城市交通管理及改革毕业设计方案

摘要上段时间闹的轰轰烈烈的西安停车难事件，映射出我国大中城市停车难的问题进入普及化的阶段,再次使得我国地方政府对于城市交通管理和发展的问题受到广大民众和专家的热议。而另一方面，哈大高铁的落成和运行，体现出我国对于新兴的交通工具的发展和应用得到进一步的提升.我国在“十二五”之后,党中央、国务院对于城市交通的发展和管理的重视程度越来越大，地方政府也越来越重视解决城市交通的问题，务求令到现在各个城市都普遍存在的交通堵塞、停车难、城市交通管理水平低下等问题得到缓解和解决。而进入信息时代，对于城市交通管理的发展，则必须要紧紧地把握好新时代下创新的管理理论与先进的科技手段两者相互结合,充分发挥城市交通对于城市社会经济发展、人民安居乐业的积极作用.因此，本文着重研究我国城市管理的现状，借鉴先进城市的交通管理经验，探讨我国城市交通管理未来发展的方向与目标.关键词：城市交通管理;交通道路系统；交通管理理论;交通管理改革

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ＰＡＧEREF_Toc358724194＼h1HＹPERLINK\l”＿Tｏc3５8724１95＂2。我国城市交通发展现状与问题ﻩPＡGEREF＿Toc358724195＼ｈ2HYPERLINKisnoｔaｔecｈnｉcaｌｐroｂleｍthatcanbesｉmplysolｖed.Prｅｄｉcｔingthｅweatheｒｉsadｉffｉｃｕltandcomplｅxｔaskandonｌypossiblewiｔhiｎcerｔaiｎlimits.Ｉｔｉsｔｈereｆｏｒeｎｅｃeｓsarytｏobserｖeａnｄfoｒecastｔhｅcｈangｉngstateofｔheａｔmosｐhｅｒｅaspreciｓelyaｎdａsrａpiｄlyaｓpoｓsiｂlｅ。Moreovｅr，measｕｒesareｒeｑuｉｒｅdｔhａｔtraｎslａte“ｗeatｈer”ｔo“impact”andmiｎimiseｔhoseimpactsontｒafficflｏwandiｔsmａnagemｅnt。Toinｆoｒｍｔｒafficparｔicｉpａnｔｓａndtｒaｆficmanageｍｅnｔcentresiｎduｅtimeｏn（exｐｅcted)advｅrsｅconditions，tailoredandａccｕｒａtemetｅｏroloｇicaｌinformａtioｎiｓrequｉredonsｈoｒtnotice.Ｔhisiｎfoｒｍａtiｏnｍｕｓtｂｅｉｎteｇraｔeｄiｎｔheprｏcessofinformａtiondistributionandｄeｃｉsiｏnmakingtｏalｌｏwfortａcticalａsweｌｌasstrategicdｅcisions.TｈeInｓtituteofAtmospｈｅｒicPhｙsicsｏftheDeutscheｓZentrumfüｒLｕft－unｄRaumfａhrｔ（ＤLR）inObｅrpｆａffｅｎｈofen，Gｅrmａnｙ,anｄthecompanｙHｙｄrometeｏrolｏｇicａlInｎovatiｖｅSｏlutiｏnsＳ。Ｌ。(HYDS）inBａrcｅｌoｎa,Spain，ｄevelｏpametｅorologiｃaldeciｓioｎsｕpｐｏｒtsysteｍｆｏravｉａtion（MEDUＳA）ｗithｉntheＥＵ’sPeoｐｌeProgramme,Industry-AcademｉaPaｒtneｒshｉpsａndPaｔhwaｙs，andDLR’sＲeｓearcｈAcｔｉviｔy“WｅａtherOptｉmisedAirＴrａnｓpｏｒtation”。Itｓgoalｉstｏaugmeｎtｓaｆetｙandefficｉencyofaｉrtｒansportation。Maｎｙofｔｈｅdｅveｌopedmetｈoｄsfocuｓoｎｔhegrｏundlevelaｎd,thｅrｅｆoｒｅ，canｗeｌlｂｅadaptｅｄanｄappliedforｗeathｅrｄictａtediｓsuesinroadtraｎsｐｏrtａtion，tｏo．Wedemｏｎstｒａtｅourlｏgic,firstｄｅｖｅlopedfｏrtheａviaｔioｎｔranｓporｔseｃtoｒ，tｏcｏｍｂiｎevaｒｉousmeteorｏｌｏgicalｐarametersｔoｓｉmple，sｅlf—explａiningｗeatｈerobｊects．Ｆurtherwｅｒepoｒtoｎreｃeｎｔdｅvｅlopmentstodeｔerminｅtｈeonseｔanｄdurａｔionoficingｃonｄｉtionsａttheｓurｆace.Anａlgｏriｔhmaimｓatｄｅtｅctingpoｔeｎtialａｒｅasofsnｏwfallbyｃｏmbｉningsｃａnniｎｇｒeflectivitydatａofｐｒeｃiｐｉｔatiｏnａndｓｕrfaｃｅｔｅｍpｅratureｄaｔａfｒomａnumerｉcalmodelaswelｌasｓｕｒfacestationｓinｈiｇhspatialｒesolｕtion（１km）．Ａｎotｈerapｐｒｏacｈcoｍbiｎesprｏｆiｌingmeasuｒements（e.ｇ。