University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Publications, Agencies and Staff of the U.S. Department of Commerce U.S. Department of Commerce 2012 Estimating Precipitation from WSR-88D Observations and Rain Gauge Data: Potential for Drought Monitoring Gregory J. Story National Weather Service Follow this and additional works at: hp://digitalcommons.unl.edu/usdeptcommercepub is Article is brought to you for free and open access by the U.S. Department of Commerce at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Publications, Agencies and Staff of the U.S. Department of Commerce by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. Story, Gregory J., "Estimating Precipitation from WSR-88D Observations and Rain Gauge Data: Potential for Drought Monitoring" (2012). Publications, Agencies and Staff of the U.S. Department of Commerce. 556. hp://digitalcommons.unl.edu/usdeptcommercepub/556
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University of Nebraska - LincolnDigitalCommons@University of Nebraska - LincolnPublications, Agencies and Staff of the U.S.Department of Commerce U.S. Department of Commerce
2012
Estimating Precipitation from WSR-88DObservations and Rain Gauge Data: Potential forDrought MonitoringGregory J. StoryNational Weather Service
Follow this and additional works at: http://digitalcommons.unl.edu/usdeptcommercepub
This Article is brought to you for free and open access by the U.S. Department of Commerce at DigitalCommons@University of Nebraska - Lincoln. Ithas been accepted for inclusion in Publications, Agencies and Staff of the U.S. Department of Commerce by an authorized administrator ofDigitalCommons@University of Nebraska - Lincoln.
Story, Gregory J., "Estimating Precipitation from WSR-88D Observations and Rain Gauge Data: Potential for Drought Monitoring"(2012). Publications, Agencies and Staff of the U.S. Department of Commerce. 556.http://digitalcommons.unl.edu/usdeptcommercepub/556
Published in Remote Sensing of Drought: Innovative Monitoring Approaches, edited by Brian D. Wardlow, Martha C. Anderson, & James P. Verdin (CRC Press/Taylor & Francis, 2012).
This chapter is a U.S. government work and is not subject to copyright in the United States.
Author:
Gregory J. StoryWest Gulf River Forecast CenterNational Weather ServiceFort Worth, Texas
281
12 Estimating Precipitation from WSR-88D Observations and Rain Gauge DataPotential for Drought Monitoring
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12.1 INTRODUCTION
Since its deployment, the precipitation estimates from the network of NationalWeather Service (NWS) Weather Surveillance Radars-1988 Doppler (WSR-88D)havebecomewidelyused.TheseprecipitationestimatesareusedfortheflashfloodwarningprogramatNWSWeatherForecastOffices(WFOs)andthehydrologicpro-gramatNWSRiverForecastCenters(RFCs),and theyalsoshowpotentialasaninputdatasetfordroughtmonitoring.However,radar-basedprecipitationestimatescan contain considerable error because of radar limitations such as range degra-dation and radar beam blockage or false precipitation estimates from anomalouspropagation(AP)oftheradarbeamitself.Becauseoftheseerrors,foroperationalapplications,theRFCsadjusttheWSR-88Dprecipitationestimatesusingamultisen-sorapproach.Theprimarygoalofthisapproachistoreducebothareal-meanandlocalbiaserrorsinradar-derivedprecipitationbyusingraingaugedatasothatthefinalestimateofrainfallisbetterthananestimatefromasinglesensor.
Thischapterbrieflydiscussesthepasteffortsforestimatingmeanarealprecipita-tion(MAP).Althoughtherearecurrentlyseveralradarandraingaugeestimationtechniques,suchasProcess3,MountainMapper,andDailyQualityControl(QC),thischapterwillemphasizetheMultisensorPrecipitationEstimator(MPE)PrecipitationProcessingSystem(PPS).ThechallengesfacedbytheHydrometeorologicalAnalysisandSupport(HAS)forecastersatRFCstoqualitycontrolallsourcesofprecipita-tiondataintheMPEprogram,includingtheWSR-88Destimates,willbediscussed.TheHASforecastermustdetermine in real time ifaparticular radar iscorrectlyestimating,overestimating,orunderestimatingprecipitationandmakeadjustmentswithintheMPEprogramsotheproperamountofprecipitationisdetermined.Inthischapter,wediscussproceduresusedbytheHASforecasterstoimproveinitialbestestimatesofprecipitationusing24hraingaugedata,achievingcorrelationcoeffi-cientsgreaterthan0.85.Finally,sinceseveralorganizationsarenowusingtheoutputofMPEforderivingshort-andlong-termStandardizedPrecipitationIndices(SPIs),thischapterwilldiscusshowspatiallydistributedestimatesofprecipitationcanbeusedfordroughtmonitoring.
The U.S. Drought Monitor (USDM), which is considered the current state-of-the-artdroughtmonitoringtoolfor theUnitedStates, ispresentlynotdesignedforcounty-scalerepresentations,yetitsoutputisusedbycustomersforcriticaldecisionmakingatthisspatialscale.Thus,droughtindicatorsareneededatthecountyandsubcountyscale.TheMPEestimatescanbeusedasa“goldstandard”precipitationproducttocomparewithorvalidateotherremote-sensingdroughtproducts,aslongas theuserunderstands theweaknessesofMPE.In thehandsofaknowledgeableuser,MPEprovidesinformationthatnootherexistingdroughttoolcanprovide.Withtheseproducts,wecanlookatdetailedrainfallpatternsandseehowtheycorrelate
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withevapotranspiration(ET)productsacrosslargeareas,aswellasidentifylocalizedareasofrainfalldeficitsovertime.Thesedatacouldalsoprovidehigher-resolutioninputsforremote-sensingdroughtindexformulationssuchastheVegetationDroughtResponse Index (VegDRI) (Brown et al., 2008). VegDRI currently integrates SPIgridsspatiallyinterpolatedfromAppliedClimateInformationSystem(ACIS)gaugedata,whichcharacterizebroadscaleprecipitationpatternsbutareoftenunrepresenta-tiveofcounty-scalelevelprecipitationvariations.Higher-spatial-resolution4kmMPEobservationsarenowavailabletoenhancethesetypesoftoolsandsupportlocal-scaledroughtmonitoringandearlywarningactivitiesthathavebeenidentifiedasaprioritybytherecentlyestablishedNationalIntegratedDroughtInformationSystem(NIDIS).
