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Use of Ground-based Radar for Climate-Scale Studies of Weather and Rainfall
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Title: Use of Ground-based Radar for Climate-Scale Studies of Weather and Rainfall
Series Title: Matyas, C. J., 2010: Use of Ground-based Radar for Climate-Scale Studies of Weather and Rainfall. Geography Compass, 4, 1218-1237.
Physical Description: Journal Article
Creator: Matyas, Corene
Publisher: Geography Compass
Publication Date: 2010
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Abstract: Reflectivity data from weather radar provide information on the location and quantity of water and ice in the atmosphere at high spatial and temporal resolutions. Although the analysis of radar data facilitates spatially accurate climatologies of weather events and rainfall, relatively few studies have utilized data from the US Next Generation Radar (NEXRAD) network for climate-scale research. Towards the goal of increasing the use of these data by geographers, this article details the collection of radar data, their limitations, and conversion into rainfall rates. Examples of climate-scale research incorporating NEXRAD data are also presented. Although its capabilities to analyze temporal data are limited, the use of Geographical Information Systems (GIS) by nongeographers is growing. This suggests that the collaboration of geographers specializing in geospatial techniques with those pursuing research in climatology could develop new GIS-based methods for the spatial analysis of radar data that facilitate climate-scale research of weather patterns.
Acquisition: Collected for University of Florida's Institutional Repository by the UFIR Self-Submittal tool. Submitted by Corene Matyas.
Publication Status: Published
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Source Institution: University of Florida Institutional Repository
Holding Location: University of Florida
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UseofGround-basedRadarfo rClimate-ScaleStudiesof WeatherandRainfallCoreneJ.Matyas*DepartmentofGeography,UniversityofFloridaAbstractReectivitydatafromweatherradarprovideinformationonthelocationandquantityofwater andiceintheatmosphereathighspatialandtemporalresolutions.Althoughtheanalysisofradar datafacilitatesspatiallyaccurateclimatologiesofweathereventsandrainfall,relativelyfewstudies haveutilizeddatafromtheUSNextGenerationRadar(NEXRAD)networkforclimate-scale research.Towardsthegoalofincreasingtheuseofthesedatabygeographers,thisarticledetails thecollectionofradardata,theirlimitations,andconversionintorainfallrates.Examplesof climate-scaleresearchincorporatingNEXRADdataarealsopresented.Althoughitscapabilitiesto analyzetemporaldataarelimited,theuseofGeographicalInformationSystems(GIS)bynongeographersisgrowing.ThissuggeststhatthecollaborationofgeographersspecializingingeospatialtechniqueswiththosepursuingresearchinclimatologycoulddevelopnewGIS-basedmethods forthespatialanalysisofradardatathatfacilitateclimate-scaleresearchofweatherpatterns.Introduction Hazardousweathercausesfatalitiesandbillionsofdollarsindamageeachyear(Pielke etal.2008).Theabilitytodetectthequantityandlocationofwaterandiceintheatmospherehasimportantbenetstosocietysuchasthepredictionofthedepthandlocation oflake-effectsnowfall(Lairdetal.2009b;Steenburghetal.2000),determiningwhich rainfalleventswillleadtoashooding(Carpenteretal.1999;Fangetal.2008),andto quantifythedegreetowhichurbanizationcontributestoprecipitation(Moteetal.2007). Scanningtheatmospherewithground-basedweatherradarprovidesdataofthehighspatialandtemporalresolutionneededtoanalyzeatmosphericconditionsforshort-term forecasting(e.g.DonavonandJungbluth2007)andlonger-termclimatologicalstudiesof rainfall(e.g.Overeemetal.2009)andhazardousweather(e.g.HockerandBasara2008). Themajorityofstudiesthatutilizeground-basedradartoanalyzehazardousweatherin theUSarecasestudiesorcomparerelativelyfewevents.Itislesscommonforradar reectivitydatatobeutilizedoveralargetemporalscaleorfortheanalysisofmultiple occurrencesofatypeofweatherevent,suchaslandfallingtropicalcyclones.However, thehighspatialandtemporalresolutionsofradardatarenderthemappropriateforclimatological-scaleexaminationsofhazardousweather.Ageospatialanalysisofradardatacan quantifycharacteristicssuchasarealcoverageandspatialpatternsofweatherevents (e.g.Basaraetal.2007;Billetetal.1997;Matyas2009).Asgeographersspecializeinthe analysisofspatialdata,itwouldseemthatgeographer-climatologistsmightlargely contributetoclimate-scaleweatheranalysisusingradardata.Yet,afterapresentation givenduringthe2003ClimateSpecialtyGroupStudentPaperCompetition(Matyas 2003),anaudiencememberposedthequestion,Shouldclimatologistsbeusingradardata intheirresearch?'GeographyCompass 4/9(2010):1218–1237,10.1111/j.1749-8198.2010.00370.x 2010TheAuthor GeographyCompass 2010BlackwellPublishingLtd

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Interestingly,fourmeetingabstractspublishedinthe AnnalsoftheAssociationofAmerican Geographers duringthe1960ssuggestthatgeographerswereinvolvedinradaranalysisthat time(Prentice1967;RhodesandSchwarz1967;Simpson1965,1966).However,an explorationofabstractsin TheProfessionalGeographer andthe AnnalsoftheAssociationof AmericanGeographers ,yieldonlytworecentstudiesthatemployreectivitydatafrom ground-basedweatherradars(Legates2000;Matyas2007).Quiring(2007)demonstrated thatmanygeographer-climatologistspublishtheirworkinnon-geographyjournals.An examinationofabstractsinthetoptenjournalsforpublicationofresearchbygeographerclimatologistslistedbyQuiring(2007)revealsthatrelativelyfewstudiesutilizegroundbasedradardataonaclimatologicalscale(Table1).Additionally,onlyfouroftheauthor addressesforthesepaperslistaGeographyorGeosciencesDepartment.AsearchofjournalsthatpublishGeographicalInformationSystem(GIS)-relatedresearch(Table2)yields similarresults.Climate-scaleresearchutilizingweatherradardatahasbeenpublishedin journalsotherthanthoselistedinTables1and2asisindicatedbyexaminingthereferencelistforthismanuscript;however,mostoftheseauthorsarefromatmosphericscience orengineeringdepartments.Thelargevolumesofdatarequiredforanalysis,difcultiesin Table1.Listofjournalsinwhichmostgeographer-climatologistspublish(Quiring2007)and articlescontainingkeywordsNEXRADandWSR-88Dthatfeatureclimate-scalespatialanalysisofNEXRADreectivitydata.JournalNEXRADWSR-88D JournalofClimateCarletonetal.(2008)(viewedimages only;spatialanalysisisperformed byMatyasandCarleton(2010)) X PhysicalGeographyDyer(2009)X ClimateResearchXX InternationalJournalof Climatology XAshleyandAshley(2008), CroftandShulman(1989), DeGaetanoandWilks(2009) andHocketandBasara(2008) JournalofGeophysical Research–Atmospheres Cosgroveetal.(2003),Nelson etal.(2003a),Villarinietal. (2009)andYoungetal.(1999) Smithetal.(1999)andWiens etal.(2008) GeophysicalResearch Letters Gauthieretal.(2006), Kongolietal.(2003)and MooreandRojstaczer(2002) Kelleyetal.(2005), Melnikovetal.(2008), Moteetal.(2007)and Nelsonetal.(2005) BulletinoftheAmerican MeteorologicalSociety Ryzhkovetal.(2005b)Hoiumetal.(1997), ParkerandKnievel(2005), MacGormanetal.(2008), Ortegaetal.(2009), Rasmussenetal.(1994) andWestricketal.(1999) ProfessionalGeographerXLegates(2000)and Matyas(2007b) AnnalsoftheAssociation ofAmericanGeographers XX InternationalJournalof RemoteSensing BrunsellandYoung(2008) andCaoetal.(2009) NiralaandCracknell(2002) RemoteSensingofthe Environment XXRadarandclimatology1219 2010TheAuthor GeographyCompass 4/9(2010):1218–1237,10.1111/j.1749-8198.2010.00370.x GeographyCompass 2010BlackwellPublishingLtd

