined threshold, known as the cold cloudduration(CCD),islinearlyrelatedtorainfall.The TAMSAT rainfall estimation algorithm is locally calibrated using historic raingauge records producing monthly and regional calibration parameters that areapplied historically and in real-time to provide an internally consistent rainfalldataset, known as the TAMSAT African Rainfall Climatology and Time-series(TARCAT) (Maidment et al., 2014), whichwas recently extended over Africa in thecontextofadroughtmonitoringprogramme(Tarnavskyetal.,2014).Thisovercomes


the need for near real-time rain gauge observations, which are typically sparse,unevenly distributed, and report sporadically. These estimates have been crucial,particularlyinfamineanddroughtearlywarningwherereliablenearreal-timerainfallestimatesprovidingfullspatialcoverageisessential.However,tomeettheincreasingdemand for rainfall estimates at finer temporal scales, theTAMSATResearchGrouphasdevelopedasimpleapproachdesignedtoproviderainfallinformationatthedailytime step (for example see Figure 11). Such information is vitally important fordroughtmonitoring,suchascapturingdryspellsduringthecropgrowingseason,andcanbemostbeneficialwhereveryfewornoraingaugeobservationsareavailable.OperationalTAMSATproductsare:

• daily and ten-daily (decadal) cumulative rainfall estimates issuedon the1st,11thand21stofthemonth;

• monthlycumulativerainfallestimates issuedonthefirstdayof thefollowingmonth;

• seasonal cumulative rainfall estimates issued on 1st March, 1st June, 1stSeptemberand1stDecember.

Alltheproductsareprovidedat4kmresolutionforthewholeofAfrica.TAMSAT rainfall estimates are derived from Meteosat thermal infra-red (TIR)channelsbasedontherecognitionofconvectivestormcloudsandcalibrationagainstground-basedraingaugedata.

Figure11TAMSAT daily rainfall estimates for 10 January 2013 (left) and 21 August 2013 (right). The development of the daily dataset provides valuable full spatial coverage historic (1983 – present) and near real-time estimates on daily rainfall occurrence and amount.


3.12 RFE

As of January 1, 2001, the CPC African Rainfall Estimates RFE version 2.0 (Xie andArkin,1996)hasbeenimplementedbyNOAA'sClimatePredictionCenter.ItreplacesRFE 1.0, the previous rainfall estimation algorithm thatwas operational from 1995through 2000. RFE 2.0 uses additional techniques to better estimate precipitationwhile continuing the use of cloud top temperature and station rainfall data thatformed the basis of RFE 1.0. Meteosat 7 geostationary satellite infrared data isacquired in 30-minute intervals, and areas depicting cloud top temperatures of lessthan235Kareusedtoestimateconvectiverainfall.WMOGlobalTelecommunicationSystem(GTS)datatakenfrom~1000stationsprovideaccuraterainfalltotals,andareassumed to be the true rainfall near each station. RFE 1.0 used an interpolationmethodtocombineMeteosatandGTSdatafordailyprecipitationestimates,andwarmcloud information was included to obtain decadal estimates. The two new satelliterainfall estimation instruments that are incorporated into RFE 2.0 are the SpecialSensor Microwave/Imager (SSM/I) on board Defense Meteorological SatelliteProgram satellites, and the Advanced Microwave Sounding Unit (AMSU). Bothestimatesareacquiredat6-hourintervalsandhavearesolutionof0.25degrees.RFE2.0obtains the finaldaily rainfallestimationusinga two-partmergingprocess, thensums daily totals to produce decadal estimates. All satellite data is first combinedusingamaximumlikelihoodestimationmethod,andthenGTSstationdataisusedtoremovebias.WarmcloudprecipitationestimatesarenotincludedinRFE2.0.

Figure 12NOAA CPC RFE rainfall estimate over Africa (mm) for 16 November 2015.


3.13 MSWEP

TheMulti-SourceWeighted-Ensemble Precipitation (MSWEP) retrospective isanewfullyglobalprecipitation(P)dataset(1979–2015)withahigh3-hourlytemporaland0.25° spatial resolution (Beck et al., 2016).The dataset is unique in that ittakesadvantage of awide range of data sources, including gauges, satellites, andatmosphericreanalysismodels,toobtainthebestpossiblePestimatesatglobalscale.The long-termmeanofMSWEP isbasedon the elevation-correctedCHPclimdataset(Funketal.,2015)butreplacedwithmoreaccurateregionaldatasetswhereavailable.Acorrection for gauge under-catch and orographic effects isintroduced by inferringcatchment-averagePfrom streamflow observations at 13,762 stations acrosstheglobe.The temporal variability of MSWEP isdetermined by weighted averagingofPanomalies from seven datasets; two based solely on interpolation of gaugeobservations (CPC Unified and GPCC), three on satellite remote sensing (CMORPH,GSMaP-MVK, and 3B42RT), and two on atmosphericmodel reanalysis (ERA-InterimandJRA-55).Therelativecontributionofthevariouscomponents isshowninFigure13.