12.2 PAST EFFORTS IN DETERMINING MEAN AREAL PRECIPITATION
BeforeMPE,theRFCsonlyusedraingaugedatatocalculatebasin-averagedMAP,which is the average depth of precipitation over a specific area for a given timeperiod.Thisledtotimingandlocationerrorsintheidentificationofheavyrainfallevents,especiallyinahighlyconvectiveenvironmentwhereintenserainfalloftenoccursoversmallcoreareas.PrecipitationestimatesweregeneratedfromdiscreteraingaugeobservationsusingtheThiessenpolygonmethod.ThismethodattemptedtocalculateMAP,allowingforanonuniformdistributionofgaugesbyprovidingaweightingfactorforeachgauge.Inbasinswherenoraingaugesexisted,thismethodwasforcedtouseraingaugesthatwereoutsidethebasininquestionforitscalcula-tion.Althoughgauge-onlyanalysesexistfordroughtmonitoringintheUnitedStatesattheclimatedivisionscale(e.g.,the1monthaccumulatedprecipitationproductathttp://www.wrcc.dri.edu/spi/spi.html), these products are noisy, particularly in thewesternUnitedStateswheregaugedensityissparsewithonlyafewobservationsperclimatedivision.AndsinceolderradarsystemsdescribedinthenextsectiondidnothavethecomputeralgorithmsnecessarytoproduceMAP,RFCshadnochoicebuttousearaingauge–onlymethodology.
12.2.2 RadaR Rainfall estimatiOn befORe the WsR-88d
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storm intensities using digital video integrator and processor (D/VIP) levels. TheD/VIP levels were based on a predetermined value of returned power called theequivalentreflectivity,Z.AlookuptablewasusedtoestablishrainfallratesforeachD/VIP level.Radaroperatorswouldplaceadigitalgridover theplannedpositionindicator(PPI)radarscopeandmanuallywriteinavaluerangingfrom0to6thatrepresentedthemaximumD/VIPlevelineachgridcell.Therectangulargridcellsareknownasmanuallydigitizedradar(MDR)boxes,whicharebasedonasubgridoftheLimitedFineMesh(LFM)model.ThespatialresolutionoftheMDRgridcellwasapproximately40km.Bycontrast,theHydrologicRainfallAnalysisProject(HRAP)gridnowusedbytheWSR-88Dhasfurtherimprovedthespatialresolutionto∼4km.
After theradaroperatorsdeterminedthemaximumD/VIPlevel ineachMDRbox, they would transfer these values onto a paper overlay, which was usually acountyboundarymap.Asanexample,aD/VIPlevelof5meantthereturnedpowerfromtheechohadanequivalentreflectivityZofbetween50and57decibels(dBZ).Next theoperatorswould attempt todeterminehowmuch rainhad accumulated.Usingareflectivityrainfallratetable,thehourlyrainfallrateforthisvaluewouldbefoundtobe4.5–7.1in./hinaconvectiveenvironment.TheywouldthenvisuallyinspecttheD/VIPlevelsoverthepastfewhoursandaddtheD/VIPlevelstogetherforlonger-termrainfallestimatesforspecificcounties.Usingtheseearlymethods,considerableguessworkandmanualanalysiswasinvolvedinusingradartodeter-minetheamountofrainfall.
12.3 CURRENT ESTIMATION OF PRECIPITATION
12.3.1 RadaR: the WsR-88d PReciPitatiOn estimatiOn alGORithm
Estimatesfromradarhavebecomethebaseproductforderivingmeanareal,basin-averagedprecipitationwithintheNWS.AphotographofatypicalWSR-88DstationisshowninFigure12.1.TheprecipitationalgorithmintheWSR-88Dradarproductgenerator(RPG)iscomplex,andgivenallthefactorsinvolvedinradarsamplingandperformance,suchasproperradarcalibrationandassumptionsregardingradiowavepropagation through the atmosphere, errors in radar precipitation estimates oftenoccur.Theprecipitationalgorithmcontainsdozensofadaptableparametersthatcon-trolitsperformance(Fultonetal.,1998),improvingaccuracyoverearlierradaresti-mationmethods(PereiraFoetal.,1988).Thealgorithmitselfconsistsoffivemainscientific processing components (or subalgorithms) and an external independentsupportfunctioncalledtheprecipitationdetectionfunction(NWS/ROC,1999).Thefivescientificsubalgorithmsare(1)preprocessing,(2)determinationofrainfallrate,(3)determinationofrainfallaccumulation,(4)rainfalladjustment,and(5)generationofprecipitationproducts.Thefivesubalgorithmsareexecutedinsequenceaslongastheprecipitationdetectionfunctiondeterminesthatrainisoccurringanywherewithina230kmradiusoftheradar,whichisreferredtoastheradarumbrella.
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The reflectedpower returned to the radar (Z) is thenassigneda rainfall rate (R)usingaconversionknownasaZ/R relationship.As thevalueZ increases, theRestimate in inches per hour increases exponentially based on the Z/R equationemployed.Withinthisprecipitationratesubalgorithm,morequalitycontrolisper-formedusingatimecontinuitytest,aswellascorrectionsforhailandrangedegra-dation.Next,precipitationaccumulationsaredetermined through interpolationofscan-to-scanrainaccumulationwhilesimultaneouslyrunningclock-houraccumula-tions.Precipitationproductsarethengeneratedandupdatedwitheachvolumescan(NWS/ROC,1999).An important endproduct is thehourlyDigitalPrecipitationArray(DPA)productthatprovides1hestimatesofrainfallonthe4kmHRAPgriddiscussedearlier.TheseDPAsaretheoneoffourprimaryinputstotheMPEPPSprogram, a tool primarily used east of the Rocky Mountains, which will be dis-cussedlaterinSection12.4.
12.3.1.1 Problems with Radar-Based Precipitation EstimatesTheWSR-88Dprecipitationalgorithm isnotwithoutdeficienciesand limitations,whichalloperationalradarsexperiencewhenattemptingtoestimaterainfall.Manyfactorsthatmakeaccurateradarprecipitationestimatesdifficulthavebeenwelldoc-umented (WilsonandBrandes,1979;Hunter,1996).The following text isabriefdescriptionofsomeofthesefactorsandhowtheyaffectprecipitationestimates.
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(suchasachangeinactualtransmittedpower,orpathlossofthereturnedpowerbeforereachingthereceiversignalprocessorsincethelastoff-linecalibration)cancause significant changes in absolute calibration over time. Absolute calibrationneeds tobemaintainedbecauseachange inZof±4dBZwill result indoubling(orhalving)theestimatedRwhenthedefaultZ/Rrelationshipisused.Therefore,theWSR-88DRadarOperationsCenter(ROC)hasdevelopedabsolutecalibrationproceduresthataredesignedtoensurethatreflectivitydataareaccuratetowithin±1dBZ.