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theanalysisoftemporaldatawithinGIS,and,untilrecently,thelackoftoolstoincorporateradardataintoaGIS(AnsariandDelGreco2005;Shipley2005),mayexplainwhy fewgeographerscurrentlyanalyzethesedata. Thegoalsofthisarticlearetoprovidebasicinformationabouttheformatandlimitationsofradarreectivitydata,andtodemonstratehowgeographersmightutilizeradar reectivityorrainfallestimatedataintheirresearchanddevelopnewmethodsofspatial analysiswiththesedata.Asmanygeographersmaybeunfamiliarwiththesedata,section twoofthisarticlebrieydiscussesthedevelopmentoftheUSAweatherradarnetwork andthespatialandtemporalcomponentsofradardata.Thethirdsectiondetailsimportant limitationsandcausesoferroneousdataofwhichusersshouldbeaware.Theissuesthat mustbeconsideredbeforecreatingamosaicofdatafromadjoiningradarsitesare explainednext,includingdatainterpolation,asgeographershavespecialexpertiseinspatialanalysisthatcouldleadtonewinterpolationtechniques.Next,theconversionof radarreectivityvaluesintorainfallratesisdescribed,whichisakeystepinincreasing theaccuracyofrainfallclimatologiesandhydrologicmodels.Then,abriefoverviewof severalclimatological-scalestudiesthathaveemployeddatafromUSweatherradaris accompaniedbysuggestedresearchdirectionsthatcouldbetakenbygeographers,Finally, thelastsectiondiscussestheuseofGIStoanalyzeradardataandsuggeststhatthespatial analyticskillsofgeographerscouldleadtonewradargeovisualizationandanalysistechniques.Datafromradarsarealsoanalyzedforweatherresearchinothercountries,in mobileradarssuchastheDoppleronWheels,andaboardaircraftandspacecraft.Asthese radarsutilizedifferentwavelengthsfromthoseemployedbytheUSNationalWeather Serviceandtheirdatamaynotbewidelyavailableforresearchuse,theyarenotdiscussed inthispaper. RadarOperations Radar,orradiodetectingandranging,wasdevelopedpriortoWorldWarIItomonitor aircraftpositions(Atlas1990;DoviakandZrnic1993;Skinneretal.2009;Whitonetal. 1998).Usersdiscoveredthatthepulsesofmicrowaveenergyemittedbytheradarcould beemployedtodetectphenomenonsuchasbirdsorwaterdropletsinadditiontoaircraft Table2.ListofjournalsthatpublishGIS-relatedresearchandarticleswhoseabstracts and ‡ orkeywordscontainNEXRAD'orWSR-88D'.JournalNEXRADWSR-88D InternationalJournalof GeographicalInformation Science XX JournalofGeographicalSystemsXX TransactionsinGISXBasaraetal.(1997) GeoInformaticaXX GeographicalAnalysisXX CartographyandGeographic InformationScience XX GeographicalandEnvironmental Modeling XX ComputersandGeosciencesKrajewskietal.(2006), Krugeretal.(2006)and Xieetal.(2005) Nelsonetal.(2003b)1220Radarandclimatology 2010TheAuthor GeographyCompass 4/9(2010):1218–1237,10.1111/j.1749-8198.2010.00370.x GeographyCompass 2010BlackwellPublishingLtd

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(Maynard1945).Thus,modicationsweremadetoincreasetheabilityofradartodetect weathersystemsforshortrangeweatherforecastingandrainfallprediction(Marshalletal. 1947;Maynard1945;Wexler1948).DamagecausedbytornadoesandlandfallinghurricanespromptedtheUSWeatherBureautodevelopanetworkofground-basedradars, theWeatherSurveillanceRadar1957(WSR-57)(Whitonetal.1998). Thecurrentradarsinusetoday,theWeatherSurveillanceDoppler-1988Doppler (WSR-88D),orNextGenerationRadar(NEXRAD),werecommissionedinthe1990s; theirdeploymentandmanagementisajointeffortoftheDepartmentofCommerce,the FederalAviationAdministration,andtheDepartmentofDefense(Crumetal.1998; OFCM2006).Theenhancedpoweroutput,increasedgain,andnarrowedbeamwidth oftheWSR-88Dunitsallowsforagreatersensitivityandbetterspatialresolutionof targetechoeswhencomparedtotheWSR-57units(Fultonetal.1998).Morethan150 WSR-88DradarsoperatewithintheUSA(Figure1).Currentimprovementstothe WSR-88Dunitsincludemodicationsallowingradiowavesemittedbytheradartohave bothhorizontalandverticalorientations(Doviaketal.2000;Zrnicetal.2006).Polarimetricradarwillallowforimprovedrainrateestimationandprecipitation-typedetection (Doviaketal.2000;Ivicetal.2009;Meischner2004;Ryzhkovetal.2005a,b). Theradargeneratesshortpulsesofradiowavesthatareconcentratedintoanarrow beamandtransmittedacrossa360 sweepoftheatmosphereevery510min(Burgess andRay1986;Crumetal.1998;Krajewskietal.2006;Meischner2004;Skinneretal. 2009).Areceiverdetectstheamountofsignalthatreectsoffofatargetbacktowards theradarandcomputerscalculatethestrengthofthereturnedsignalanditstraveltime. Theamountofthetotalpoweroutputthatisreturnedtothereceiverismeasuredin dB Z ,ordecibelsof Z where Z istheradarreectivityfactor(Meischner2004).The amountofpowerthatreachesthereceiverdependsonthesizeandshapeofthetarget. Fig.1.LocationofWSR-88Dsitesinthelower48statesandthe230kmradiusoverwhichtheatmosphereis sensedbyeachradar.Beamblockagebymountainspreventscontinuousspatialsamplingoftheatmosphereinthe westernUSA.Radarandclimatology1221 2010TheAuthor GeographyCompass 4/9(2010):1218–1237,10.1111/j.1749-8198.2010.00370.x GeographyCompass 2010BlackwellPublishingLtd