Figure13Relative contributions of the gauge, satellite, and reanalysis components of MSWEP on an arbitrarily chosen day (25 April, 2006).

For each grid cell, theweight assigned to the gauge-based estimateswas calculatedfrom the gauge network density, while the weights assigned to the satellite- andreanalysis-based estimates were calculated from their comparative performance atthe surrounding gauges. Thequality ofMSWEPwas compared against four state-of-the-art gauge-adjusted P datasets (WFDEI-CRU, GPCP-1DD, TMPA 3B42, and CPCUnified) using independent P data from 125 FLUXNET tower stations around theglobe.MSWEPobtainedthehighestdailycorrelationcoefficient(R)amongthefiveP datasets for 60%of the stations and amedianR of 0.67 versus 0.44–0.59 for theother datasets.We further evaluated theperformance ofMSWEPusinghydrologicalmodelling for 9011 catchments (< 50000 km2) across the globe. Specifically, wecalibratedthesimpleconceptualhydrologicalmodelHBVagainstdailyQobservationswithPfromeachofthedifferentdatasets.Forthe1058sparsely-gaugedcatchments,representative of 83.9% of the global land surface (excluding Antarctica), MSWEPobtainedamediancalibrationNSEof0.52versus0.29–0.39fortheotherPdatasets.


4 Soilmoisture

4.1 ESACCISoilMoisture

Combining information derived from satellite-based passive and active microwavesensorshasthepotentialtoofferimprovedestimatesofsurfacesoilmoistureatglobalscale. TUWien has made available the soil moisture dataset from the ESA ClimateChangeInitiative(CCI)The ESA CCI soil moisture group has developed and evaluated a methodology thattakes advantage of the retrieval characteristics of passive and active microwavesatellite estimates to produce an improved soil moisture product (Liu et al., 2011,2012; Wagner et al., 2012; Dorigo et al., 2016). Level 2 soil moisture products,producedoutsidetheprocessingchainbyvariousdataproviders,areusedasinputtotheESACCISMproducts.Level2soilmoistureproductsfromallavailableactiveandpassive sensors are first mapped to a common daily time step, and then inter-calibrated and merged into an active-only (ACTIVE) and a passive-only (PASSIVE)dataset,respectively,whiletakingintoaccounttheirrelativeskill.Inasecondstep,thesystematic differences betweenACTIVE and PASSIVE are corrected bymatching foreachpixeltheircumulativedistributionfunctionagainstGLDAS-Noahlong-termlandsurface model soil moisture. In the final step, the rescaled ACTIVE and rescaledPASSIVEproductsaremergedintothecombinedactive-passiveproduct(COMBINED),again based on their error characteristics. A daily product is generatedwith a gridspacingof0.25°.Notice,thatthedataavailabilityofESACCISMvariesthroughspaceandtimeduetothevaryingspatialandtemporalavailabilityofthesingle-sensorLevel2inputproducts(seeDorigoetal.,2016fordetails).

Figure 14 Soil moisture climatology from the ECV_SM passive and active sensor dataset for the month of June over the period 1979-2010.

Whilethisthree-stepapproachimposestheabsolutevaluesofthelandsurfacemodeldataset to the final product, it preserves the relativedynamics (e.g., seasonality andinter-annualvariations)of theoriginalsatellitederivedretrievals.More importantly,the long-term changes evident in the original soil moisture products were alsopreserved. Themethod presented in this paper allows the long-term product to beextendedwith data from other current and future operational satellites. Themulti-


decadal blended dataset is expected to enhance our basic understanding of soilmoistureinthewater,energyandcarboncycles.Thesoilmoistureof themonthof June for theperiod1979-2010 isshown inFigure14.

4.2 Sentinel-1A_SM

A method was developed to estimate soil moisture from high-resolutionSentinel-1(A) Synthetic Aperture Radar (SAR) backscatter. The method usesmultiple data pixels from the Sentinel-1 image in a high resolution digitalelevationmodel, bywhich it overcomes the non-uniqueness problem,whichusually hampers most soil moisture estimation methods. The method iscurrentlytestedonaregionalinNewZealand,withthefirsttestsrunona1kmx1kmgridscale.DataisnotyetavailableontheeartH2Observeportal,as:themethodneedstobeoptimisedforfastapplicationonalarger(e.g.nationalNewZealand) scale; and the method is still tested for its quality at severalvegetation scenarios (example shown for a vegetated surface of LAI > 0.5 inFigure15).