,meteｏdaｔameasuｒedbyaiｒcrafｔanｄpolａｒimetricradａrdaｔa）wiｔhnumerｉcalwｅatheｒforecasｔprｏｄucts。Ｆorforecａstingwintｅｒweaｔhｅrcoｎditionｓuｐtoａbout24hｏｕrsormore，ｏneｃａnrelｙonoperａtionalnｕmericａlfoｒecaｓtmｏdels.Numerｉcalmodeｌsｈavemadereｍaｒkabｌeprogｒｅssｄuringthelaｓｔfeｗyeaｒsiｎforecａstingtheoverａllweatｈｅrｓtａte,e.g。thesurfaｃepreｓsurediｓtｒiｂｕtionｏｒwhetherｉｔwiｌlraｉnorｎｏt.Tｈeforecaｓtｏfwintｅrwｅatherphｅnomenａ,hｏｗｅvｅr，likｅfrｅeｚingｒainordｒizzlｅ，orlｉghtorheaｖysnowfａlｌiｓｓtillaｄeｍandingtａsｋ。Thｅsｅphenomenareｓultfroｍthesuｂtlｅｉnteｒplayofvａｒiousfactoｒs,lｉkethevｅrticaldistrｉbuｔiｏnｏftｅｍpｅratureandhuｍidiｔy，clｏuｄcoveraｎｄtype，ｓnｏwｃoveｒ，soｉlmoｉsturｅanｄｔheｃoｍpｏsitiｏnoｆthｅaｔmospherewｉｔｓolswhiｃhagaｉninfluｅnｃｅclouｄandpｒeciｐitａｔionprocｅsses．Thｅsitｕａｔｉoｎgｅtseveｎmｏｒｅcomplicatedastheｓeｐｒｏcessｅｓrｅsultｆromiｎｓｔaｂilitieswhicharｅtriggeredbysｍallｃhangeｓｉntheaｔmosphｅrｉｃｐarａｍetｅｒs,ｅ．g。ｗhｅtｈerthetemperatｕreatthegｒｏｕnｄｏｒｔhrｏｕghaｃerｔaｉndeｐtｈofｔheatｍosｐhｅreｉｓslightlyabovｅｏrbｅｌoｗ0°C.Inｏrdeｒtoｂettｅreｓｔｉmatethｅｆutｕｒｅatmosｐｈericstａｔeenｓemblｅmodｅlsgiｖeｂetｔerguidancetｈanａsingｌemodeｌrun。Combｉnedquａｎtitiｅsliｋeｅnseｍblemeａｎ,spreadａndotｈersalloｗprobａbilitiestoｂeｅstimatedwhiｃhcａｎbｅusｅdfoｒａdvａnceｄplanniｎｇ.ＨｅｒｅｏutputoｆthｅKEＮDAｅnsemblemodelfromtｈｅGｅｒｍanMｅteoｒoｌogicalSeｒvice,ＤＷD,canbｅusｅdｉnfutｕretｏｐroｖidethispｒoｂabiｌityinｆorｍation．However，inordｅrtoｍitigatethｅiｍpａcｔoｆwiｎtｒywｅatherconｄitionsoｎａiｒportoperatioｎsmorｅｅｆficienｔly，tｈefocuｓｓhouｌdbelaidonsｈort-tｅrｍfoｒecasｔiｎg(ｔermed“noｗcasting”)ｔhesecｏnditionｓ．Thiscｏmprisｅsthｅｏnset，dｕrａｔionaｎdｔｙpeofｐrecipitationasrain，snｏｗ，frｅeｚiｎgraiｎ,orfｏg。DＬRisdevelopinganowcastingsｙstｅmthaｔprｏvidesｕserｓinaｖｉatiｏｎｗith0to2houｒforeｃａｓtsofｔheｓeｗinterweaｔhｅrcoｎdｉtｉｏns［8].Wearｇueｔhatasimｉlarsysｔemiｍbeddｅｄinｔheproceｓsofinformａtｉonsｈarinｇforｃｏllaboratiｖeｄｅｃｉsionｍakｉngｗoulｄａlsobebeｎeficialfｏroｐerａtionsonroａdneｔworks。2WINTＥRＷEATHＥROBJＥＣＴSAcertaｉｎwinterｗeａｔherphenｏmenon,likｅe.ｇ。freezｉngpｒｅｃipitaｔion,cａnbeｔhougｈtofaceｒｔａｉｎvｏｌｕｍeoｆairｗithinwhiｃｈｔhisphｅnomenoｎcanbeｏbsｅｒveｄ．Variousｏbservatｉoｎｓａresuitedfｏｒdescribingoneorｔheotheｒatｔributeofthａtpheｎomenｏｎ，ase。ｇ。ｔｈesurfaceteｍpeｒａture，theprecipiｔatｉonｔｙpe。Wiｔhnｏｄoubttｈｅacｔualweaｔhｅrpheｎomenoncanbｅdeterminedmorepreciseｌｙwheｎdataｆromvａrioussｅnsorsarecombiｎed[7]。Itｉstherｅforeadｖiｓaｂｌeｔotｈinkofsucｈvolｕmesasｗeatherobjectswithｃerｔaiｎiｎhｅreｎｔattrｉbｕtes．Foｒourｐurposes，awinteｒwｅａtherｏbjeｃｔ（ＷＷO）inaｃｅｒtainlimitedaｒea,ｅ．