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Dopplerradaralsodetectsthephaseshiftofthepulseofenergysothatthespeedand directionofatarget'smotioncanbedetermined.Aftereachsweep,thetiltoftheradaris changedthrougharangeof0.5 to20 fromthehorizonsothattheatmosphereissampledatmultipleelevationstoproduceavolumescan.Differentscanelevationsandtimes, orvolumecoveragepatterns(VCPs),areimplementedbymeteorologistsateachNational WeatherService(NWS)Ofcedependingonthetypeofweatherthatisdetected(Fultonetal.1998;Krugeretal.2006;Maddoxetal.1999;Milleretal.1998;OFCM 2006).Thedataproducedduringeachvolumescan,whichincludereectivity (Figure2a,b),meanradialvelocity,andspectrumwidth,aretermedLevelIIdataandare best-suitedformostresearchapplications(Crumetal.1998)andrainfallestimation (Fultonetal.1998). Reectivityvaluesareproducedeveryonekilometeroutwardfromtheradarineach 1 arcofthe360 circlewith0.5-dB Z precision(Crumetal.1998;Fultonetal.1998). However,theazimuthalspacingofeachbininwhichdataarecollected,aswellasthe beamwidth,increasesasdistancefromtheradarsiteincreases(Zhangetal.2005),leading toadecreaseinspatialresolution.Also,thebeamincreasesinaltitudeasittravelsoutbound(Figure3).Atthelowestscanelevationof0.5 ,whichisknownasthebasescan (Figure2a),beamheightisapproximately2.5and5kmatdistancesof150and230km fromthesite,respectively.Giventhedecreasingsampleresolutionandthefactthatthe lowestlayersofcloudsmaynotbesampledatlargedistancesawayfromtheradarsite, onlydatawithina230kmradiusoftheradarsite(Figure1)areutilizedformostanalyses (OFCM2006).SeveralproductsarecreatedfromLevelIIdataincludingacomposite analysiswherethehighestreectivityvalueoutofallelevationsisrecordedforeachdata bin(Figure4),andonehourprecipitationtotals(Figure5).Atotalof41productscreated fromtheLevelIIdataareavailableintheLevelIIIdataset(Collier1996;OFCM2006). ThesedataareavailablefromtheNationalClimaticDataCenter(NCDC)website (NCDC2009a),andcanbeorderedformostWSR-88Dsitesbeginningin1995, althoughLevelIIdataarenotavailablefromsomesitesafter2001. ConditionsthatProduceErroneousReectivityValues Anomalousbeamcurvature,groundclutter,andthepresenceofmeltingicecancause erroneousreectivityvaluesorinstanceswherereectivityvaluesareabsentwhenthey shouldbepresent.Theamountofcurvatureofaradarbeam(Figure6)isaffectedbythe rateofdecreaseinpressure,temperature,andhumiditywithheightinthetroposphere. Undernormalatmosphericconditions,aradarbeamcurvesslightlylessthanthesurface oftheearth,causingittosensehigheraltitudesatfartherdistances(BurgessandRay 1986;Rinehart1991).Falseechoesinradarreectivitydatacanbecausedbyanomalous propagationofthebeamwhenmoistureandtemperaturedonotchangewithheightas expected(Moszkowiczetal.1994).Fortunately,researchershavedevelopedandimplementedautomaticdetectionandremovalalgorithmsforfalseechoesunderconditions associatedwithabnormalrefraction(Fultonetal.1998;Moszkowiczetal.1994;Steiner andSmith2002). Groundclutterproducesfalseechoesasenergythatisreturnedtothereceiveris scatteredoffofobjectsnearorontheearth'ssurface(DoviakandZrnic1993).Fixed objectssuchastrees,buildings,windfarms,andhillsaresourcesofgroundclutter (Changetal.2009).Thesizeandintensityoftheechoesproducedbytheseobjects cancauserainfallestimatestobeerroneouslylarge(Changetal.2009;Smithetal. 1996).Algorithmsexistthatsuccessfullydetectandremovegroundclutterasfalse1222Radarandclimatology 2010TheAuthor GeographyCompass 4/9(2010):1218–1237,10.1111/j.1749-8198.2010.00370.x GeographyCompass 2010BlackwellPublishingLtd

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(a) (b) Fig.2.Reectivityvaluesdetectedbythe0.5 tilt,orbasescan(a),andcompletevolumeofLevelIIdata(b),from theradarlocatedinDodgeCity,Kansas(KDDC)atthetimeoftheGreensburg,Kansastornado(5May2007, 0241UTC).Higherreectivityvaluescorrespondtolargerrainfallrates.Radarandclimatology1223 2010TheAuthor GeographyCompass 4/9(2010):1218–1237,10.1111/j.1749-8198.2010.00370.x GeographyCompass 2010BlackwellPublishingLtd

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echoesfromxedobjectsarerelativelyeasytodistinguish(Collier2009;Doviakand Zrnic1993;Fultonetal.1998). Whentheradar'sbeamintersectsalayerofmeltingprecipitation,reectivityvaluesare higherthannormalasmeltingsnowakesreectthesameamountofenergyasverylarge raindrops.Thislayerofmeltingprecipitationthatoccursjustbelowthefreezinglevelis termedthebrightband(Grayetal.2001;GourleyandCalvert2003).Convertingerroneouslyhighreectivityvaluesfromthebrightbandintorainfallratescausesrainfalltotals tobeoverestimated(Bechinietal.2008;GourleyandCalvert2003;Smith1986).Also, identicationofthebrightbandisimportantwhencompositeradarreectivitydataare utilized(e.g.Matyas2009)asthecompositedatafeaturethemaximumreectivityvalue foundatanyaltitudeforeachdatabin(Zhangetal.2008).Algorithmsimplemented toremovetheseregionsfromrainfallcalculationshaveproveneffective(Gourleyand Calvert2003;Legates2000). CreatingaMosaicofNEXRADData Togainacompleteunderstandingofthechangesinasinglestormasitpasseswithin rangeofseveralradarsites,ortoanalyzesynoptic-scaleweathersystemsintheirentirety (e.g.Figure6),itisnecessarytocombinedatafromneighboringradarsitesintoasingle mosaic.Creatingamosaicthatisspatiallyandtemporallyaccuratepresentsnumerous Fig.3.Thealtitudeofthereectivityreturnsdetectedduringavolumescanoftheatmospherebytheradar locatedinOklahomaCity,Oklahoma(KTLX)duringaseverethunderstormeventon20June2007.1224Radarandclimatology 2010TheAuthor GeographyCompass 4/9(2010):1218–1237,10.1111/j.1749-8198.2010.00370.x GeographyCompass 2010BlackwellPublishingLtd