12.3.1.1.2 Proper Use of Adaptable ParametersAsmentionedearlier,severaladaptableparametershaveabearingontheprecipita-tionalgorithm, includingparametersdefining theZ/R relationshipand themaxi-mumprecipitation rate (MXPRA). In theWSR-88D, thedefaultZ/R relationshipis the convective Z = 300R1.4, and the default MXPRA is established at 53dBZ,whichequates toamaximumrainfall rateof∼104mm/h (4 in./h)when thecon-vectiveZ/Risemployed.ThisvalueofMXPRAwasestablishedtoeliminatetheeffects of hail contamination on rainfall estimates, as water-coated ice in cloudsreturnslargerreflectivityvaluesthanliquidwateralonewouldproduce.However,extremerainfallratesabovethedefaultMXPRAhavebeenshowntooccurwhena deep warm cloud layer exists and warm rain processes prevail, which is mostprevalentintropicalrainfallregimeswherelargerwaterdropsizediametersexist(BaeckandSmith,1998)andhailisabsent.Tocompensateforthis,radaroperatorshavetheoptionofusingadifferentZ/RrelationshipcalledtheRosenfeldtropicalZ/R (Z = 250R1.2). When the tropical Z/R relationship is employed, significantlymorerainfallisestimatedforreflectivitieshigherthan35dBZ(VieuxandBedient,1998).Forexample,theconvectiveZ/Rrelationshipyieldsarainfallrateof28mm/h(1.10in./h)whenZ=45dBZ,whilethetropicalZ/Ryieldsdoubletherainfallrateof56mm/h(2.22in./h).ThreeadditionalZ/RrelationshipshavebeenapprovedforusebytheROC:theMarshall–Palmerrelationship(Z=200R1.6)forwarmoraridclimateswhererainfalleventsaremostlystratiforminnatureandtwocool-seasonstratiform relationships (EastZ=200R2.0 andWestZ=75R2.0).RadaroperatorsmayalsochangetheMXPRAparametersothatahigherrainfallratewillbeusedintheprecipitationaccumulationfunctiontoamaximumof152mm/h(6.00in./h).Ingeneral,changesintheZ/Rrelationshiphavebeenshowntobeextremelyimportantinradarprecipitationestimation(Fournier,1999),whilechangesinMXPRAhavefarlessimpact.
Twootherimportantadaptableparameters(RAINAandRAINZ)controlwhenrainfall accumulations start and stop (Boettcher, 2006). Rainfall underestimationcanoccuriftheseparametersaresetsuchthataccumulationsbegintoolateand/orend too early. RAINA is the minimum areal coverage of significant rain with adefault settingof80km2.RAINZ is thedBZ threshold that represents significantrain (i.e., the levelof returnedpower forwhichyoudesire tobegin radar rainfallaccumulation)withadefaultsettingof20dBZ.WhenthereflectivitiesofechoesareatoraboveRAINZandthetotalarealcoverageofreturnsmeetsorexceedsRAINA,the precipitation algorithm will accumulate rainfall. If these parameters are notadjustedfortherainfalltypenotedonanygivenday,thiswouldhaveimplications
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12.3.1.1.3 Hail Contamination, Bright Band, Snow, and Subcloud EvaporationThepresenceoffrozenorwetfrozenprecipitationcancausesignificantlyenhancedreflectivity values (Wilson and Brandes, 1979). As hail stones grow in size, theybecome coated with water and reflect high amounts of power back to the radar,whichcanbesignificantlyhigherthanthepowerreturnedfromliquidprecipitationpresentwithin thestorm.Thehail-contaminatedhigherpowervalueresults inanoverestimationoftheprecipitationreachingtheground.Similarly,whenicecrystalsfallthroughthefreezinglevel,theiroutersurfacesbegintomelt.Thesewater-coatedicecrystalsalsoproduceabnormallyhighreflectivities,whichleadto“brightband”enhancement(thelayeroftheatmospherewheresnowmeltstorain)andanoveres-timationoftheprecipitation.
Snowflakesaresampledfairlywellbyradar,butimproperZ/RrelationshipscanleadtoanunderestimationofthesnowfallbytheWSR-88D.Asnowaccumulationalgorithm(SAA)hasbeenaddedusingamorerepresentativerelationshipbetweenreflectivityandfrozenprecipitation(Z/Srelationship,identicaltotheEastorWestcoolseasonstratiformZ/Rrelationship) to improve thewaterequivalentsnowfallestimates.Vasiloff(2001)andBarkeretal.(2000)providemoredetailedreviewoftheSAA.
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sorapidlywithheightwithinacloudthattheradarwillhaveahigherdegreeofunderestimationastheradarbeamincreasesinaltitude.Insuchrainfallevents,thebeamheightbecomesthelargestsinglecontributortoradarrainfallunderestima-tions.Last,instratiformraineventsandwithrainsfromthunderstormsthathavesmallverticalheight(usually20,000ftorless),arainfallunderestimationoccursduetotheradarbeamovershootingtheprecipitationatfarranges,whichisalackofdetectionproblem.Tocompensateforthis,theNWSsetuptheNEXRADradarnetworkwithaspatialdistributionofroughly300kmapart.Figure12.2showstheWSR-88D radar coverage area for theUnitedStates.Notice thatmany sectionsof the western United States are without adequate radar coverage, which leadstounrepresentativeprecipitationestimates.Thus,radar-andrange-dependentlowprecipitationbiasescanaccumulateover time, leading toanunderestimationofprecipitation and a depiction of drier conditions. Users should understand thisissuebeforeusingtheseestimatestoevaluatedroughtconditionsandotherinfor-mationalproducts.
12.3.1.1.5 Anomalous Propagation and Clutter SuppressionThe WSR-88D displays reflectivity returns at locations assuming the beam isrefractingnormallyinastandardatmosphere.Attimes,severedeviationsfromthestandardatmosphereoccurinlayerswithlargeverticalgradientsoftemperatureand/or water vapor. When these deviations occur, super-refraction of the radarbeam can result, and inaccurate calculations of actual beam height are made.Thesechangesinrefractionusuallyoccurinthelowertroposphereandcanleadtopersistentandquasi-stationaryreturnsofhighreflectivityeitherfromductingof the radar beam (where radio waves traveling through the lower atmospherearecurvedtoavaluegreater thanthecurvatureof theearth)orfromthebeamcomingincontactwiththeground(Chrismanetal.,1995).ThisAPcanleadtoextremeprecipitationaccumulationestimatesfromfalseechoes.TheWSR-88Ddoesemployacluttermitigationdecisionalgorithm,whichallowstheradaropera-tor to filter undesirable reflectivity returns, often from permanent targets neartheradar(Maddox,2010).However,thiscapabilitydependsontheradaropera-tor’sabilitytorecognizetheAPandinvokethealgorithm.Improperorexcessiveuseofclutterfilteringmaycauserealmeteorologicalechoestobeunnecessarilyremoved,leadingtorainfallunderestimation.ThisoccursmostfrequentlywhenrealrainfalltargetsareembeddedinornearareasofAP,whichiscommonbehinda lineof strong thunderstorms.Also, precipitation estimates fromnonmeteoro-logicaltargets(suchaswindfarms)arestillobservedonprecipitationproducts,ascertaintargetsthatexhibitmotionarenotremovedusingcurrentclutterfilteringtechniques.Figure12.3showsanexampleofAPacrossthesouth-centralUnitedStatescausedbysuperrefractionofthebeamsofseveralradars.Notethewide-spreadlightrainfallindicatedoverOklahomaandcentralanddeepsouthTexasandheavyrainovertheGulfofMexico.Norainfallwasactuallyoccurringatthistime.Forhydrologicapplications,thisfalserainfalliseliminatedbyconductingfurtherdataqualitycontrolexternaltotheWSR-88DandisperformedwithintheMPEPPSatRFCs.