Figure 15 Repeatability tests of Sentinel1-A soil moisture algorithm for avegetatedsurface(LAI>0.5).


5 Evaporationandevapotranspiration

5.1 GLEAM

Daily evaporation at 0.25 degree has been derived from the Global LandEvaporationAmsterdamModel (GLEAM;Miralles et al., 2011;Martens et al.,2016;gleam.eu)andmadeavailabletotheconsortiumbyVUA.Dailydatawithglobal coverage are available for the period 1980–2014 (see Figure 16 for aschematicsofthemodelandaanexampleoftheoutput).GLEAMisasetofalgorithmsthatseparatelyestimatethedifferentcomponentsof land evaporation (often referred to as 'evapotranspiration') includingtranspiration, interception loss, bare-soil evaporation, snow sublimation andopen-waterevaporation.Additionally,GLEAMcalculatessurfaceandroot-zonesoilmoisture,potentialevaporationandevaporativestressconditions.The rationale of GLEAM is to maximize the recovery of information aboutevaporation contained in current satellite observations of climatic andenvironmental variables. The Priestley and Taylor equation used in GLEAMcalculatespotentialevaporationbasedonobservationsofsurfacenetradiationand near-surface air temperature. Estimates of potential evaporation for thefractions of bare soil, tall canopy and short canopy within each pixel areconverted into actual evaporation using a multiplicative evaporative stressfactor based on observations of microwave Vegetation Optical Depth (VOD)and estimates of the root-zone soilmoisture. The latter is calculated using amulti-layerrunningwaterbalance.Totrytocorrectforrandomforcingerrors,satelliteobservationsofsurfacesoilmoisturearealsoassimilatedintothesoilprofile. Interception loss is calculated separately in GLEAM using a Gashanalyticalmodel.Finally,estimatesofactualevaporationforwaterbodiesandregions covered by ice and/or snow are obtained using the Priestley andTaylorequationwithaspecificcoefficientderivedfromliterature.ThreedatasetsareavailableinGLEAMversion3.0:

• V3.0a:availablefortheperiod1980–2014andcoversthewholeglobe.The dataset is based on reanalysis net radiation and air temperature,satellite-basedvegetationopticaldepth(VOD)andsoilmoisture,andacombination of gauge-based, reanalysis and satellite-basedprecipitation.

• V3.0bspanstheperiod2003–2015andisbasedsolelyonsatellitedata.Thedatasetcoverstheareabetween50°Nand50°S.

• V3.0cisavailablefor2011-2015andfortheareabetween50°Nand50°S. The dataset is based exclusively on satellite data, and uses soilmoistureandvegetationopticaldepth(VOD)fromSMOS.


Figure16Schematics of the GLEAM model (left) and annual evaporation for the year 2000 (right).

5.2 MOD16

EvapotranspirationasmeasuredbytheMODISsatellite(MOD16,Muetal.2011)wastestedonanation-widescale inNewZealand.Aconversionto thenationalstandard(PenmanPET)wasmade, includinganuncertaintyestimate(Westerhoff,2015).Thisconversion included a sensitivity and uncertainty assessment of both ground-observedandsatelliteestimatestoitsinputcomponents.Thenationalevapotranspirationdata(bothoriginalMOD16AETandMOD16derivedPenman PET) have been used in a national estimate of rainfall recharge togroundwater (see Figure 16 and 17). These data on their turn were used in anestimateofalong-termwater,accordingtoanEWT(Fanetal.2013)approach.

Figure 17 Mean annual Penman PET, compiled to mean annual values(Westerhoff,2015).


Figure 18 Uncertainty (as a standard deviation) ofmean annual Penman PET(Westerhoff,2015).