g.ａｎairｐortoraｄenseｍoｔorｗwｏｒk，canbedefｉnedthroｕghthefollｏwｉnｇpaｒameｔers：•aveｒticalcoluｍｎｏfaircoｎsistingofseverａllaｙｅrs•ｉssuｅｄtｉｍｅ•ｖalidtime•nｅxtｕpdatetime•layeｒｄescriｐｔion,e。g.：-Snｏｗ：ｕppｅranｄloweｒbounｄaryｗithiｎtensity：liｇht,modeｒａｔe,seｖere—Raiｎ：upｐerandlowｅrboｕndaryensｉｔｙ:ｌigｈt,mｏderａte，seｖｅrｅ-Freezingrain：ｕpｐeraｎｄlｏwerboｕndary-Fｒeezingdriｚzle：upｐｅｒａndlｏwerｂoｕndarｙ•surfaｃecoｎditions•trenｄs，e．ｇ。inteｎｓｉｔｙｉncｒeasｉng,cｈangetｏmｅlting,etｃ.Ｆiｇｕｒe1ｓketchｅshowweathｅｒｐarａmeｔersfromvaｒｉouｓｓourｃesａrecomｂｉｎｅdｂydataｆｕsioｎtｏawinｔerwｅａtｈeｒｏbjecｔ，ＷWO（yelｌoｗｃylｉnder）,wｉthdiffeｒeｎtaｔｔｒiｂｕｔesindiffｅrentlayeｒs.ＳYＮＯPandaｕtomaticｓenｓｏrs(asfrｏmＳWIS）aｌｌoｗdeｔerminiｎｇsｕrfacｅcondiｔions,inｔhisexampｌerainwithｔｅmｐｅratureabｏvｅｚero。Thｅtｅｍperａture／humｉditysouｎdingｃanｂeprovidedfromanｕmericalweatherｆｏrecastmodeｌ,aircrafｔmeasｕreｄｄaｔa（AMDＡR）,orcoｎsｔructedfromｂoｔhdepｅnｄiｎgｏnｄatａａvａilabiｌity.Rａdarsoｂserveｔhepｒｅｃipiｔａtioｎheightaｎｄmayａlｓｏｂｅａｂｌetoｄeteｒmiｎethehydｒomeｔeorswiｔｈinｔheclｏud（polariｍｅｔricｃapabilｉtyａｎdrelateｄａlgorithｍs）.ADＷIＣＥ–ｔheＡdvancｅdDiagｎosｉsandWａｒnｉngSｙstｅmfｏrＩciｎgＥnviｒonmｅnts–［6,4]useｓthｅiｎｆorｍａｔionofrｅportedｗeatｈeratthｅgroundtｏｇeｔherwitｈthesouｎｄinｇsoftｅmｐeratureaｎdhumidｉtｙａndradａｒmeａsｕrementstｏdeterｍinｅthｅiｃinｇｔhreattoaiｒcrａfｔｉnｆlｉghｔ。ADＷICＥisnｏwfurtherｅxpandｅdtodiａgｎoｓeandｐｒediｃｔsnoｗａｎｄiciｎgconditｉonsattｈeｓｕrfaｃe，ｔoo，seefolｌowiｎgＳecｔｉｏn．Ｔａkeｎtｏgetｈer,thｅdeｒivｅｄａnａlyｓｉscanbecｏmpactｅdinｔotheWＷOwhiｃｈiｓｓhownschematicallyasayellowcyｌiｎderontheriｇhtｏfFiguｒe1．Itisobvioｕsthａtｔｈeobｊectｃanhaveseveｒaｌｄiffeｒenthazarｄｌayerｓintｈevｅｒtｉｃａl。ＦorｔheｇｉveｎcａsetherewoｕldbeaneａrｓｕrfacelaｙerwithtempeｒaｔｕresaｂovefrｅｅｚｉｎguｐｔoｈeightH１ｗhichｃoｎｔaiｎsraindｒops，aｓecondlayerｆromH１toH２wｈichｃoｎtａｉnｓｓuｐer-cooleddroｐlｅtｓｗithcorrｅspoｎdiｎgiciｎgｔhreat，anｄapreciｐｉｔａｔiｎgcｌoｕdｌayerｏntｏp。Foｒnowcastｉｎgicing＆ｓｎoｗｃonditioｎsａｔthｅｓurfaceoneｈａsｔoｃonsidｅrweaｔｈeｒcｈａngeｓdueｔｏadveｃｔｉoｎoｆaｉrwithｄiffeｒｅnｔchaｒaｃterｉｓticsａnｄ,espｅciallydeｍａndｉｎg,pｏssibｌeｃhａngesrｅｓultinｇfrompreｃipitaｔionaｎdｃｌoudpｈysicｓproｃesseswｈicｈcaｎｏccurwｉthiｎｓｈｏｒttｉmesｐansaｔｔｈｅoｂservａtionｓite．ForcaｐtｕriｎgｂothoftｈｅseefｆｅctsａnapproachiｓfoｌlｏwedwheｒeＷWOｓａredeｔeｒmiｎeｄatthevariouｓobservatｉonｓｉteｓaroｕndａｎaiｒpｏｒtｏralonｇmotoｒwayｓｗheredａｔａfｒomSYNOP,radaｒandSＷＩSstationsareavａilabｌｅ.CｈaｎgｅｓｉnWWOsａｒoundthelocatioｎｃａnthenｐroviｄｅguidancefortｈeexpｅｃteｄｃhaｎｇeａtthｅloｃation。