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12.3.1.1.6 Beam BlockageBeamblockage isamajorproblemwhereradarsaresituatednearmountainsandisunavoidableinmanywesternU.S.locations.Forradials(portionsofthecircularscanoftheradaratasetelevationangle)withablockageofnomorethan60%intheverticaland2°orlessinazimuth,correctionsaremadetothereflectivitiesandareincreasedby1–4dBZintherangebinsbeyondtheobstacle,dependingonthepercentageoftheblockage.Manysiteshavebeamblockagesofmorethan60%andgreaterthan2°inazimuth,andthiscorrectioncannotbeapplied.Instead,theWSR-88Demploysaterrain-basedhybridscan(O’Bannon,1997),soradialsthatexperi-encethishighdegreeofbeamblockageusethenexthigherelevationslice(completescanoftheradaratasetelevationangle)forthePPSforthatradial(uptoamaximumelevationangleof3.4°,whichisthefourthelevationsliceaboveground).However,ifahigherelevationsliceisemployed,rangedegradationismorelikely,leadingtounderestimation of the precipitation. As a result, precipitation underestimation iscommonfromradars locatednearmountains.Theproblemhasbeenmitigatedatsomesitesbyinstallingradarsonapeak.However,inthissituation,thelowesteleva-tionslicesaresohighabovevalleysthatnear-surfaceprecipitationisnotdetected,which leads to theunderestimationof rainfall fromcloudsof lowverticalextent.Figure12.2alsoillustratesthegapsinradarcoverageoverthewesternUnitedStatesduetothemountainousterrain.
FIGURE 12.3 (See color insert.)Widespreadfalseprecipitation,orAP,shownontheMPEradarmosaic.(PhotoscourtesyNOAA/NWS,SilverSpring,MD.)
12.3.1.1.8 PolarizationThecurrentWSR-88Disasinglehorizontallinearpolarizedradar.Dualpolarizationradarmeasurementsofaspecificdifferentialphaseattwoorthogonalpolarizations(horizontalandvertical)haveshownimprovedskillinrainfallestimationcomparedto single polarization radars using Z/R relationships (Zrnic and Ryzhkov, 1999).Additionalhydrometeormicrophysicalinformationcanbeinferredfromtheaddi-tionofverticalpolarizationmeasurementstoobtaindifferentialreflectivity,whichaidsindeterminingthesizeandtypeofliquidorfrozenwaterparticles(e.g.,precipi-tationsuchasrain,sleet,hail,orsnow),whichwouldleadtoimprovedprecipitationestimation.AretrofitfortheWSR-88Dtoimplementdualpolarizationonanationalscaleisslatedfor2011–2013.IthasbeendeterminedthataddingdualpolarizationcapabilitytotheWSR-88Dwillprovideimprovedrainfallestimationforfloodsanddroughtandadditionalbenefitsthatincludeimprovedhaildetectionfordiscriminat-ingbetween liquidand frozenhydrometeors, rain/snowdiscrimination forwinterweather,dataretrievalfromareasofpartialbeamblockagetoimproveservicesinmountainousterrain,andremovalofnonweatherartifactssuchasbirdsandgroundcluttertoimproveoveralldataqualityfortheprecipitationalgorithm.
12.3.1.2 Benefits of Radar-Based Precipitation EstimatesInspiteof the limitationsandsomeof the issuesrelated toradar-basedprecipita-tionestimates,therearevalidreasonsforusingthem.ArecentstudybyKrajewskiet al. (2010) summarized the operational capability of radar to provide quantita-tiverainfallestimateswithpotentialapplicationsnotonlyinhydrologybutalsoindroughtmonitoringbyimprovinggriddedstandardprecipitationindices.Radarhastheabilitytoshowthespatialandtemporaldistributionofrainfallmoreaccuratelythanothertraditionalsensorssuchasraingauges.Thetimingandintensityoftherainfallismoreeasilydeterminedbecauseoftheavailabilityofhourlyandsubhourlyestimates.Radaralsoprovidesamoreaccuratedeterminationofrainfall location,which is critical for providing more local-scale information to the drought com-munity about spatial variations in rainfall patterns and the identificationofmorelocalizedareasexperiencingprecipitationdeficits.Thisisfarsuperiortowaitingfor24hraingaugedatatobereportedandperformingonlyasinglecalculationofMAPoverapredefinedgeographicarea(e.g.,ariverbasin),aswasthestandardoperatingprocedureinthepast.
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12.3.2.1 Near-Real-Time GaugesSeveral near-real-time rain gauge networks with the ability to report precipita-tionhourlyorevenat15minintervalsexist.TheseincludetheAutomatedSurfaceObservingSystem(ASOS)raingaugesatairports,datacollectionplatformsoper-atedbytheU.S.GeologicalSurvey,andmesonetalertsystemsmaintainedbyvari-ouscities,states,andriverauthorities.Althoughthesegaugesarepartofdifferentnetworks,theyallusetippingbucketgauges(Figure12.4a)toautomatethequantifi-cationofprecipitationamounts.
Unfortunately, although these data are important, they are not without error,which can be introduced by wind, tipping bucket losses, poor siting (e.g., block-agefrombuildings,trees,andothertallvegetation),frozenprecipitation,electronicsignalmalfunctions,mechanicalproblems,andtiming/codingissuesrelatedtothetransmission of rainfall data. Linsley et al. (1982) showed that strong winds willcauseallraingauges,regardlessoftype,toundercatchtheprecipitation.Forexam-ple,approximatelya10%lossisestimatedata10mphwindspeed,withlossesoftenexceeding50%atwindspeedsover39mph.Tohelpcompensateforlosses,ASOStipping bucket gauges have a shield around them to disrupt the air flow over thetopofthegauge(seeFigure12.4b).Tippingbucketgaugesalsotendtounderreportintenserainfallwhentherainfallrateexceedsthebucket’sratetodiscardthecap-turedrain(∼1.5s).Thus,theycannotbecalibratedfor0.01ofaninchprecisionorwellcalibratedforhighrainfallrates.Maintenanceisalsoanissuebecausemanygaugesarelocatedinremotelocationsandfrequentsitevisitsbytechniciansmaynotbepossible.Ingeneral,automatedgaugesprovidegoodqualityrainfalldataifthe gauges have good exposure, are well maintained, are recording when the airtemperatureisabovefreezing,whenwindconditionsarerelativelylight(15mphorless),andtherainfallrateisnotinexcessof4in/h.