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challenges.NEXRADcoverage(Figure1)issuchthatsomelocationsaresampledbyas manyaseightradarsatanaltitudeof8km,whileotherlocationsareonlysampledby oneradar(Basaraetal.2007;Maddoxetal.2002;Zhangetal.2005).Scanningbymultipleradarsimprovesdataqualitybyfacilitatingtheidenticationofgroundclutterand anomalouspropagation,andprovidingadditionaldataalongtheverticalscaletollinthe coneofsilence'immediatelyabovetheradar(Figure2b)wherescanningisnotpossible (Lakshmananetal.2006;Maddoxetal.1999).Unfortunately,therearelocationswithin theUSA,particularlyintheintermountainwest,whereradarcoverageisnotavailable duetobeamblockingbytheelevatedterrain(Gourleyetal.2002;Legates2000).Asthe elevationofeachradarvariesacrosstheUSA,thelowest-levelscaninregionsofhighterrainwillbehundredsofmetersabovethebase-levelscanofaradarlocatednearsealevel (Maddoxetal.2002;Woodetal.2003).Thus,differencesinelevationmustbeconsideredwhencreatingamosaic. FurthercomplicatingtheprocessofcreatingamosaicisthateachWSR-88Dunitmay becalibrateddifferently,causingittooverorunderestimatereectivityvalues(Basaraetal. 2007;DelobbeandHolleman2006;Xuetal.2008).Cold,orunder-calibratedradars,may underestimatetheintensityofrainfallandtheoverallseverityoftheweatherevent,whilea hot,orover-calibratedradar,mayoverestimaterainfallrates.Differencesinreectivity valuesoftwotomorethanvedecibelsarecommonamongstradarsintheWSR-88D network(Zhangetal.2005).Whenreectivitydatafrommiscalibratedradarsare Fig.4.CompositereectivitydatafromtheradarlocatedinOklahomaCity,Oklahomaduringasevereweather eventon20June,2007.ThesedataarefromthesametimeasthosedepictedinFigure3.Radarandclimatology1225 2010TheAuthor GeographyCompass 4/9(2010):1218–1237,10.1111/j.1749-8198.2010.00370.x GeographyCompass 2010BlackwellPublishingLtd

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convertedtorainfalltotals,heavyprecipitationeventstendtobeunderestimated,while totalsfromlighterrainfalleventsareoverestimated(Legates2000).Williamsetal.(2005) foundthataccuratedetectionofshort-livedconvectivecellsfeaturingalargegradientin reectivitysufferedthemostfromradarcalibrationissues.Fortunately,ParkerandKnievel (2005)foundthattheproblemisreducedwhenexaminingreectivityvaluesof40dB Z orhigher,whicharegenerallyutilizedtodelineateconvectiverainfall(Jorgensen1984; Matyas2009). Fig.5.OnehourprecipitationtotalsestimatedbytheradarlocatedinPeachtreeCity,Georgia(KFFC)duringthe rainfalleventof21September2009thatledtooodinginandaroundAtlanta,Georgia. Fig.6.SideviewofbasereectivitydatafromtwoadjacentradarsduringthelandfallofHurricaneFloyd(1999) illustratingtheoverlapofthedatainspace.1226Radarandclimatology 2010TheAuthor GeographyCompass 4/9(2010):1218–1237,10.1111/j.1749-8198.2010.00370.x GeographyCompass 2010BlackwellPublishingLtd

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Issueswiththetimingofdatacollectionmustbeconsideredwhendatafromoneradar siteareanalyzed,butbecomemorechallengingwhencreatingamosaicofdatafromseveralradars(Lakshmananetal.2006;Yangetal.2009).Whenscanninginprecipitation mode,eachscantakesplaceovera56minperiod(Crumetal.1998;KlazuraandImy 1993;Yangetal.2009).Asthedatacollectedfortherst1 arctobesensedareolder thanthedatareturnedfromthelastarcofthesweep,thesetwospatiallycontiguousarcs donotdisplaytemporallycontinuousdata.Whendatafromtwooverlappingradarsare considered,asmallandfast-movingcellmayappearattwodifferentlocationsifscan timesaregreatlyoffset(Yangetal.2009).Dependingonthetypeofweatherthatis occurring,neighboringradarsmayutilizedifferentVCPswhosescantimesrangefrom4 to10min(Yangetal.2009).Also,astheclocksoftheradarsarenotsynchronized (Lakshmananetal.2006;Yangetal.2009),eachscancouldbeginatadifferenttimefor eachradarthatcomprisesthemosaic. Thefactorspreviouslydiscussedmayleadtomultiplereectivityvaluesbeingavailable forthesamelocation(Figure6).Tocreateanaccuratemosaic,researchershaveinvestigatedseveralinterpolationstrategiesforselectingareectivityvalueforeachgridcell (Lakshmananetal.2006,2007;TrappandDoswell2000;Yangetal.2009;Zhangetal. 2005).Innearest-neighbormapping,thereectivityvaluefromtheradarthatisclosest tothegridcellisutilized.Thisstrategyminimizesproblemswithlowreectivityvalues producedbyawidebeamthattravelsfarfromtheradar,butdiscontinuitiesalonglocationsthatareequidistantbetweentworadarsitesareproblematic.Thus,differencesin calibrationandsweeptimes,aswellasbrightbandsamplingappearasstraightlines throughthereectivityvalues.Averagingthereectivityvaluescansmooththedata,but erroneouslyloworhighreectivityvaluesamongthedatathatareaveragedproducea gridcellwhosevalueisalsotoohighorlow.Usingthemaximumreectivityvalue regardlessofwhichradardetectedthisvalueassuresthathigherreectivityvaluescomprisingsmall-scalefeatures,suchasindividualconvectivethunderstorms,arenotltered outbyasmoothingstrategy,butthegridbecomesbiasedtowardshot'radars.Calculating aweightedmeansuchthattheclosestradarcarriesthehighestweightcanprovidea methodforsmoothingdatawhilelimitingproblemswithbeamwidthandlowerreectivityvaluessensedatfardistances.Zhangetal.(2005)andLangstonetal.(2007)found thisstrategytoproducethemostaccuratemosaic.AstheanalysisofLangstonetal. (2007)alsoincludesatemporalcomponent,theyaddthatatemporalweightingscheme utilizinganexponentially-decayingweightingfunctionimprovesaccuracyoverthe methodemployedbyZhangetal.(2005).Asgeographersutilizemanymethodsfor spatialinterpolation(e.g.BurroughandMcDonnell1998;Goodchild1992,2003; Goodchildetal.1993;Lam1983;Miller2004;Mugglinetal.1999,),furthertechniques tointerpolateradardatacouldbedevelopedbygeographers. UsingRadartoEstimateRainfall Toestimaterainfallratesfromradarreectivityvalues,the Z R relationshipisutilized where Z istheradarreectivityvalue(mm6m) 3)and R istherainfallrate(mmh) 1) (MarshallandPalmer1948).Thesevariablesarerelatedviaapowerfunctionsothat Z dependsuponthesizedistributionandraindropdiametertothesixthpower,and R dependsupontheraindropsizedistribution,thesizeofthedropstothethirdpower,and thefallvelocityofdropsofagivendiameter.Giventhedifferenttypesofprecipitation, stormtypes,andstormintensities,itisnotpossibleforasingle Z R relationshiptoaccuratelyderiverainfallratesineverygivensituation(Collier1996;Hardegreeetal.2008).Radarandclimatology1227 2010TheAuthor GeographyCompass 4/9(2010):1218–1237,10.1111/j.1749-8198.2010.00370.x GeographyCompass 2010BlackwellPublishingLtd