３ＤＩAＧNOSＩSANＤPＲOＧNOSISＷIＴＨADＷIＣEＡDWＩＣEstandsfｏrＡdvaｎcedDｉagnosisaｎdWarnｉｎgsystｅmｆoｒairｃraftIｃingEnｖiroｎmenｔs。BａseｄoｎtheexpeｒtsystemIIDA（InteｇraｔedIcingDiagnosticＡｌgoｒithm)ｂyNCAＲ/RAＰ［４］,tｏｄeｔectaｎｄpreｄｉｃtｃloudｓandpｒecipitatｉｏnｗiｔhｓｕpeｒcｏoledliquiｄwateｒ,ithasbeｅndesignedａｔDLRinObｅrpｆaｆｆenhofenin2０03［6］andfurtheｒdevelｏｐeｄbytheGermanMetｅｏrologｉｃａｌSｅrvice（DＷD)[３].Itｓｐurｐｏseistheｄeteｃtｉonaｎdｆorecaｓtｏfaｒｅaswithｓupercooledlarｇeｄｒopleｔs(SLＤ)andpossｉbｌeｔhreｅdｉmensｉｏnalicinｇａrｅａs,ｒｅｓpectｉveｌy，whｉchposeａsｉｇｎｉficantthreattｏａｉｒｃraft.ADWICEprｏvｉdeｓａdiagnosticprｏｃｅduｒeｔｏaｎａlｙsethecuｒrｅntiｃingsituatｉonoｆtheiｎvestiｇａｔeｄpａrｔofthｅatｍosphere.Figure２scｈeｍaｔicalｌyilluｓｔratesｔｈｅpｒocｅｅｄiｎgoｆtｈeｄiagｎosticｐａｒｔｏfADＷIＣE.Themostimportａnｔｉnformaｔioｎｉsgｒｏundmeａｓurementｓｒｅceivedfromｏbserｖationｓｉtes.ThecurrenｔvｅrsｉoｎｏfAＤWICEｕｓesobservatioｎsofthｅｐreｓentwｅatｈｅraswellaｓthecｌoｕｄamｏuntanｄanestimationｏfｔｈｅclｏｕdbaｓｅheｉght。Iｎｃoｍｂinａｔｉonｗｉｔhｒａdarreflｅｃtiviｔyｍeaｓuremenｔs，theｓｅdaｔaareｕｓeｄtogetａfirstgueｓsinfｏrmationfｏｒacertaiｎｉｃingｓcｅnａｒｉoabovetheoｂseｒｖaｔiｏnsiｔeｓ。Atstatｉｏｎｓwheretｈeｓeｄatａｉndicaｔeaｎiciｎgweａtｈersｉtｕation，ｔｈeAＤWICＥalgorｉthmｉｓusedtoｓｅarchfｏｒthevｅrｔicalextenｄｏｆｔhｅpossibledanｇerzonｅ。Oｎtheｂasisofobservedormodｅｌfoｒecaｓｆilｅsｏfhumiｄityａｎｄtemｐｅｒatｕrｅasｗellaｓsｏmｅdeｒivedconｖectionparametｅrs,liｋeclｏudbaseｈeｉｇhtａnｄclouｄthicknesｓｏrｓpecificcloｕdｗaｔeｒandcloudiｃｅcontｅnt，thｅiｃingalgorithmｉｓdesiｇnedtodｅtectpossｉbleicingarｅas.Ｆourｄｉfｆerｅｎtiｃiｎgｓｃenａｒiosaｒeclａssifｉed，wｈiｃhresｔuponｄiｆfｅｒｅｎttypesofｆｏｒｍaｔｉonproｃeｓses。Forｅxamplｅ,tｈeicingｓceｎarioｆrｅeｚinｇdescｒibeｓtheｔypｉｃalformatiｏnproｃeｓsofsupeｒｃooｌedｒain.Itiｓｍａiｎlycharactｅriｚedbyaｗａrｍatmｏsｐhｅｒiｃｌayer(temperatuｒesａbｏve0°Celsuiｓ）embedｄedｉncolｄｅr(T＜0°C）layersｏrabovetｈecolｄgrｏunｄ.Soｌiｄｐrｅｃｉpｉｔaｔiｏｎeｘpeｒiｅncesaphasetransｆorｍationfｒomsｏlｉdtoｌiquｉｄiｎtｈewarｍlayeraｎdgeｔsｓuｐerｃoolediｎcoldlayeｒｂenｅａthｏroｎtｈegrｏｕnｄwithｏutｃhangingｔｈephaｓeａgaiｎ.IｎｔｈepｒognosｔiｃpartofＡDＷICEsolelｙtheoutｐutｏfanumｅriｃaｌｗeａtｈeｒpredictionmｏdｅｌisuｓedｔｏｆorｅcastｔhｒeｅdimensioｎalareaｓｗｉththepｏｓsiｂleoccurrenceoｆsuper—cooleddｒｏplets。ThecuｒrｅntｖersionｏｆADＷＩCE,ｗhicｈisｏperationalｌyuseｄbyDWD，ｉsopeｒａtingwｉththeｏutｐutofｔhｅCOSＭO—EUｍodel．Twiceadayｈｏｕrlyicingｐreｄictionｓupｔｏ21foreｃasthourｓaｒecrｅａtｅd。Ｆiｇure3illustratesｔheoｐeｒａtiｏｎａｌproｃessｉng。Tｈｅproｇnosｔicａlgoriｔhmｉsstaｒｔｅdａt03UＴＣoｎthebaｓisｏfthｅ00UTCmodｅlruｎandat1５UTConｔheｂasisoｆｔhe１２UTCmｏdelrun。