12.3.2.2 Daily Reporting GaugesGaugenetworksthatreportdaily,24hrainfalltotalsareusuallysubmittedbyhumanobserverswhotypicallyuseanontippingbuckettypeofgauge.Datareceivedfromthesenetworksareconsideredtobeofhigherqualitythanthedatareceivedfromthe hourly automated networks partially because of the standard 4 in rain gaugeoraweighinggaugeusedbytheobservers,whicharetypicallyfreefromsomeoftheerrorscommonlyencounteredwithtippingbucketgauges.ThetwobestknowndailygaugenetworksaretheNWSCooperativeObserver(COOP)networkandtheCommunityCollaborativeRain,HailandSnow(CoCoRaHS)network.Wewilldis-cusshow thesedata areused to improveprecipitation estimates producedby theRFClaterinSection12.4.5.
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12.4 RADAR-BASED MULTISENSOR PRECIPITATION ESTIMATOR PRECIPITATION PROCESSING SYSTEM
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12.4.1 thRee staGes Of mPe PReciPitatiOn PROcessinG
12.4.1.1 Stage I of the MPE PPSThefirstPPSstageingeststhehourly4kmDPAdatathataregeneratedbytheWSR-88D,selectingtheDPAthatistimedclosesttothetopofeachhour.TheonlyqualitycontrolappliedtotheDPAdataisfeaturesassociatedwiththeWSR-88Dprecipita-tionalgorithmitself.SomeofthesefeatureswerediscussedinSection12.3.1,butforamoredetaileddiscussion,seeStory(1996).
12.4.1.2 Stage II of the MPE PPSThesecondPPSstagecalculatesandappliesabiasadjustmentfactorbasedonacom-parisonofraingaugereadingsandradarprecipitationestimates(Seoet al.,1999).TwobiasingtechniquesarederivedinthePPS:amean-fieldbiasandalocalbias.Themean-fieldbiasrepresentstheratioofthesumofallpositive(nonzero)raingaugedataovertheradarumbrellafromthepreviousxnumberofhourstothesumofallnonzeroDPArainfallestimatesatthecorrespondinggaugelocationsoverthesametemporalsamplingwindow.Thesizeofthetemporalwindowxisspecifiedbytheadaptableparameter“mem-span”(memoryspaninhours,determinedasafunctionofhowwidespreadtherainfallis,howmanygaugesareavailableforsampling,andhowlongagosinceitlastrained).TheMPEprogramcalculatesamean-fieldbiasfor10memoryspans,rangingfromthecurrenthour(instantaneousbias)to10,000,000h(climatologicalbias).TheprogramalsohasanadaptableparameterthattellsMPEwhichbiascalculatedfromthe10memoryspanstoapplytotheDPAfile.Thedefaultforthisadaptableparameterisaminimumof10radar-rainpairs(calledN-Pairs)foramean-fieldbiastobeappliedtothe“raw”radarrainfallestimate.Ifthereare10ormoreN-Pairsformem-span1,theprogramusesthebiascalculatedfromtheradar-gaugepairsfromthecurrenthour.Ifthereareno10N-Pairsforthecurrenthour,theprogramgoesbackintimeuntilamem-spanisfoundwhere10radar-gaugepairsareachieved.Atime-weightingfactorisappliedtoolderN-Pairssothatthemostrecentdatacarrythemostweightinthesecalculations.Forexample,ifthebiascalculatedfrommem-span720isused,theprogramhadtogobackbetween168(themaximumnumberofhoursfromthepreviousmem-span)and720htofindenoughraineventsthathadatleast10N-Pairs,whichwouldincludeallnonzeroradar-gaugepairsfromthepast30days. Ingeneral, thedenser theraingaugenetwork is, theshorter themem-span, unless a drought is in progress or the radar samples an area in a dryclimate.Intimesofdrought,themem-spancontinuestoincreaseovertimeasfewN-Pairsareachieved,leadingtothepossibilitythatwhenitdoesrainagain,thebiascalculationwillbeinappropriate.ThegoalofMPEistocapturethetemporalvari-abilityofthebiasfordifferentrainfallregimestoallowforthevariabilityofradarprecipitationestimates.AdetaileddescriptionofallMPEfunctionalitycanbefoundintheMPEEditorUser’sGuide(NWS/OHD/HL,2010).
Inshort, the larger thenumberof raingauges locatedundera radarumbrella,thebetterchancetheprogramhasofobtainingnonzeroradar/raingaugepairsandcalculatingamean-fieldbias.Under radarumbrellas thathavea largenumberof
295Estimating Precipitation from WSR-88D Observations and Rain Gauge Data
In addition to themean-fieldbias (onebias for each radar), a local bias tech-nique is also calculated in the MPE program, assigning a bias correction factorforeachHRAPgridbox(orcell)intheMPEarea.Likethemean-fieldbias,localbiasvaluesarecomputedbycomparinggaugevaluestorawradarestimates.Theyarealsoprocessedover10memoryspans,selectingthememoryspanwhosebiasvaluehasat least10contributinggauge/radarpairsfallingwithina40kmradiuscirclearoundeachHRAPgridboxforwhichabiasfactorisbeingcomputed.Theresultinggridoflocalbiasvaluesisthenappliedtotherawradarmosaic(similarto how the mean-field bias is applied) to produce the local bias–corrected radarmosaic.Bycomputing thebias foreachHRAPgridbox, localgeographicalandmicroclimatologicaleffectsonrainfallcanbeaccountedfor(SeoandBreidenbach,2002).Becauseofthisaccounting,thechosendefaultMPEfieldatmanyRFCsisthelocal bias multisensor field(i.e.,thecombinationofthelocalbiasradarmosaicandagauge-onlyanalysis).