6 Surfacewater

6.1 GIEMS

Estellus has beenworking on the improvement of GIEMS (Global InundationExtentfromMulti-Satellite;Prigentetal.,2007)datasetbetween1993and2000(Figure19).Going from amonthly to a 10-day resolution is still under investigation. Validationneedstobedoneinordertochecktheimpactofthechangeofresolutionsincesignal-to-noise ratio increases with increased time resolution. Another important aspectconcernsthemissingvaluesandinparticularthecoastalareas.Thecoastalareasareveryimportantbecausemostoftheglobalpopulationlivesincoastalcities.However,the microwave satellite observations over land are contaminated by the oceanicsurfacesforcoastalpixels.Anextrapolationtoolwasdevelopedinordertofillinthesemissing values. It is based on the topographic information (i.e., the HydroSHEDSdataset).ThiscorrectedGIEMScoarseresolutiondatasethasbeenvalidated.Various downscaling approaches have been developed depending on the high-resolutioninformationthatisavailable(SatelliteVisible/InfraRedfromMODIS,activemicrowave from SAR, or topographic information from a Digital Elevation Model)(Airesetal.,2014).Wehavefocusedonthetopographyinformationbecausethishigh-resolution information (90m) is availableglobally (almostglobally forHydroSHEDSbecausedataisstillmissingoverlatitude60°N).Afloodabilityindexisbuilt,basedonthe distance to large, medium and small rivers and on difference of elevation withnearestriver.Thisfloodability indexisthenusedtodownscalethecoarseresolutionGIEMSdatasettowardsthehigh-resolution(90m).ThisnewdatasetiscalledGIEMS-D. Testswere conducted and the newGIEMS-Ddataset is available soon. Validationtestreconductedovervarioushydrologicalbasins.


Figure19Globalsatellite-derivedinundationresultsoverthe1993–2000periodwith a 773 km2 spatial resolution (i.e., equal area grid of 0.25° × 0.25° at theequator). From bottom to top: (a) the annualmaximum fractional inundationaveragedoverthe8years,(b)thevariabilityoftheannualmaximumfractionalinundation(standarddeviationofthemaximumoverthe8years),(c)themeanannual number of inundated months, and (d) the most probable month ofmaximuminundation.[Prigentetal.,2007;courtesyofWiley]


6.2 Surfacewater

Tomapsurfacewaterextentglobally,twodevelopmentswereconductedbyDeltares:software to process Synthetic Aperture Radar (SAR) data from Envisat has beendebuggedand restructured so that it canbeadaptedmoreeasily forother satellitesand data streams; and an algorithm to process Landsat imagery into global highresolution(30m)surfacewaterextentmapsisindevelopment.

6.2.1 EnvisatASAR-GMclimatology

Bugs were found in the histogram builder module that caused that certain lowincidenceangleswerewrongly interpretedandaddedtothewrong incidenceanglesinthehistogram.Thesebugswerefixed.AnexampleoftheeffectofthisbugisgiveninFigure20.Furthermore,someimagery fromaparticular imagecollectioncentrehadquite consistent and large geolocation errors. These images are ignored during thehistogramgenerationprocess.Comparison of histograms over different land cover types across Africa werecomparedtocheckifthedifferencesinlandcoverareapparentinthehistograms.Toenableclassification,histogramsareneeded thatdiscernwater from land.Anewwatermaskwith a far higher resolutionwas generated to enable inclusion ofmuchmorecellsinthewaterhistogram.Thiswatermaskcontainslandcells,landboundarycellsandfreshwaterandseawatercells.Atpresent,thealgorithmistestedforcompatibilitywithSentinel-1imagery.

Figure20Comparisonofhistogrambuiltwiththeoldcode(left)andnewcode(right) over a 1 × 1 degree savannah area in West Africa. The new codereproducesthelowincidenceangles.

6.2.2 Landsatbased30-metersurfacewatermapping

AlthoughtheimagecollectionfrequencyofLandsatismuchlowerthanEnvisat’s(16dayrevisittime),andLandsatdataisimpactedbycloudcover,mappingofpermanentwater bodies at unprecedented resolution is in principle possible. Until recently,globalmappingofsurfacewaterextentswithLandsatwouldhavebeenanextremelycumbersome job as thiswould requiredownloading, filtering and classifying ahuge


amount of data,whichwould cost a large amount of time toprocess and storage tosaveintermediateresults.Recently, Google launched the Google Earth Engine, which allows to performprocessingof large amountsof spatially and temporally variabledataon theGooglecloudforfree.Deltaresisestablishingamethodtoclassifyopenwaterusingafusionofalargestackof Landsat data and topography data from30m ShuttleRadarTopographyMission(SRTM).ThemethodisnowbeingtestedovertheMurrayDarlingbasin–Australia.Inshort,thestepsinclude:

• SelectwithinagivendomainallLandsatimages,reducedoveratimedomainchosen

• Based on spatially cloud cover frequency estimates, select a radiancepercentile in timeofallpixelswithin thechosendate-reducedcollectionthatshouldbecloudfreeandishighenoughtonotgeneratetoomuchcloudcover

• Compute the Normalized Difference Water Index (NDWI) from the chosenpercentile

• Compute a spatially variable threshold that distinguishes open water fromland using the NDWI. The threshold is determined by an unsupervisedclassificationmethodon a histogramofNDWI values that is based onpixelsthat are very certain to be open water, and surrounding land pixels. Thisthreshold is estimated at the sub-basin scale, using HydroBASINS (seehttp://hydrosheds.org/page/hydrobasins).