WeaｒeabouttomodｉfｙAＤWICEbyuｓｉｎｇtｈelocaｌarea，highresolutｉonnumericalwｅaｔherprediｃｔiｏnｍodelCＯSＭO—DE。ItcoｖerｓthｅaｒeａsofGeｒmany,Ｓwitzerland，Aｕstriaandpartsoftheiｒnｅighboｕringcｏuntrｉｅsａndhａsａhｏｒｉｚｏntalｒｅsolutｉｏnof2．8ｋm.InｃontraｓtｔoｔheregiｏnaｌmodelCOＳMO－ＥＵ,COSMO－DEｉsabletoexｐliｃitlysiｍulate(ｌarｇe）convectivｅpｒocesseｓ．AmajｏrcｈanｇｅintｈｅproｇnostｉcpartwiｌｌbeｔheusｅofsｏmediｒeｃtｌyderｉvedcｏnvectionparaｍeｔerｓofthｅＣOMＳO-DEmodel．Alsothedｉａgnoｓticiciｎgpｒoｄuctwｉllｂeeｎｈａncedｆurtherｔhroughthecｏmbiｎatｉonｗithａdｄitioｎallｏcaldata.Fｏreｘample,ｍeasuｒeｄｐrofilesfrｏmsｔartingandlaｎdingaiｒcraftｉnsteadｏffilｅswiｌlbｅappｌiｅdaｓｗelｌａspｏlarimetrｉcｒadardatafroｍPOLDIRADinstｅadofconventiｏnalraｄarｉnformaｔion(Euｒoｐeanraｄａrｃｏmpｏsｉte)。Ｗhereavailａblｅ，aｌsosurｆaceｄaｔａｆrom‘Sｔraｓsenwetｔerinformaｔioｎｓsystｅm–SWＩＳ'aloｎgmajorｈiｇｈwayｓwillgｉveｉnfｏｒmationoｎｆreezinｇｃonｄｉtioｎs.Tｈeobｓerｖatｉｏｎoｆｐrｅｓentwｅａther,cloudamoｕｎtanｄclouｄｂasｅhｅiｇhｔｗillｆurthermorebeusedafterthemｏｄｉficａtions.4ＮOWCＡＳＴOＦＰOTＥNＴＩＡLSNOWＦＡLLARＥASＴoｎowcａｓｔｐotentialarｅａsofsnｏwfａlｌiｎarｅgｉonｗｅutilize•Syｎoptic（largesｃale)mapsoｆ1000-50０hPaaｎd1000-8５0hＰａthicknessprovｉdinｇtherｅgiｏnoｆcoldａｉrｏbtａinedfromMEＴAＲ(sｔａnｄardｈourlyｏbseｒvaｔion)ｏrｎumericalweａｔherpｒeｄiction（NWP)ｍodelｓ•Sｕrｆａceｔeｍｐerａturｅbｅｌow/aｂoｖe０～2°Cor/andweｔ—bulbtｅmplessthan0°CｂaseｄｏnＮWPmodeloutputsandobservaｔion•Vｏlumeｔriｃradarreｆlｅctiｖityｏbservａtion•Snoｗｄeｐthmeaｓurｅment•Soundｉngsfｒｏｍraｄiosｏndeandａｉrcｒaftmeａsuremｅntsor／andnumeｒiｃalweathｅrpredｉctionmodeｌs.SIRWＥC20１2,Ｈelsinki，２3—2５May2０125ThｅPoteｎtialSｎowFaｌlArea（PＳA）algorｉthmiｓbａsedonreaｌ—timehｏurｌyoperaｔionaldaｔa，lｉkeregionalmoｄｅlsurｆacｅtemperａｔure，preｃpositeeｓｔimatedfｒomlow－levelradａrｓcans,aｎdsuｒｆacｅoｂｓｅrｖatｉｏns.Thisalｌowｓthatｔheoutput，ａwａｒｎinｇiｎｔeｒmsofPSA,canbｅｇeｎeraｔeｄiｎreal—timeａndatlow－cost.Ａlso,tｈｅｏｕtｐｕtcaｎbeusedinbuiｌｄiｎgmｏreｃoｍpｌicaｔeｄalgｏrｉｔhｍsoｆwｉntｅrweａtｈｅrwarｎingsbasedoｎvａrioｕsothersouｒceｓ（e。g．,theADWICEintrｏｄuｃedintｈeprｅviousseｃtioｎ）．4.1DatａsouｒｃesTｈeaｌｇoｒiｔhmisconstrucｔedwitｈdataavａilabｌeoｖeｒＣatａluñaｉｎcｌuding1）terrainheiｇhtfroｍDEＭ,2）temｐerａtuｒeｆrommodel,surｆacestatｉon,sounｄｉｎgs，anｄ3）radarｒeｆlectivｉｔy．TheｄeplｏymeｎtofｔheoｂserｖａtionaｌｓoｕrcesisshowniｎFｉgure4ovｅrlaiｄｏnorｏgraphｙ.Moredｅtailｏnｅachsourceisprovidedintheｆollｏｗingpoiｎtｓ。Fｉgｕre4。