In addition to the biased radar mosaics, a gauge-only gridded field is derivedusinghourlyraingaugeobservations,whichmustbequalitycontrolledatthisstage(Fultonetal.,1998).ToolsexistwithinMPE(suchasagaugetable)thatallowHASforecasterstodetectraingaugereadingsthatsubjectivelyappeartobeinaccurate.Although raingaugedata areoften referred to as “ground truth,” thesedata alsohaveknowndeficiencies,asmentionedintheprevioussection.However,theWestGulfRFC(WGRFC)HASforecastershavefoundthatmostraingaugedatareceivedareofacceptablequalityandcanbeused(withsomecaution)tomakeaccuratebiasadjustmentsduringmostevents.Ifanygaugereadingappearsincorrect(e.g.,whenradarfieldsarenonzeroandagaugereadszero),itisremovedbytheHASforecaster,andalltheMPEfieldsareregenerated.Thismaycauseachangeinthebiasadjust-ment factors for one or more radars and in the gauge-only fields. The end resultofthissecondstageisanadjustedradarprecipitationestimateforeachWSR-88DdefinedintheMPEprogram.
12.4.1.3 Stage III of the MPE PPSInstagethreeofthePPS,theadjustedradarfields(thosederivedinStageII,whichwerediscussedintheprevioussection)aremergedwiththederivedgauge-onlyfieldtocalculatethefinalmultisensorfields.Themultisensorfieldofthespecificradarsiteisthenmosaickedwiththemultisensorfieldsofotherradarsitestoobtainthefinalmultiradarprecipitationmap.TwoprimarymultisensorfieldsarecreatedinMPE,one for eachbiasing techniquedescribed in theprevious section.TheHAS fore-castermakesadeterminationofwhichmultisensorfieldisestimatingcorrectlyeachhour(touseasourbestestimatefield,discussedfurtherinthenexttwosections).
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TheWGRFChasbeenexperimentingwithanewprecipitationestimationtechniquecalledQ2,whichisthesecondtechniquederivedbyresearchmeteorologistsattheNationalSevereStormsLaboratory(NSSL).TheNationalMosaicandMultisensorQPE(NMQ)projectisajointinitiativebetweentheNSSLandotherentities(suchas the Federal Aviation Administration [FAA] and the University of Oklahoma).TheNationalMosaicandQ2systemisanexperimentalsystemdesignedtoimproveQPEandeventuallyveryshort-termQuantitativePrecipitationForecasts(QPF).Fordetailed information on the system, readers are referred to the NMQ web site athttp://nmq.ou.edu.TheNMQingestsdatafrom128WSR-88Dstationsevery5min,qualitycontrolstheradardata,andderivesaverticalprofileofreflectivityfromeachradar.AnalysesaredoneoneighttilesofradardatathatarestitchedtogethertoformacontinentalU.S.(CONUS)three-dimensional(3-D)grid.Hybridscanreflectivityandotherproducts(suchasacompositereflectivitymapandprecipitationflagprod-uct)arethenderivedtoproducetheexperimentalQ2products.Theproducts(suchasQPEaccumulationsforthecurrenthourorseveralhoursofupto72h)arethentranslatedover to the4kmHRAPgrid.TheQ2productsholdseveraladvantagesovertraditionalradar-basedestimates,withtwoprimaryadvantagesincludinganAPremovaltechniqueandrainfallestimatesbeyondthenominal230kmrangeoftheDPAfilesthatareusedinregionswhereradarumbrellasdonotoverlap.Becauseoftheseadvantages,WGRFCHASforecastershavetheoptionofimplementingQ2asourfinalbestestimatefield.
12.4.3 satellite PReciPitatiOn estimates
The MPE also ingests satellite-derived precipitation estimates from the NationalEnvironmental Satellite, Data, and Information Service (NESDIS). TheHydroestimatorisanautomatedtechnique,initiallydesignedforlarge,moistthun-derstorm systems, which uses Geostationary Operational Environmental Satellite(GOES) infrared (IR) imagery cloud top brightness temperatures (Scofield andKuligowski,2003).PixelswiththecoldestIRtemperaturesareassignedtheheavi-est rainfall rates at the surface. Numerous other factors, including the cloud-topgeometry, the available atmospheric moisture (precipitation efficiency), stabilityparametersfromweathermodels,radar,andlocal topography,areusedtofurtheradjusttherainrates.Althoughcautionshouldbeusedindrawingconclusionsaboutradarperformancebasedonsatellite-derivedprecipitationestimates,HASforecast-erscanconfirmradarperformanceiftheprecipitationestimatesfrombothsourcesare in closeagreement.However, correlationcoefficients comparing24h satellite
297Estimating Precipitation from WSR-88D Observations and Rain Gauge Data
12.4.4 final POstanalysis Quality cOntROl techniQue
Hundredsof24hCOOPrainfallreportsandCoCoRaHSobservationsareavailableforpostanalysisoftheMPEresults.DirectcomparisonsoftheMPEandobserverrainfall totals shortly after 12 Coordinated Universal Time (UTC) each morningallow HAS forecasters to determine areas where the MPE estimates may be toolowortoohigh.Forecasterscanraiseorlowerestimatesinspecifichoursinordertoproducea24hestimatethatismoreconsistentwith24hgaugereports.Thegoalis to achieve a “general” level of acceptable error in the estimates.ProgramsarerunthatshowthecorrelationcoefficientandpercentbiasofMPEestimates,whichvarybytimeandlocation.Thegoalistomodifytheestimatestoachievecorrelationcoefficientsofgreater than0.85.Most initialestimatesare low(meaningthe24hgaugereportsarehigher thanMPE)andhavecorrelationcoefficientsof less than0.85.WheninitialMPEestimatesareraisedorlowered,theinherenterrorofmostestimatesisimprovedtothedesiredcorrelation.Sincethesedataaretobeusedforimproveddroughtmonitoring,removalofthetraditionalunderestimationiscrucial.Ifthesebiasesarenotmitigated,afalseidentificationoftheonsetofdroughtmightoccurovertime.
12.5 DROUGHT MONITORING: HOW THESE ESTIMATES CAN BE USED TO DETERMINE CURRENT LOCATIONS OF DROUGHT
12.5.1 nWs sOutheRn ReGiOn PReciPitatiOn analysis PROject
In the early and mid-2000s, NWS Southern Region offices began to displaythe gridded MPE output maps on the Internet, and the data became avail-able fordownloada short time later. Initially, thesepagesgraphically showedthe short-term observed and climatic trends of precipitation across the south-ern region (from New Mexico eastward to Tennessee, Georgia, and Florida).In 2009, this project was expanded to include the entire CONUS and PuertoRico. The national-level products can be found on the Advanced HydrologicPrediction Service (AHPS) web site (http://water.weather.gov). Tools are also
298 Remote Sensing of Drought: Innovative Monitoring Approaches
available to compare MPE estimates to normal rainfall over different times-cales(http://water.weather.gov/precip/),whichcanprovidevaluableinsightintodetailed spatiotemporal patterns of precipitation deficits to characterize bothshort-andlong-termdroughtconditions.