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Marshalletal.(1947)suggested Z =200 R1.6,andtoday,theNWSuses Z =300 R1.4as thedefaultequation(Fultonetal.1998).Table3liststhevarietyof Z R relationships thatcanbeemployedateachradarsiteandtherainfallratesforreectivityvaluesof 20dB Z representingaccumulatingstratiformprecipitation,and40dB Z representingthe higherrainratesofconvectiveprecipitation. Givenitsabilitytocollectdataoveraspatiallycontinuousareacoveringapproximately 166000km2,radarhasbeenaninvaluabletoolutilizedtodeveloprainfallclimatologies. PriortotheestablishmentofNEXRAD,raingaugesweretheprimarysourceofrainfallrelateddata.However,thespatialcoverageofgaugedataisinadequatetodevelopa spatially-accuraterainfallclimatologyasHabibetal.(2009)estimatethatonly1.3gauges areavailableper1000km2acrosstheUSA.Toofewgaugesarepresentinmostregions todetectthemajorityofsmall-scaleconvectiverainfallevents,andgaugesoftendonot capturethepeakrainfallproducedbyastorm(AllenandDeGaetano2005;Habibetal. 2009;Lietal.2008).Additionally,raingaugemeasurementsmaybeerroneousdueto theeffectsofturbulenceandincreasedwindowaroundthegaugesothatundercatch maybe2040%duringastrongthunderstorm(WilsonandBrandes1979). Despitetheirlimitations,raingaugedataprovideagroundtruthtowhichradar-derived datacanbecompared(DeGaetanoandWilks2009;Hardegreeetal.2008;Traperoetal. 2009).Datafromraingaugeshavebeenutilizedtodeterminethat15dB Z reectivity valuesdonotproducerainfallthataccumulatesontheground,thusmoststudiesutilize 20dB Z asathresholdforstratiformcloudsthatproducerainfall(AllenandDeGaetano 2005;GorokhovichandVillarini2005;Matyas2007).AttheNWSRiverForecastCenters, raingaugedataarecombinedwithradardatatocreateanhourlyprecipitationmapwitha spatialresolutionof16km2onanationalpolargridknownastheHydrologicRainfall AnalysisProjectgrid(Fultonetal.1998;Habibetal.2009;Hardegreeetal.2008).Data fromtheHRAPgridareutilizedbyRiverForecastCentersforrivermodelingactivities andoodforecasting,andVieux(2001)describestherelativeeasewithwhichHRAPdata areimportedintoaGIS,thusmakingthemsuitableforanalysisbygeographers. Radar-basedClimatologiesofHazardousWeatherEventsandOpportunitiesforGeographicResearch Climatological-scalestudiesoflakeeffectsnowthatincorporateradarreectivitydata havebeenpublishedfortheGreatSaltLake(Steenburghetal.2000),theFingerLakesof NewYork(Lairdetal.2009a),andLakeChamplaininVermont(Lairdetal.2009b). Theuseofreectivitydataallowsthequanticationofthespatialcoverageandorientationofcloudbands,andestimationofsnowfalltotals(Lairdetal.2009a;Rodriguezetal. Table3.Five Z R relationshipsemployedatWSR-88DsitesRelationshipEquationUse 20dB Z rain rate(mm hr) 1) 40dB Z rain rate(mm hr) 1) Marshall–Palmer Z =200 R1.6Stratiform0.7611.43 East-CoolStratiform Z =130 R2.0Winterstratiformeastof continentaldivide 1.028.89 West-CoolStratiform Z =75 R2.0Winterstratiformwestof continentaldivide 1.2711.68 Convective(Default) Z =300 R1.4Summerdeepconvection, non-tropical 0.5112.19 RosenfeldTropical Z =250 R1.2Tropicalconvection0.5121.591228Radarandclimatology 2010TheAuthor GeographyCompass 4/9(2010):1218–1237,10.1111/j.1749-8198.2010.00370.x GeographyCompass 2010BlackwellPublishingLtd

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2007).Astheanalysisofradarreectivityreturnsallowstocalculationofstatisticssuchas meanandmedianreectivity,thepercentageoftimethatreectivityvaluesequalor exceedthresholdvalues,andthespatialcoverageofeachthresholdvalue,itispossiblefor otherresearchers,includinggeographers,toutilizeNEXRADdatatoconstructsnowfall climatologiesforotherlakes.Asconventionalsurfacedataarenotofahighenoughspatialortemporalresolutiontoidentifylocalconvergencezones,landandlakebreezes,and rollconvectivepatternsthatareprecursorstolakeeffectsnowevents(Kristovichetal. 1999;Steenburghetal.2000),thesefeaturesmayalsobeobservedbyradartoimprove thepredictionoflakeeffectsnowfallevents. Theappearanceofacriticalreectivitythresholdhashelpedresearchersidentifycold seasonweathereventssuchasbowechoes,freezingdrizzle,andthundersnow.Reectivitydatawereessentialtocharacterizechangesduringthelifecyclesof51coldseason bowechoesexaminedbyBurkeandSchultz(2004).Bowechoescanproducewidespreaddamagingwindsandareidentiedinradarimagesbytheircurvedshapethat expandsovertimewithatightreectivitygradientontheirleadingedge.Burkeand Schultz(2004)utilizedtheappearanceofa40dB Z echowithinthesqualllinetodenote thestartofabowecho,andthencalculateditsduration.Recently,Ikedaetal.(2009) developedamethodtoidentifyradarreectivitypatternsthatareassociatedwithfreezing drizzle,whichisahazardforaircraftbothin-ightandontheground.Theirdetection schemecorrectlyclassiedfreezingdrizzleevents70%ofthetimeduetothepresenceof reectivityvaluesunder5dB Z anduniformechoeldsinthehorizontaldirection. AccordingtoSteigeretal.(2009),morethan20%oflakeeffectsnoweventsinwestern NewYorkproducelightning.Theyutilizedaradarreectivitythresholdof35dB Z to conrmthepossibilityofthunderstormactivityastheirprimaryaccountsoflightning werefromstormspotters.Theysuggestthatfutureresearchersutilizereectivitydatato examinethestructureofthesestorms,andtheanalysisofspatialpatternsdescribedinall threeofthesepaperscouldbeperformedbygeographer-climatologists. ClimatologicalstudiesutilizingWSR-88Ddataofthunderstormsthatproducehail haveimprovedhailpredictionanddetectionalgorithms(Basaraetal.2007;Billetetal. 1997;Mallafreetal.2009).Itcanbedifculttodistinguishasmallnumberoflargehailstonesfromalargenumberofsmallhailstonesasreectivityvaluesaredependentonboth thesizeandnumberofhydrometeors(DelobbeandHolleman2006).Thus,reectivity valuesgreaterthanorequalto55dB Z aregenerallyassumedtocontainhail(Delobbe andHolleman2006;Fultonetal.1998),andresearchershaverelatedtheprobabilitythat astormwillproducelargehailtotheverticallyintegratedliquid(VIL)productproduced fromLevelIIradardata(Billetetal.1997;EdwardsandThompson1998;Holleman etal.2000;LopezandSanchez2009).InadditiontousingVIL,theprobabilityofhail hasalsobeendeterminedutilizingthe45dB Z echotopheight(DelobbeandHolleman 2006;Waldvogeletal.1979).Hailclimatologiesutilizingradardata,aswellasdatafrom atmosphericsoundingsandgroundobservations,havebeendevelopedforthesouthern plains(Basaraetal.2007),theWashingtonDCarea(Billetetal.1997;Donavonand Jungbluth2007),andOklahoma(WittandNelson1991).However,moreresearch examiningvariousradar-basedpredictionthresholdsforhailsizeisneededforother regionsofthecountryasatmosphericdynamicsandthermodynamicsexhibitregionalvariability(EdwardsandThompson1998). Incorporatinglightningdataintoradar-basedthunderstormclimatologiescanhelpto separatestratiformandconvectiverainfallandindicatethestageofdevelopmentofa stormcell(Steinackeretal.2000;TadesseandAnagnostou2009).Lightningactivity commenceswhenupdraftsreachthe ) 10 Cisotherm(GremillionandOrville1999;Radarandclimatology1229 2010TheAuthor GeographyCompass 4/9(2010):1218–1237,10.1111/j.1749-8198.2010.00370.x GeographyCompass 2010BlackwellPublishingLtd