DaｔａavailａblearouｎdBarceｌｏｎaovｅｒｏrography：CｒossesiｎdiｃaｔethelocatｉonofCDV－RaｄarａndＡirｐoｒtBaｒcelonａ.Simｉlａrｌy,largｅdiａｍoｎdforAEＭETsurｆaceｓtatiｏｎｓ,smａlldiamｏｎｄforＳＭCｓurfａcｅstations,aｎｄtriaｎglｅsfｏrｓｏunｄinｇs.Digitａleｌevａtiｏnmodeｌ（DＥＭ)datausedherearefｒoｍASTERＧDEM（AdvaｎｃｅdSpace-ｂorneThermａlEｍisｓionandRｅflecｔionRadiomeｔerＧlobalDigitalＥlｅvationMｏｄel1)whicｈhａsahoｒizontａlｒesoluｔioｎoｆ1arc—seconｄ,bothinｌｏnｇitudｅandｉnlａtituｄe。Tｈiｓhigh—resolutｉonterraｉnheiｇhｔisre－ｍappedwithaｇriｄspacingｏf０．0１°inｔhｅhorizontalａsｓhownｉnFｉｇuｒｅ4andｕsednｏtonlyastｈebackｇrouｎdofthｅouｔｐutbutalｓofｏrtｈeadjustmenｔoｆatmoｓpherictemｐeｒatureiｎthevertical。SｕrfaｃeＳtatｉonｓ:Ｉnrｅal—time，surfａceｓtａtioｎｄata(e。ｇ．,tempｅｒaｔure[°Ｃ］,ｒelatiｖehumiｄity［％］,andpreciｐitation[mm／h］）aｔ2ｍheightwｅｒeavaｉlaｂleeverｙ10ｍinutesfｒoｍLaＡgｅｎｃiaEｓtaｔａlｄeMeteoroｌｏgíａ(ＡＥMＥＴ）overtheIbｅriａnPｅnｉｎsuｌaasｗellasｆromｔheaddiｔｉonalｄeｎseｒneｔworkｏfsuｒｆacemeasuremeｎtsoｆSｅrvｅiMeteorｏlògicdｅＣａtａluña（SMＣ）overCataｌuña。Ｈoｗｅｖｅｒ,ｆortｈeselectedcasｅ,thｅseweｒeprｏvidedinhｏurlｙｕpdatedvalｕes.Radarreflectivｉty：Rｅal-tｉmeａndｑｕalitycheｃｋｅdraｄarrefｌeｃtivityｆｉeldsａt０．5°ｅｌevａtionanglｅaｒegeneraｔedｗiｔhSMＣ’sCＤVoperａｔionalC-bａndｒadａｒ。Tｈealgｏrｉthｍtａkｅｓinｓｔａｎtaneoｕｓscａnsｃorresｐondingtｏtｈeｆorecasｔtiｍe（e．g。，1ｈｏｕrforｅcast)wiｔh0.01°gridspａｃｉng．Numericａlweathｅrｐｒｅdictionmodel：ThｅmｏｄeloｕtpuｔｆorthesurｆacｅｉsgeneratedoveｒtheIberianPeninｓuｌabymｅteoblueAG,aprｉvａtｅcompanyｔｈａｔｒunｓNMM（Ｎｏn—ｈｙdrosｔaｔicMesoscａｌeMoｄeｌ［２］）iｎ13-kmｓｐａtiaｌｒeｓolutｉontwiceａdａｙｗith1-hｏurfｏreｃastupto７2houｒs。4.2AnalｙｓiｓofｐoｔｅntｉalsnowarｅaTｈePSAiｓdｅtｅrｍiｎeｄmａinlybasedｏｎtemperaｔuｒe(obtaｉnedfrombothmodeloutｐｕtａndsurｆacestaｔiｏns）aｎdprｅｃｉpitａｔion(ｏbtaｉnｅdfrｏmｒadarreｆｌｅcｔｉviｔｙand/ｏｒmodeloutput)ａｔnear-grｏｕndlｅｖｅl．Thｅalｇoriｔｈmｉnterｐolateｓeaｃｈｉnputfieldoｎtｏｔheｃommｏｎgrｉｄwｉth0。０1°bｙ０．０1°resolutiｏnandｍoｄifｉestｈemodelｔemperatuｒefieldbyheighｔadjｕｓtmentsandtaｋｉoaｃｃｏunttheｍeasuredteｍpｅratures。Thespａtiaｌvａriability（orthｅstrｕcｔuｒe）oｆthｅtemperatureisobtaiｎｅｄｆromthemｏdeloutput.Figｕre5sｈｏｗsaprelｉｍinaryresｕltofapoｔentiaｌｓnｏｗfallaｒeadetermiｎedwiｔhｔhｅｔhｒesｈoldsoｆtｅmｐeraturｅ〈2°Ｃandｒadaｒrｅflectivitｙ>１5dBZshownasliｇhtyelloｗfilledaｒeａsｆor08Mａrch2010at14：0０ＵTC。Atthisanalｙｓisｔｉme,iｔsｎowedinｔhecitｙoｆBarceｌonａ，ｗhｉchisｎotｃaｐtureｄuｓiｎｇoｎlｙｔhemｏdelｔempｅｒａturｅ（Figure5a）.Ontｈｅoｔherhand,ａｆterthｅmodification(Ｆiｇuｒｅ5b），ｔheｓelecｔｅｄareabｅcomesmｏrereａlｉsｔic，ｓuggestingｔhａtｓuchｉntｅrｐolatｉoｎcａnbｅusefulwhenｅrｒoｎeｏｕsmodeloutpuｔsaｒeｕsedinthePSA.Cｒosｓvalidatioｎofｔheｉnteｒpolationteｃhniｑｕｅwillbｅperformｅdoverloｎｇer-tｅｒｍperiodintｈefｕtｕre.Fｉgure5:ＰoteｎtialSnowＡreas（ｉnyeｌloｗｉsｈfilled-conｔour)at14：0０UTC０8Maｒ2010：a）ｍｏdｅltemｐeｒａｔureoｎly，b）aｆtermodiｆicatioｎ．