• Estimatewaterbyapplyingthespatiallyvariablethreshold.

• FusionwithSRTM30melevationdataisperformedasfollows:falsepositivesaregenerallycausedbyshadowintopographicallyhighlyvariableareas.A30m resolution topographymask has been generated by estimating theHeightAbove Nearest Drain (HAND) index from SRTM 30 m, that measures theverticaldistancefromacertainpointtoitsneareststream.Anyareasthathavea HAND value higher than a threshold are filtered out as they aretopographicallyhighlyunrealistictocontainopenwater.Thismethodisverysuccessfulinreducingfalsepositives.

Figure21ProcessingchainofLandsatandSRTMdataintoSurfaceWaterExtentmaps.


Themethod is fully coded in the Javascript API of Google Earth Engine, and is nowbeing used to assess the accuracy and completeness of open water bodies inOpenStreetMap and will be published in a peer-reviewed journal. The processdescribed above aswell asmethods that are used to compare Deltares openwaterestimateswithOpenStreetMapisoutlinedinFigure21.

6.3 Lakewaterlevel

Construction of EO derived data products and time series for deriving natural lakelevel data at a regional scale was carried out by I-MAGE. Topex/Poseidon, Envisat,Jason-1and2andSaralwereused.Datasetsconcernlakewaterleveltime-seriesderivedfromsatelliteradaraltimetryforLakeTana(Ethiopia)andfortheAfricanGreatLakes(EastAfrican)forthetimeperiodbetween1992and2014-2015-2016.Accordingtotheavailablealtimetrydata,7lakeshavebeenprocessed:Malawi,Mweru,Rukwa,Tana,Tanganyika,TurkanaandVictorialakes.LakeTanadatasetwaschosenespeciallygivenitsinterestfortheEthiopiancasestudy of the project. In addition, a studywas conducted on theDchar El Oued dam(Morocco) and awater level time-serieswas built using PISTACH data for the timeperiodbetween2008and2016.I-MAGE is establishing a method to generate long-term time series (20 years). Themethod is being tested over the 7 lakes and the dam mentioned above. ThemethodologyincludesthefollowingstepsalsooutlinedinFigure22:

• Determination of the study area: identification of the water area. Check thesatellitepassesoverthestudyareausingsatelliteorbitfiles.Thelengthofthetime-seriesisfixedaccordingtothemissionsthatcouldbeusedforthisarea.

• Downloadaltimetrydata(T/P,Envisat,Jason-1&2,Saral)fromdataproviderhub(NASA,ESA,CNES)basedonthedateandthenumberofgroundtrackthatcrossthestudyarea.Onefile/cycleisdownloadedforeachmission.

• RadarAltimetrydata processing: altimeter data is processedusing theBasicRadar Altimetry Toolbox (BRAT). Statistical parameters, measurement(altitude, dates, geophysical corrections) and computed data (DynamicTopography, Lake Level height, etc.) are extracted from each footprint(trackingpoint).Overthestudyarea,allGeophysicalDataRecord(GDR)filesofthesamealtimetrymissionareprocessedatonce.

• Storage and post-processing: the output files from BRAT are stored in adedicated spatial database (PostgreSQL/Postgis). Outliers are removed andthe tracking points completely within the lake are selected as qualifiedmeasurement. Several qualified measurements are available for one pass atthe same date. Then, the daily level height is computed by considering themedianofallaltimetervalues.Themedianbeingamorerobustestimator,lesssensitive to extreme values than average. Data is spatialized and can bevisualizedinaGISSoftware.

• Time-seriesproduction:dailymedianvaluesper cycle andaltimetrymissionare extracted from the database. These values are plotted on a graph thatshowsthegloballong-termtime-series.

• Validation/Comparison:thetime-seriesiscomparedtoexistingdatawhichcanbein-situdata(fromgaugereference)asthoseobtainedfortheLakeTana,otother time-series produced by other providers and available on the web(DAHITILagos,Hydroweb).