“DeparturefromNormal”and“PercentageofNormal”productsaregeneratedbyusingsimplegridmathematics,wherethe“Normal”datasetisrespectivelysub-tractedfromordividedintothe“Observed”dataset.“Observed”dataarederivedfromoutput(e.g.,fromMPEorsimilarPPSs)from12NWSRFCs.“Normal”pre-cipitation isderivedfromParameter-elevationRegressionson IndependentSlopesModel(PRISM)climatedata(Gibsonetal.,2002),whichrepresenta30yearperiodof record (1971–2000). The data sets were created as a unique knowledge-basedsystem that uses point measurements of precipitation, temperature, andother cli-maticfactorstoproducecontinuous,digitalgridestimatesofmonthly,yearly,andevent-based climatic parameters. This unique analytical tool incorporates pointdata,adigitalelevationmodel,andexpertknowledgeofcomplexclimaticextremes,includingrainshadows,coastaleffects,andtemperatureinversions.Inordertofillinareas thathaveradar-coveragegaps in themountainouswesternUnitedStates,gaugereportsareplottedagainstlong-termclimaticPRISMprecipitationdata,andamountsbetweengaugelocationsarespatiallyinterpolated(moreinformationaboutthis method is available at http://www.cnrfc.noaa.gov/products/rfcprismuse.pdf).The derived precipitation products (specifically, “Departure from Normal” and“PercentageofNormal”products)canprovideusefulcontextualinformationtoiden-tifytheamountandmagnitudeofprecipitationdeficitsthatcanbeusedfordroughtmonitoring.
Figure 12.5 shows an example of a percent of normal rainfall graphic fromDecember 2010 across the southern United States. This month was exception-allydry,andthisgraphicdepictsfewareaswherepercentofnormalprecipitation
FIGURE 12.5 (See color insert.)PercentofnormalrainfallforthesouthernUnitedStatesfromtheAHPSprecipitationanalysispageforDecember2010.(ImagecourtesyofNOAA/NWS,SilverSpring,MD.)
Before 2009, all radar-based product data displayed by the Southern RegionPrecipitation Analysis Project were considered to be “experimental.” To makethesedata“operational,”thedatapageswerepackagedintoanationwideprogramknownastheAHPS,anewandessentialcomponentoftheNWSClimate,Water,andWeatherServices.AHPSisaweb-basedsuiteofproductsthatdisplaydroughtmagnitudeanduncertaintyofoccurrence,basedontherangeofpotentialoutcomescomputedfromhistoricalhydrometeorologicaldataandcurrentconditionsusinganensemblestreamflowpredictionmodel.ThesenewproductsareenablingtheUSDM,NationalDroughtMitigationCenter(NDMC),governmentagencies,privateinstitu-tions, and individuals tomakemore informeddecisionsabout risk-basedpoliciesandactionstomitigatethedangersposedbydroughts.Althoughtheseproductswerenotdesignedspecificallyfordroughtmonitoring,thehigh-spatial-resolutionprecip-itation information they provide has substantial potential to support this applica-tion.Forexample,theofficeoftheTexasStateClimatologistcreatesagridded4kmresolutionandacounty-scaleresolutionSPIfromtheAHPSprecipitationanalysesdata(http://atmo.tamu.edu/osc/drought/).AmoredetaileddescriptionoftheSPIgridgenerationusingtheAHPSisprovidedbyNielsen-GammonandMcRoberts(2009).
Traditionally, coarse resolutionSPImapsderived fromspatial interpolationsof point-basedgaugedatahavebeenused for droughtmonitoring, as shown inFigure12.6a.InFigure12.6b,the4kmSPImapsgeneratedfromradar-basedpre-cipitationdatadepictconsiderablymorespatiallydetailedprecipitationvariations,whichprovideconsiderablymorelocal-scaleinformationaboutprecipitationdefi-citsthatismoreappropriateforcountytosubcountydecisionmakingrelatedtodrought. In brief, the SPI map generated from AHPS precipitation analyses iscreatedusing the followingprocess. Initially,aclusteranalysis isperformed todetermineTexasprecipitationnormalsbylocationandseason.Afrequencydistri-butionisthencalculatedforeachlocationandseason,fromwhichhigh-resolutiongriddedfrequencydistributionsareproduced(usingPRISMdataoverhigherter-rain of west Texas and roughly 1500 COOP stations in Texas and surroundingstates).Finally,accumulationsofprecipitationarecomputed,creating4kmandcounty-aggregatedSPIforvarioustimeperiodsfrom2to24months,andrelatedproductssuchasanSPIblend,anSPIblend1weekchangemap,andapercentofnormalprecipitationmap.
FIGURE 12.6 (See color insert.) An 8 week SPI map interpolated from station-basedprecipitation data (a) and an8weekSPImapderived from4kmprecipitation fromMPE(b) (Image courtesy of Dr. John Nielsen-Gammon) for early September 2009 during theseveredrought insouthernTexas,asshownby theUSDMmaponSeptember7,2009(c).ThecirclehighlightsanareaofexceptionaldroughtintheUSDMthatisshowntohavenear-normalconditionsintheinterpolatedSPImap(a)butclearlyhadlocalizedareasofseveredroughtconditionsthatweredetectedintheSPImapbasedonhigher-resolution,radar-basedprecipitationobservations(b).
301Estimating Precipitation from WSR-88D Observations and Rain Gauge Data
USDMmaponacountyscalefor itsdrought reliefdecisions,yet theUSDMandother existing drought index tools do not have the sufficient spatial resolution toenableestimationofdroughtatthisspatialscalewithinTexas.AnexampleoftheSPIblendforTexasduringthe2009droughtcanbeseeninFigure12.6b.Duringthisdrought,theMPE-basedSPIblendwasabletoaccuratelyhighlightthelocationsofmostseveredroughtinTexas.Gaugeswithinthesehardest-hitareas,asindicatedbyourMPEproducts,wereindeedexperiencinghistoricdroughtseveritybasedonananalysisoftheperiod-of-recorddata,whilestationsadjacenttotheseareaswerenot.Ninecounties(Nueces,SanPatricio,Aransas,Refugio,Jackson,Calhoun,Bee,Brazoria,andGoliad)experiencingunprecedenteddroughtseveritywereidentifiedinsouthernTexasalongtheGulfcoastusingMPEdata,eventhoughmostofthosecountiesdidnothavelong-termprecipitationrecordsbecauseofthesparsenumberofCOOPstationsthathadalonghistoryinthatregion(Nielsen-Gammon,August2010, personal communication). Without the long-term precipitation records, SPIblendsbasedonMPEdataprovidedinformationthatimprovedtheassessmentoftheseverityofthelocaldroughtsituation.