Ｇreybａckgｒｏundiｓｏrｏｇraphy,dｉaｍonｄsarｅｓｕrｆacestations，aｎdtheｉrｃolorscoｒｒeｓpondtｏtempｅrａture[°C]shownｉnｔｈｅcolorBar。Bluiｓhｆiｌｌedcontourｉｎdicａteｓreflectivｉｔylａrgｅrthan１5dBZ。4。3FoｒeｃaｓｔofpoｔeｎtiaｌsnowａreaTheforecasｔupdateｆｒequencｙｄｅpendsontｈｅｕpdatetimeｏfｏbservationａldata，ａｎdtｈeforeｃastｌead—ｔimeｄependsoｎthoｓeｏfthemodeｌanｄtheｒａdarprecipiｔaｔionｎｏｗcａsｔiｎｇ．Fｏurfoｒecａstsｔratｅｇieｓarepropoｓｅd：A.Ｍｏdｅl:Itisiｎiｔialｉzedａt00anｄ12ＵTCａｎｄａctualizatｉｏｎisｂetwｅen４anｄ6hourafterｉnitiaｌｉzatｉｏn.Inotherｗｏrｄs，forverifｉcaｔioｎtimeat00ＵＴC，amｏdelrｕｎischｏsenａtlｅａd—tｉmｅ12hourｏftheruｎsinｉｔiａliｚedｐreｖiouslyａt—12UTＣ.Ontｈｅotherhandforverifiｃationtimeａｔ０6ＵTC，ｔhemｏdelrｕnｉsｃhoｓenfromｔｈeruｎsiniｔiａlizedat0０UTＣ．B。Modelｔendｅncy（Mteｎdeｎcy)：Tendencyisrｅｆerrｅdtoａsthｅforwaｒdｃhaｎgesofｍｏdeltｅｍperａtureｉntｉme(°C/hｏur）.Aｌthoughthemｏdｅlvaluｅｓmaｙbewrong,thｅirｃhaｎｇesｉntiｍestillrｅｐrｅｓenｔapaｒｔofreａlity。Ｈｅnce，extrapolaｔionｏfａcorrecteｄiｎｉｔiａlconｄiｔｉoncａｎbeｐerfｏrｍedusingthecｏmｐutedteｎｄeｎｃyｆrｏmｉnｉｔialｔｉmeｔoeａchｌｅad—time。Ｈｅre,ｔhesｔatiｏnｔempeｒaｔurｅｉsｕｓｅdasthｅｉｎitｉalｖaluｅ（ｆｏrecａstｌｅａdtｉｍezero；FLT0herｅafter）.C。Conｄitｉonａｌｍerging（ＣM)：Ｔhissｔrateｇyaｓｓumesthａｔthｅinitｉaｌobｓervａtｉonpersistsintheｆutureandｏnlytheforecastedｓｐatｉaｌvariabｉlityofthetemperatｕrefｉeldisused。Thｉｓfｒｏｚｅnrealitｙaｓsumｐtｉonmayworkforｓhoｒtleａｄtimｅs（2to3hours）ｂecauseｉtrｅfleｃtstｈeｒealitybetｔertｈanthemodeｌｉniｔialｉzed12～6hoursｅarｌｉer.Ofcouｒse，quiｃkｃhａngesasinｆronｔalpaｓsageswｏulｄreducetheuseablelｅad—time.D．Relaｘatｉoｎ：Aweigｈtingfｕｎctioｎisapplieｄ［1],wheretheobservatioｎｄaｔahａveahiｇherweｉghtthanthemodｅldataforsｈorｔｆorecastｌｅaｄtｉmesanｄｖｉceｖersafｏｒloｎgｅrleadtiｍｅｓ.Fｉgｕrｅ6shｏwsanexaｍｐlｅｏfatemperaturｅ-fｏrｅcaｓｔｖｅrificａｔioｎintermsｏfｍeanａbsoluteerrorfｏrtｈediffeｒenｔmｏdｅlｓｔrateｇies．Foｒthispartiｃularｃase，thecｏndｉｔiｏnalmerｇinｇsｔｒateｇｙCseemstoｂemoreacｃｕｒatetｈａnuｓingｍodeｌｏnlysｔrategyＡoｒmｏｄelｔendenｃysｔｒaｔegyB.Uptonｏｗ，ｔｈeａｌgoｒithｍhａsbeｅnteｓtedwithoneeveｎｔonｌy。Aｌong－termvｅrｉｆicatiｏｎofteｍpｅraturｅ,ｐｒecipitatioｎandｐotentialｓnｏwfalｌareａnowcastswiｌlbepeｒformeｄinthefutｕre．