Asthepresenceofvariablelandcover(coastline, islands,sandbanks,etc.)affectsthereturned radar signal in altimetry measurement, I-MAGE analyzed the waveformshapewhichgivesinformationofthewaterbodysurfaceandtheinfluenceoftheothersurface aswell as othermeasurements likewind speedor surface roughness.UsingBRATtoolbox, thewaveformofeach trackcanbeclassified,displayedandanalyzed.Theshapeofthewaveformrepresentsatransitionfromlandtowaterandacomplexshapeshowstheinfluenceofanon-waterbodyarea.Then,thetrackswithcomplexornon-classified shapes are discarded from further processing (See Sulistioadi et al.(2015)).

Figure22LakewaterlevelretrievalmethodologybyGISAT.


7 Snow

7.1 Snowcover

AninvestigationintoEOsnowdatawasperformedbyGISATanddatafromacoupleofdifferent sensors was assessed. 8-day composite snow data from MODIS (Terrasatellite) are continuously being acquired and uploaded on the project’s FTP site,starting from themost recent and going backwards. Spatial coverage of theMODISdatacorrespondstotheMODISSinusoidalGrid(SIN),spatialresolutionis500mandtemporal resolution is8-daycomposite.MODIS8-daycompositesnowdatacontainsthe following information: Maximum Snow Extent Field and Eight Day Snow CoverField. An example datasets of daily snow data fromMODIS and fromMSG satellite(SEVIRI sensor) have also been uploaded and can be obtained for other periods ondemand.ExamplesarereportedinFigure23.

Figure 23 Examples of snow cover mapping through MODIS (left) and MSGSEVIRI(right)opticalsensors.

7.2 Snowfall

Amethodfordetectionofsnowfallbasedinthefrequencyrange90-190GHzoftheAdvancedMicrowave Sounding Unit-B (AMSU-B) andMicrowaveHumidity Sounder(MHS) isnowbeing improvedand testedbyCNR.Thenewalgorithm isaprototypemodule of the 183-WSL retrieval scheme (Laviola and Levizzani, 2011) originallyproposed in(Laviolaetal.,2010).CNRpreviousstudieshaveshowntheskillsof the183-WSLalgorithmtodiscerndifferenttypesofprecipitationviatheidentificationofvarioushydrometeorphasesasthemethodishighlysensitivebothtoliquidandsolidhydrometeors (Laviola et al., 2013). Although the separation between differentprecipitating ice particles is really difficult especially over frozen soils, the currentversion of the 183-WSL is improved with a prototype snowfall processor (183-WSLSF)workingforlandandopenwater.


Figure24SnowstormasobservedbytheN18-MHSon30March2010,0312UTC.Fromtheupper-leftpanelclockwise:theNIMRODradarprecipitationrates,theNOAASF,the183-WSLSF,andthe183-SCM,whichisthemoduleofthe183-WSLmethodforcomputingthesnowcovermask.

Recently,the183-WSLSFperformanceshavebeencomparedwithresultsoftheNOAASFmethod(Kongolietal.,2015)showinghighcapabilitiesindetectingsnowfallareas.The 183-WSLSF prototype on one hand reveals a systematic overestimation in thequantificationofverylightsnowfallrates(<0.5÷1.0mmh-1)fromtheotheritshowshigh sensitivity in reproducing the patterns of snow clouds as observed by radar(Laviola,2015;Laviolaetal.,2015)(seealsoFigure24).

8 Waterquality

PML has focused its dataset production activity on: a) Improvement of lake waterquality retrieval algorithms focusing on high coloured dissolved organic matter(CDOM) lakes inEstonia. b) Suggestions for improvementof the currentmonitoringcapabilities and use of the Sentinel 2 MSI and Sentinel 3 Ocean Land ColourInstrument(OLCI)satellites.ThemainobjectiveofthiseffortistoimprovethevalueofCDOMforuseinwatercyclemodelling. Current remote sensing algorithms poorly separate the dissolved andparticulatefractions.TheneedtoderiveCDOMisaddressed,andmoreimportantlyformodellingefforts,dissolvedCarbon,byevaluatingalargedatasetoflaketotalorganiccarbon (TOC) and any available CDOM absorption measurements against yellowmatterretrievalalgorithms.Adatasetof94,611 lakeTOCobservations in theperiod2002-2012 has been compiled from databases of the US EPA, Swedish and Finnishnationalmonitoringdatabases.Asmallpercentageoftheseobservationswillcoincidewith MERIS observations of the respective water bodies, which will be used toevaluate thewithin-lake retrieval accuracy of organic carbon.Optical in situ data to


furthersupportwaterqualityretrievalalgorithmdevelopmenthavebeengatheredfortheEstonianlaketestsites.In preparation for processing MERIS data for all test sites, the PML lakes clustercomputer has been upgraded to Hadoop version 2 to support the (on-going)integrationoftheDiversity-IIprocessorintothenewCaLimnosprocessingchain.