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LhermitteandKrehbiel1979;Toracintaetal.1996),andoncereectivityvaluesincrease above35dB Z (Tapiaetal.1998;Toracintaetal.1996).Thegreatestashdensityoccurs inthetallestconvectivecloudswhere40dB Z reectivityvaluesarepresentatanaltitude above5km(Proctor1991;Steigeretal.2007;Tapiaetal.1998;Toracintaetal. 1996).TadesseandAnagnostou(2009)foundthatahigherdensityofashescoincides withalongerdurationofthestorm.Duetothestrongrelationshipbetweenradarreectivityandlightningashoccurrence,Tapiaetal.(1998)concludedthatlightningash datacouldbeutilizedtodetectheavyrainfalleventsassociatedwithconvectiverainfallin locationswhereradardataarenotavailable. ManyoftheNEXRAD-basedstudiesoftropicalcyclonesarecasestudiesoftheevolutionofconvectivethunderstormsandprecipitationwithintheeyewallorouterspiralrainbands(e.g.Blackwell2000;BluesteinandHazen1989;Leeetal.2008;Matyas2009; Medlinetal.2007;UlbrichandLee2002),tornadoes(e.g.Bakeretal.2009;McCaul etal.2004),orwindelds(e.g.Arndtetal.2009;Blackwell2000;ZhaoandJin2008). Fewerstudieshaveexaminedthesecharacteristicsthrougharadar-basedanalysisacross multiplestorms.PowellandHouston(1998)examinedthewindeldsoffourhurricanes thatmadelandfallduring1995utilizingbothWSR-88DandairborneDopplerradar, whileGalletal.(1998)examinedthespiralrainbandslocatednearthecoresofHurricanes Hugo(1989),Andrew(1992)andErin(1995).Schroederetal.(2009)haveinvestigated theprecipitationstructuresofmultipletropicalcyclonesthroughcombineduseofWSR88Ddataandmobileinstrumenttowers.Matyas(2006,2007,2010a,b)hasemployedGIS toanalyzeWSR-88Dreectivitydataformultipletropicalcyclones.Reectivitydatafrom theLevelIIIbasescanorlowestscanelevationcontainedwithintheLevelIIdataare mosaickedwithinaGISandpropertiesoftheraineldsincludingtheirarea,compactness, elongation,fragmentation,extentoutwardsfromthecirculationcenter,andthepositions oftheircentroidsarecalculatedandthencomparedtotheenvironmentalconditionsthat eachstormexperiences(e.g.verticalwindshear,relativehumidity,etc.).Quantifyingthe spatialattributesofconvectiveandstratiformregionswithintropicalcyclonescouldhelp toimprovethespatialaccuracyofraineldssimulatedinmodeledstorms. RadarDataandGIS Radar-derivedclimatologiesofweathereventssuchasthosedescribedintheprevious sectioncouldbeconstructedbygeographersemployingGIS.Asconvertingradardataintoa formatsuitableforanalysiswithinaGISwasdifcultpriorto2005,thiscouldbeoneexplanationastowhyanalysiswithradardataisnotroutinelyconductedbygeographers.In 2005,researchersattheNCDCdevelopedtheJavaNEXRADExporter(AnsariandDel Greco2005)thatgeoreferencesthedataasagridoflatitudinalandlongitudinalcoordinates basedonaWorldGeodeticSystemspheroid(WGS84)modeloftheearth.Thedatacan thenbeconvertedintoseveralformatsincludingpolygonshapeles,GeoTiff,andnetCDF. ThistoolhasbeenutilizedbyresearcherssuchasMoteetal.(2007)andMatyas(2010b)to convertlargevolumesofWSR-88DreectivitydataintoaGIS-friendlyformatsothat climatological-scaleanalysescouldbeperformed.Nowupgradedandre-titledtheWeather andClimateToolkit,thistoolkitisavailablefromtheNCDContheirwebsite(NCDC 2009b),whichshouldfacilitatetheuseofthesedatabygeographers. TherecentincreaseintheuseofGISbyscientistswhoarenotgeographerssuggeststhat itisbecominganacceptedtoolforuseatmosphericresearch.In2005, MeteorologicalApplications ,ajournalpublishedbytheRoyalMeteorologicalSociety,dedicatedaspecialissueto theuseofGISinatmosphericresearch(Thornes2005).However,onlyfourcoauthorsout1230Radarandclimatology 2010TheAuthor GeographyCompass 4/9(2010):1218–1237,10.1111/j.1749-8198.2010.00370.x GeographyCompass 2010BlackwellPublishingLtd