302 Remote Sensing of Drought: Innovative Monitoring Approaches
eastward to Gonzales County), the MPE-based blended SPI showed severe toextreme drought conditions. Since hourly gauge data are incorporated into thefinalmultisensorMPE,thedroughtfeaturesthatappearoverthisareainFigure12.6bshouldberepresentativeofrelativeprecipitationpatterns(anddeficits)atalocal subcountyscalebecauseground-basedprecipitationobservationsarecon-sideredintheadjusted,radar-basedprecipitationfields.AcrossthisareaofTexas,notablerainfalldiscrepanciesamongstationsduringthedefinedSPIintervalwerelikelyduetotheconvectivenatureoftherainfallinthisregion,withtheintersta-tion variations being relatively consistent with the drought/nondrought patternsdepictedinFigure12.6b.TheUSDMmapforSeptember8,2009(Figure12.6c),reaffirmstheseveredroughtconditionsoverthisarea,classifyingthesecountiesinthemostseveredroughtclass(D4,anexceptionaldroughtthatisdefinedasaonein50yearevent).FurthervisualanalysisoftheMPE-derivedSPImapofthearea reveals many subtle subcounty variations in dryness that are not detectedin thestation-basedSPImap.Manycounties insouthernTexashavepocketsofbothdroughtandnondroughtconditionsintheradar-generatedSPImapthatcan-notbe spatially resolvedusing traditional interpolatedmaps fromstation-basedobservations.
The use of 4km precipitation data provides a more accurate depiction of thebreadthandscopeoftheTexasdroughtconditionsin2009.Thisresultsuggeststhattheimprovedspatialresolutionofthisinformationwillbeatremendousbenefitforlocal-scaledroughtmonitoringactivitiesbycharacterizingdetailedsubcountyspa-tialvariationsinprecipitationdeficits.The4kmprecipitationandotherderivativeproductssuchastheSPIwillalsobeextremelyvaluableinareaswithsparseweatherstationnetworksandforcountieswithlargeareasthatcommonlyexperienceconsid-erablewithin-countyclimatevariations.
12.6 CONCLUSIONS
Over the past several years, advancements have been made in both radar-basedprecipitation sensing and multisensor estimation processing techniques. Furtherimprovementswillbemadeinradarprecipitationestimationwiththeimplementa-tionofdualpolarizationinthenextfewyears.NewrainfallratealgorithmssuchasQ2havealsobeenimplementedwithintheMPEPPS.Thischapterhasdiscussedthebenefitthatimproved,quality-controlled,andfiner-scaleprecipitationdatacanhaveindroughtmonitoringbydetailingdeficitsinrainfallwithgreaterspatialresolutionthatisnotavailableusinggauge-basedSPIdataalone.
EastoftheContinentalDivide,RFCsderiveestimatesofprecipitationusingamultisensorapproach.HourlyprecipitationestimatesfromWSR-88Dradarsarecomparedtogroundrainfallgaugereports,andabias(correctionfactor) iscal-culatedandapplied to the radarfield.Theradarandgaugefieldsarecombinedintoa“multisensorfield,”whichisqualitycontrolledonanhourlybasis.Inareaswithlimitedornoradarcoverage,SPEcanbeincorporatedintothismultisensorfield,andtheSPEcanalsobebiasedagainstraingaugereports.Inmountainousareas west of the Continental Divide, a different method is used to derive the
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Studieshaveshown(Seo,1999;SeoandBreidenbach,2002)thatalgorithmsthatcombinesensorinputs—radar,gauge,andsatellite—yieldmoreaccurateprecipita-tionestimates than those that relyonasinglesensor (i.e., radaronly,gaugeonly,andsatelliteonly).Althoughit isnotperfect, theMPEdataset isoneof thebestsources of timely, high-resolution precipitation information available. Still, usersshouldunderstandtheinherentweaknessesofthisdatasetbeforeusingitindroughtmonitoringapplications,especiallythosethatrequireahighdegreeofaccuracy.
SeveralofthePalmerindicesandtheSPIareusefulfordescribingdroughtonvaryingtemporalscales(i.e.,weeks,months,oryears).Onaclimate-divisionscale,astandardsuiteofproductsincludingtheNCDC’sSPI,theCPC’ssoilmoisture–relateddroughtseverityindex,andtheWesternRegionClimateCenter’sSPIexist.Onasta-tionscale,theU.S.GeologicalSurveyprovidesgauge-basedstreamflowdata,andtheHighPlainsRegionalClimateCenterproducesa30daySPIusingdailydatafromACISthatincorporatesCOOPobserverandautomatedweatherdata.Satellite-basedtoolssuchasVegDRI(Brownetal.,2008)thatassistinagricultural-relateddroughtmonitoringalsorelyonprecipitationdataasaprimaryinput.Collectively,thesedroughtindiceshavereliedongauge-baseddataandhavenotprovidedindi-ces representative of county- to subcounty-scale drought information because ofthe coarse spatial resolution inputs. The higher-resolution 4km precipitation dataproducedbyMPEcanbeusedtoreplacethetraditionalpointorinterpolatedpre-cipitationproductsinthedevelopmentoftheseindicestoprovideamoredetailedcharacterizationofdroughtpatterns.Thisholdsthepotentialtoadvancelocal-scaledroughtmonitoringactivitiesasprioritizedbyNIDIS,aswellas improvecurrentstate-of-the-artmonitoring toolssuchas theUSDM,whichwas initiallydesignedtoclassifybroadscale,nationaldroughtpatternsbutisincreasedbeingrelieduponforcountyandsubcountydroughtinformation.Withthegoalofimproveddroughtmonitoring,TexasA&MUniversity,NorthCarolinaStateUniversity, andPurdueUniversityreceivedaUSDAawardtoimprovethelong-termcalibrationoftheAHPSMPEanalyses,andtaketheSPIproductsbeyondTexastoincludeatleasttheeasternpartsoftheUnitedStates(i.e.,south-centralandeasternsections).TheprojectbeganinJanuary2011,withtangibleresultsexpectedafewmonthsafterthat.
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FIGURE 12.3 Widespread false precipitation, or AP, shown on the MPE radar mosaic. (Photos courtesy NOAA/NWS, Silver Spring, MD.)
FIGURE 12.5 Percent of normal rainfall for the southern United States from the AHPS precipitation analysis page for December 2010. (Image courtesy of NOAA/NWS, Silver Spring, MD.)
FIGURE 12.6 An 8 week SPI map interpolated from station-based precipitation data (a) and an 8 week SPI map derived from 4 km precipitation from MPE (b) (Image courtesy of Dr. John Nielsen-Gammon) for early September 2009 during the severe drought in southern Texas, as shown by the USDM map on September 7, 2009 (c). The circle highlights an area of excep-tional drought in the USDM that is shown to have near-normal conditions in the interpolated SPI map (a) but clearly had localized areas of severe drought conditions that were detected in the SPI map based on higher-resolution, radar-based precipitation observations (b).