9 Watertabledepthin-situobservations

Byitsverynaturegroundwaterishiddenandrequiresbothgeologicalexplorationandthe drilling or digging ofwells before it can become a resource. Areas underlain bysimple stratiform sediments or lava flows present far less of a challenge than dogeological settings with complex structures or that are dominated by ancientcrystalline basement rocks. Time and again, however, crises in water supply arisefromdroughtorsuddendisplacementsofpopulationsagreatdealfasterthanthepaceofgroundwaterexplorationordevelopmentneededtocopewithshortages.Werethepotentialforsubsurfacesuppliesknownbeforehandreliefwouldbebothquickerandmoreeffectivethanitisatpresent.Miguez-Machoetal.(2008)havesimulatedtheclimatologicwatertabledepthat30-arc-sresolutionasconstrainedbyUGSGsiteobservationsoverNorthAmerica.Morerecently,Fanetal.(2013)madeastartinquantifyingtheavailabilityofgroundwaterworldwide. They have modelled how the likely depth of the water table may varybeneath the inhabited continents. As a first input they digitised over 1.5 millionpublished records of water table depths. Of course, that left huge gaps, even ineconomically highly developed areas. There is also bias in hydrogeological datatowards shallow depths as most human settlements are above easily accessiblegroundwater.To fill in the gaps and assess the deeper reaches of groundwater Fan et al. (2013)adaptedanexistingmodel thatassumesgroundwaterdepth tobe forcedbyclimate,topography and ultimately by sea-level. It is based on algorithms that predictgroundwater flowafter its infiltration fromthesurface.Suchanapproach leavesoutdrawdown by human interference and is at a spatial resolution that removes localcomplexities.Theinfluenceofterrainreliesonthenear-globalelevationdataacquiredbyNASA’sShuttleRadarTopographyMission(SRTM)inFebruary2000,resampledtoapproximately 1 km spatial resolution, supplemented by the less accurate Japan/USASTER GDEM produced photogrammetrically from stereo- image pairs. Other inputdataareassumptionsaboutvariationinhydraulicconductivity,whichisreducedtoasteady decreasewith depth,models of infiltration from the surface based on globalrainfallandevapotranspirationpatternsandthoseofsurfacedrainageandslopes.Noattemptwasmadetoinputgeologicalinformation


Figure25ImageofsimulateddepthtowatertableforAfrica.[courtesyofY.Fan,RutgersUniv.].

The results have been adjusted using actual water-table depths as a means ofcalibration across climate zones on all inhabited continents. The article itself is notaccessiblewithoutaSciencesubscription,butthesupplementarymaterialsthatdetailhowtheworkwasdoneareavailable to thepublic,and includeremarkablydetailedmapsofsimulatedwatertabledepthsforallcontinentsexceptAntarctica.Thedetailismuch influenced by terrain to create textures that override climate, whichmightsuggestthattheresultsflattertodeceive.Yetthemodellingdoesresultinvalleysandbroadbasinsofunconsolidatedsediment showingshallowerdepths that tallieswiththe tendency for less infiltrationwhere slopes are steep and run-off faster. The factthat the degree of fit betweenmodel and knownhydrogeology is high does suggestthat at the regional scale the maps are very useful points of departure for moredetailedworkthatbringsinlithologicalandstructuralinformation.ThedatasetismadeavailablebytheUniversityofSantiagodeCompostela.


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11 Glossary

AATSR AdvancedAlong-TrackScanningRadiometerAMSR-E AdvancedMicrowaveScanningRadiometer-EarthObservingSystemAMSR-2 AdvancedMicrowaveScanningRadiometer-2AMSU AdvancedMicrowaveSoundingUnitAPI ApplicationProgrammingInterfaceASAR AdvancedSyntheticApertureRadarASAR-GM AdvancedSyntheticApertureRadarGlobalModedataASCAT AdvancedSCATterometerATMS AdvancedTechnologyMicrowaveSounderAVHRR AdvancedVeryHighResolutionRadiometerCCI ClimateChangeInitiativeCDOM ColouredDissolvedOrganicMatterCDRD CloudDynamicsandRadiationDatabaseCESBIO Centred'EtudesSpatialesdelaBIOsphèreCLASS ComprehensiveLargeArray-DataStewardshipSystemCMORPH CPCMORPHingtechniqueCNR ConsiglioNazionaledelleRicercheCNRS CentreNationaldelaRechercheScientifiqueCONUS CONterminousUSCOV ConvectiveOvershootingCPC ClimatePredictionCenter(NOAA)CSK COSMOSkyMedDTH Dynamic-Thermodynamic-HydrologicvariableECMWF EuropeanCentreforMedium-rangeWeatherForecastsEGU EuropeanGeosciencesUnionEnvisat EnvironmentalSatellite(ESA)EO EarthObservationERS EuropeanRemoteSensingsatelliteESA EuropeanSpaceAgencyEUMETSAT EuropeanOrganizationfortheExploitationofMeteorological