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ofthe49whocontributedtomanuscriptsinthisissueof MeteorologicalApplications were afliatedwithaGeographydepartment(Dyrasetal.2005;MadelinandBeltrando2005; Thornesetal.2005).Alsoin2005,the BulletinoftheAmericanMeteorologicalSociety published veshortarticlesthatfeaturedtheuseofGISforatmosphericscienceresearch.Yuan(2005) describedherresearchontornadodamagetracks,andShipley(2005)reportedthatthe NWSOfceofScienceandTechnology ‡ SystemsEngineeringCenterbeganconverting WSR-88DdataintoashapeleformatforutilizationinaGIS.Threeotherarticles (Habermann2005;Krugeretal.2005;WilhelmiandBetancourt2005)discussedhowthe UniversityCorporationforAtmosphericResearch(UCAR)andtheNationalCenterfor AtmosphericResearch(NCAR)aimtoincorporateGISintotheirdatadisseminationand analysisplans.Thus,opportunitiesexistforgeographerswhoutilizeGIStocontributemore frequentlytoatmosphericresearch. Severalgeographershavedescribedtheproblemsinherentintheanalysisoftemporal datawithinaGIS(Egenhoferetal.1999;Galton2001;O'Sullivan2005;Peuquet2001), andthiscouldbetheprimaryreasonwhyrelativelyfewgeographersemployittoanalyze radardata.Mosttemporaldatabasemodelsutilizesnapshotstodeterminechangesover time,butthisdoesnotallowanexplicitrepresentationofaneventoccurrence(Worboys 2005).However,researchersaredevelopingtoolscapableoftemporalanalysisofspatial data.Forexample,Edsalletal.(2000)demonstratetheirtoolfortemporalFourieranalysis withdatafromraingaugescollectedover180days;itisreasonabletoassumethatradarestimatedrainfalldatacouldbeanalyzedusingtheirtechnique.Iftemporalanalysistools couldbeincorporatedintowidely-availableGISsoftware,perhapsgeographerscouldperfectGIS-basedtechniquesforaccuratespatialandtemporalanalysesofweatherphenomenondetectedbyradardata. Geographersspecializeinmodelingspatialphenomenon(e.g.WilmottandGaile 1992).Ifmanynon-geographersareutilizingGISandradardataintheirwork,then moregeographersshouldalsobegintoperformtheGIS-basedanalysisofradarreectivity dataandrainfallestimates.AsaGISfacilitatesthegeospatialanalysisoflargevolumesof data,ithasbecomemorewidelyusedforprecipitationmappingandhydrologicmodeling (DyrasandSeran-Rek2005;Fangetal.2008;Knebletal.2005;Whiteakeretal.2006; Xieetal.2005).Thecollaborationofgeographersspecializingingeospatialtechniques withthosepursuingresearchinclimatologyandhydrologycouldprovidespecializedtools forthegeovisualizationandanalysisofradarreectivityandvelocitydatawithinaGIS. Utilizingthehightemporalandspatialresolutionprovidedbyradardata,geographers, withtheirexpertiseinspatialmodeling,havethepotentialtomakeimportantcontributionstoourunderstandingofweathersystems. Acknowledgement ThankyoutoDr.MarshallShepherdfortheinvitationtocontributethismanuscriptto GeographyCompassandtotwoanonymousreviewersfortheirhelpfulcomments.Ialso acknowledgetheaudiencememberwhoaskedmewhetherclimatologistsshoulduse radardataintheirresearchduringtheStudentPaperCompetitionatthe2003annual meetingoftheAssociationofAmericanGeographers. ShortBiography CoreneMatyasisanAssistantProfessorattheUniversityofFlorida,Gainesville,Florida, USA.HerprimaryresearchfocusesontheGIS-basedanalysisoftropicalcyclones.InRadarandclimatology1231 2010TheAuthor GeographyCompass 4/9(2010):1218–1237,10.1111/j.1749-8198.2010.00370.x GeographyCompass 2010BlackwellPublishingLtd

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muchofherwork,sheutilizesGIStospatiallyanalyzeradarreectivitydata.Herwork hasbeenpublishedinTheProfessionalGeographer,JournalofAppliedMeteorologyand Climatology,TheoreticalandAppliedClimatology,MeteorologyandAtmosphericPhysics,andInternationalJournalofAppliedGeospatialResearch.ShereceivedaTeacherof theYearAwardfromtheCollegeofLiberalArtsandSciencesattheUniversityofFloridain2009.SheholdsaB.S.inEnvironmentalGeosciencefromClarionUniversityof Pennsylvania,M.A.inGeographyfromArizonaStateUniversity,andPh.D.inGeographyfromPennsylvaniaStateUniversity. Note*Correspondingaddress:CoreneJ.Matyas,DepartmentofGeography,UniversityofFlorida,Gainesville,FL, 32611-7315,USA.E-mail:matyas@u.eduReferencesAllen,R.J.andDeGaetano,A.T.(2005).Considerationsfortheuseofradar-derivedprecipitationestimatesin determiningreturnintervalsforextremearealprecipitationamounts. JournalofHydrology 315(14),pp.203219. Ansari,S.andDelGreco,S.(2005).GIStoolsforvisualizationandanalysisofNEXRADradar(WSR-88D) archiveddataattheNationalClimaticDataCenter. 21stInternationalConferenceonInteractiveInformationProcessing Systems(IIPS)forMeteorology,Oceanography,andHydrology ,SanDiego,CA. Arndt,D.S.,etal.(2009).ObservationsoftheoverlandreintensicationofTropicalStormErin(2007). Bulletinof theAmericanMeteorologicalSociety 90(8),pp.10791093. Ashley,S.T.andAshley,W.S.(2008).ThestormmorphologyofdeadlyoodingeventsintheUnitedStates. InternationalJournalofClimatology 28(4),pp.493503. Atlas,D.(1990). Radarinmeteorology .Chicago:UniversityofChicagoPress. Baker,A.K.,Parker,M.D.andEastin,M.D.(2009).Environmentalingredientsforsupercellsandtornadoes withinHurricaneIvan. WeatherandForecasting 24(1),pp.223244. Basara,J.B.,Mitchell,D.,Cheresnick,D.R.andIllston,B.G.(2007).Ananalysisofseverehailswathsinthe SouthernPlainsoftheUnitedStates. TransactionsinGIS 11(4),pp.531554. Bechini,R.,Baldini,L.,Cremonini,R.andGorgucci,E.(2008).Differentialreectivitycalibrationforoperational radars. JournalofAtmosphericandOceanicTechnology 25(9),pp.15421555. Billet,J.,DeLisi,M.,Smith,B.G.andGates,C.(1997).Useofregressiontechniquestopredicthailsizeandthe probabilityoflargehail. WeatherandForecasting 12(1),pp.154164. Blackwell,K.G.(2000).EvolutionofHurricaneDanny(1997)atlandfall:Doppler-observedeyewallreplacement, vortexcontraction ‡ intensication,andlow-levelwindmaxima. MonthlyWeatherReview 128(12),pp.40024016. Bluestein,H.B.andHazen,D.S.(1989).Doppler-radaranalysisofatropicalcycloneoverlandHurricaneAlicia (1983)inOklahoma. MonthlyWeatherReview 117(11),pp.25942611. Brunsell,N.A.andYoung,C.B.(2008).LandsurfaceresponsetoprecipitationeventsusingMODISandNEXRADdata. InternationalJournalofRemoteSensing 29(7),pp.19651982. Burgess,D.andRay,P.S.(1986). Principlesofradar.Mesoscalemeteorologyandforecasting .Boston:AmericanMeteorologicalSociety. Burke,P.C.andSchultz,D.M.(2004).A4-yrclimatologyofcold-seasonbowechoesoverthecontinental UnitedStates. WeatherandForecasting 19(6),pp.10611074. Burrough,P.A.andMcDonnell,R.A.(1998). Principlesofgeographicalinformationsystems .NewYork:Oxford UniversityPress. Cao,Y.,Yang,C.W.andWong,D.W.(2009).Aninteroperablespatiotemporalweatherradardatadissemination system. InternationalJournalofRemoteSensing 30(5),pp.13131326. Carleton,A.M.,etal.(2008).SynopticcirculationandlandsurfaceinuencesonconvectionintheMidwestU.S. CornBelt'',summers1999and2000.PartI:Compositesynopticenvironments. JournalofClimate 21(14),pp. 33893415. Carpenter,T.M.,etal.(1999).NationalthresholdrunoffestimationutilizingGISinsupportofoperationalash oodwarningsystems. JournalofHydrology 224(12),pp.2144. Chang,P.L.,Lin,P.F.,Jou,B.J.D.andZhang,J.(2009).Anapplicationofreectivityclimatologyinconstructingradarhybridscansovercomplexterrain. JournalofAtmosphericandOceanicTechnology 26(7),pp.13151327. Collier,C.G.(1996). Applicationsofweatherradarsystems .Chichester,UK:JohnWiley&Sons,Inc. Collier,C.G.(2009).Onthepropagationofuncertaintyinweatherradarestimatesofrainfallthroughhydrological models. MeteorologicalApplications 16(1),pp.3540.1232Radarandclimatology 2010TheAuthor GeographyCompass 4/9(2010):1218–1237,10.1111/j.1749-8198.2010.00370.x GeographyCompass 2010BlackwellPublishingLtd

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Cosgrove,B.A.,etal.(2003).Real-timeandretrospectiveforcingintheNorthAmericanLandDataAssimilation System(NLDAS)project. JournalofGeophysicalResearch-Atmospheres 108(D22),pp.12. Croft,P.J.andShulman,M.D.(1989).A5-yearradarclimatologyofconvectiveprecipitationforNewJersey. InternationalJournalofClimatology 9(6),pp.581600. Crum,T.D.,Safe,R.E.andWilson,J.W.(1998).AnupdateontheNEXRADprogramandfutureWSR-88D supporttooperations. WeatherandForecasting 13(2),pp.253262. DeGaetano,A.T.andWilks,D.S.(2009).Radar-guidedinterpolationofclimatologicalprecipitationdata. InternationalJournalofClimatology 29(2),pp.185196. Delobbe,L.andHolleman,I.(2006).Uncertaintiesinradarechotopheightsusedforhaildetection. Meteorological Applications 13(4),pp.361374. Donavon,R.A.andJungbluth,K.A.(2007).Evaluationofatechniqueforradaridenticationoflargehailacross theuppermidwestandcentralplainsoftheUnitedStates. WeatherandForecasting 22(2),pp.244254. Doviak,R.J.andZrnic,D.S.(1993). Dopplerradarandweatherobservations .SanDiego,CA:AcademicPress. Doviak,R.J.,etal.(2000).ConsiderationsforpolarimetricupgradestooperationalWSR-88Dradars. Journalof AtmosphericandOceanicTechnology 17(3),pp.257278. Dyer,J.(2009).Evaluationofsurfaceandradar-estimatedprecipitationdatasourcesovertheLowerMississippi Riveralluvialplain. PhysicalGeography 30(5),pp.430452. Dyras,I.andSeran-Rek,D.(2005).TheapplicationofGIStechnologyforprecipitationmapping. Meteorological Applications 12(1),pp.6975. Dyras,I.,etal.(2005).Theuseofgeographicinformationsystemsinclimatologyandmeteorology:COST719. MeteorologicalApplications 12(1),pp.15. Edsall,R.M.,Harrower,M.andMennis,J.L.(2000).Toolsforvisualizingpropertiesofspatialandtemporalperiodicityingeographicdata. ComputersandGeosciences 26(1),pp.109118. Edwards,R.andThompson,R.L.(1998).NationwidecomparisonsofhailsizewithWSR-88Dverticallyintegratedliquidwaterandderivedthermodynamicsoundingdata. WeatherandForecasting 13(2),pp.277285. Egenhofer,M.J.,etal.(1999).Progressincomputationalmethodsforrepresentinggeographicalconcepts. InternationalJournalofGeographicalInformationScience 13(8),pp.775796. Fang,Z.,Bedient,P.B.,Benavides,J.andZimmer,A.L.(2008).Enhancedradar-basedoodalertsystemand oodplainmaplibrary. JournalofHydrologicEngineering 13(10),pp.926938. Fulton,R.A.,etal.(1998).TheWSR-88Drainfallalgorithm. WeatherandForecasting 13(2),pp.377395. Gall,R.,Tuttle,J.andHildebrand,P.(1998).Small-scalespiralbandsobservedinHurricanesAndrew,Hugo,and Erin. MonthlyWeatherReview 126(7),pp.17491766. Galton,A.(2001).Space,time,andtherepresentationofgeographicalreality. Topoi-anInternationalReviewofPhilosophy 20(2),pp.173187. Gauthier,M.L.,Petersen,W.A.,Carey,L.D.andChristian,H.J.(2006).Relationshipbetweencloud-to-ground lightningandprecipitationicemass:aradarstudyoverHouston. GeophysicalResearchLetters 33(20),pp.5. Goodchild,M.F.(1992).Geographicaldatamodeling. ComputersandGeosciences 18(4),pp.401408. Goodchild,M.E.(2003).Geographicinformationscienceandsystemsforenvironmentalmanagement. Annual ReviewofEnvironmentandResources 28,pp.493519. Goodchild,M.F.,Parks,B.O.andSteyeart,L.T.(1993). EnvironmentalModelingwithGIS .Oxford:Oxford UniversityPress. Gorokhovich,Y.andVillarini,G.(2005).ApplicationofGISforprocessingandestablishingthecorrelation betweenweatherradarreectivityandprecipitationdata. MeteorologicalApplications 12(1),pp.9199. Gourley,J.J.andCalvert,C.M.(2003).AutomateddetectionofthebrightbandusingWSR-88Ddata. Weather andForecasting 18(4),pp.585599. Gourley,J.J.,Maddox,R.A.,Howard,K.W.andBurgess,D.W.(2002).Anexploratorymultisensortechnique forquantitativeestimationofstratiformrainfall. JournalofHydrometeorology 3(2),pp.166180. Gremillion,M.S.andOrville,R.E.(1999).Thunderstormcharacteristicsofcloud-to-groundlightningatthe KennedySpaceCenter,Florida:astudyoflightninginitiationsignaturesasindicatedbytheWSR-88D. Weather andForecasting 14(5),pp.640649. Habermann,T.(2005).WhatisGIS(forUnidata)?BulletinoftheAmericanMeteorologicalSociety 86(2),pp.174175. Habib,E.,Larson,B.F.andGraschel,J.(2009).ValidationofNEXRADmultisensorprecipitationestimatesusing anexperimentaldenseraingaugenetworkinsouthLouisiana. JournalofHydrology 373(34),pp.463478. Hardegree,S.P.,VanVactor,S.S.,Levinson,D.H.andWinstra,A.H.(2008).EvaluationofNEXRADradar precipitationproductsfornaturalresourceapplications. RangelandEcologyandManagement 61(3),pp.346353. Hocker,J.E.andBasara,J.B.(2008).A10-yearspatialclimatologyofsqualllinestormsacrossOklahoma. InternationalJournalofClimatology 28(6),pp.765775. Hoium,D.K.,Riordan,A.J.,Monahan,J.andKeeter,K.K.(1997).Severethunderstormandtornadowarnings atRaleigh,NorthCarolina. BulletinoftheAmericanMeteorologicalSociety 78(11),pp.25592575. Holleman,I.,Wessels,H.R.A.,Onvlee,J.R.A.andBarlag,S.J.M.(2000).Developmentofahail-detectionproduct. 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