SatellitesEWT EquilibriumWaterTabledepthFASTEM-4 FASTmicrowaveEmissivityModelGCOM GlobalChangeObservationMissionGDR GeophysicalDataRecordGGMN GlobalGroundwaterMonitoringNetworkGIEMS GlobalInundationExtentfromMulti-SatelliteGLDAS GlobalLandDataAssimilationSystemGLEAM GlobalLandEvaporationtheAmsterdamMethodologyGMI GPMMicrowaveImagerGPCC GlobalPrecipitationClimatologyCentreGPM GlobalPrecipitationMeasurementmissionGRACE GravityRecoveryandClimateExperimentGSMaP GlobalSatelliteMappingofPrecipitationGTS GlobalTelecommunicationSystemHAND HeightAboveNearestDrainHydroSHEDS HydrologicaldataandmapsbasedonSHuttleElevationDerivativesat multipleScalesHyMeX HydrologicalcycleinMediterraneanEXperimentICLAMS Integrated.ModellingSystem


IGRAC InternationalGroundwaterResourcesAssessmentCentreISAC IstitutodiScienzedell’AtmosferaedelClimaKKTITC KentroKainotomonTechnologionInnovativeTechnologyCenterLMD LaboratoiredeMétéorologieDynamiqueLPRM LandParameterRetrievalModelMERIS MEdiumResolutionImagingSpectrometerMetOp MeteorologicalOperationsatelliteMHS MicrowaveHumiditySounderMODIS ModerateresolutionImagingSpectroradiometerMSG MeteosatSecondGenerationMSI MultiSpectralInstrument(Sentinel2)MSWEP Multi-SourceWeighted-EnsemblePrecipitationMW MicroWaveMWCC MicroWaveCloudClassificationNDWI NormalzedDifferenceWaterIndexnetCDF networkCommonDataFormNOAA NationalOceanicandAtmosphericAdministrationNPP NationalPolar-orbitingPartnershipNRT Near-RealTimeOLCI OceanLandColourInstrument(Sentinel3)PDF ProbabilityDensityFunctionPERSIANN PrecipitationEstimationfromRemotelySensedInformationusing

ArtificialNeuralNetworksPM PersonMonthPML PlymouthMarineLaboratoryPMW PassiveMicroWavePNPR Passive-microwaveNeural-networkPrecipitationRetrievalPR PrecipitationRadarRAMS RegionalAtmosphericModelingSystemRFE RainFallEstimateSAR SyntheticApertureRadarSEVIRI SpinningEnhancedVisibleandInfraRedImagerSIN MODISSinusoidalgridSM SoilmoistureSMAP SoilMoistureActiveandPassivesatelliteSMMR ScanningMultichannelMicrowaveRadiometerSMOS SoilMoistureandOceanSalinitymissionSNR Signal-to-NoiseRatioSOP SpecialObservingPeriodSRTM ShuttleRadarTopographyMissionSSM/I SpecialSensorMicrowave/ImagerSSMIS SpecialSensorMicrowaveImagerSounderSTAR CenterforSatelliteApplicationsandResearch(NOAA)SUR SapienzaUniversityRomaTAMSAT TropicalApplicationsofMeteorologyusingSATellitedataandground- basedobservationsTB BrightnessTemperatureTELSEM TooltoEstimateLand-SurfaceEmissivitiesatMicrowavefrequenciesTMI TRMMMicrowaveImagerTMPA TRMMMulti-satellitePrecipitationAnalysisTOC TotalOrganicCarbonTRMM TropicalRainfallMeasuringMissionTSX TerraSAR-XTUWien TechnischeUniversitätWien


USC UniversidaddeSantiagodeCompostelaUSGS UnitedStatesGeologicalSurveyVIIRS VisibleInfraredImagingRadiometerSuiteVOD VegetationOpticalDepthVUA VrijeUniversityAmsterdamWCI WaterCycleIntegratorWP WorkPackage183-WSL WatervapourStrongLinesat183GHz183-WSLSF 183-WSL

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