5CONCLＵSＩONSANDＯUＴLOOKAlgorithmｓtｏｄiaｇｎoseａndnowcastｓｎowfａｌlaｎdicｉｎgｃondｉｔiｏnsinｌimitｅdａrｅasｈavebeenｄｅscribed.Prｏvｉｄiｎｇｅnｄ—uｓersadｅqｕatｅaｎｄeasytouｎdｅrsｔanｄgrouｎdleveｌwarniｎgsforwinteｒprｅcｉpitatiｏnatalocalｐositｉonｏrareaisｎotaneasyｔask.Bｅsidesｔｈｅproｂｌemｏfunderｓtaｎdiｎgandmodelliｎgｃomplexphysicalｐroｃessesｌiｋeｉcｉng，snｏwfｏｒmatｉonandprｅｃipitatiｏntｏtheｇrounｄ,alsoｔhecombinatioｎofdａｔafｒoｍdｉｆｆerenｔsourｃeｓsuchasrａｄar，satellite,aｎdｓurfacestationｓｗithmodｅｌoutputsiｓａbigcｈａlｌeｎgｅ。Notonlyaremodelｆorecasｔsoftenｉnａｃcurate，buｔalsoｏbsｅｒvatiｏｎｓcaｎｂedｉｆfiｃulttｏhａndlebecaｕｓeoｆｒegionａllyｄiffｅreｎｔdatａavaｉlabｉｌｉtｙ,dataqｕａlｉtｙａndｏbsｅｒｖaｔiｏnrepresentaｔiveｎｅssduetothediffｅｒenｔtemporalａndｓpatｉaｌreｓoｌuｔiｏnsａｎdstationｈeights。Ｉｔiｓexpecｔedｔｈatｔhｅexpeｒｉeｎcegａiｎｅｄfrommａnｙwｉntｅrｗｅathｅrｃaseswillenabletheｂuild-upoｆafuｚzyｌｏｇicprocedurewhichcａnｉmprovｅｔｈenoｗcaｓｔinｇofｗiｎtｅrweatｈerandtｈuｓｐｒovideaｒeliablｅsoｕｒｃｅｏｆｉnfｏrmationfordecｉsionmａkeｒsｉnaviaｔｉonaswelｌaｓgrｏundtrａnｓpoｒtatｉoｎsectors．6ＲＥFERENCES［1］Ｈaｉｄen，T。，A.Kaｎn，G．Piｓtｏtnik，Ｋ。Ｓtａdlbachｅr，andＣ.Wittmａnｎ，20０9：IｎteｇraｔｅdNoｗcastｉngｔhrｏuｇhＣomprｅhensivｅAnalyｓｉs(INCA)-Systｅmdeｓcｒipｔｉon。ZAMＧRep。,ＺentralansｔaltｆürMeteorｏｌogｉeunｄGeoｄｙnaｍik,Vienna，Austria，６0ｐp.[Availaｂleonｌｉnｅａｔhｔtp：//www．zamg。ac.ａt/fｉx/IＮCA_sｙsｔem.pdf。][2]Janjic，Z。I.anｄJ.P.Gerｒtty,2001:Analｔeｒｎativeapproaｃｈtｏnoｎhｙdrｏstａtiｃmodelｌｉng。Mｏｎ.Ｗea.Rev。,1２9，116４-1１78．[3]Lｅifｅlｄ，C．，2０04：ＷeiteｒｅntwｉｃklungｄｅsNｏwｃastｉngsysｔeｍsＡＤWICEｚuｒErｋｅnnｕｎgvｅｒeisungsgｅfährdetｅrLuｆtｒäｕme．BerichteｄeｓＤt．Weｔterdiｅnｓtes，224［４]McDoｎｏugh，F．,B．Beｒnstｅiｎ，1999：Combiｎinｇsatellｉte,radａrａｎdsurｆａcｅoｂseｒｖatioｎswiｔhmoｄｅldａtaｔｏｃreａteａbetteraircraｆｔicinｇｄiaｇnｏsｉｓ.8thCｏｎｆeｒｅｎceｏｎAviaｔｉon，RａｎgｅａｎｄAerospacｅMeteorologｙ，10–15Ｊaｎ。19９9,ＡMＳ，Daｌlａs，Ｔexas,46７–4７１［5]Ｓｃhｒaｆf，C。,H．Reiｃｈ,A．Rhoｄin，R.Ｐottｈasｔ，U.Blａhak，Ｋ．Sｔepｈaｎ,Ｙ．Zeng，D．Epperｌeｉn,D。Ｌｅuｅnberger，T.Wｅusｔhoff，M．Tsyrulｎｉkov,V.Ｇorin,A。Ｉriｚa,M．Lazanowicz,Ｌ，Torrisｉ:20１1COＳMOＰｒｉoｒityProjｅctＫEＮDAｆorKｍ-SｃａｌｅEｎsemｂｌｅ—BasｅdDatａAｓsｉｍilａtion，９thSＲＮWPWoｒkshoｐonNoｎhydrostaticModｅllｉｎg，BａdOrb，16–18Ｍay2０11［6］Ｔaffernｅr，A.，T。Hａｕf，C．Leifelｄ,T.Ｈaｆneｒ，H.Lｅykauf，U.Voｉgt,2003：AＤＷＩＣＥ–AdvancedDiagnosiｓanｄWaｒniｎgSystemfｏrAirｃrａｆtIcｉnｇEnviｒoｎmentｓ.Weａ.Forecaｓtiｎg,18，1８4–2０3［７］Ｔafferｎer，A.,Hａgen，M。，Kｅｉｌ,Ｃ。，Zｉnner，T。aｎdVolkｅrt，Ｈ.，2０0８，DｅveｌopmenｔanｄpropａｇationofsｅveｒeｔｈundｅrsｔormsｉntｈeuppｅrDanubecaｔcｈmeｎｔareａ:Towaｒdsａnintｅｇrateｄnoｗcaｓｔiｎgandｆｏrｅcastingsysｔeｍｕsiｎgｒeａl—timeｄａtａａndhigh—resoｌｕｔiｏnsiｍｕｌａtiｏｎs,ＭeteｏｒolｏgyandAtmｏsｐheriｃPhyｓics，101，211—22７,ＤOI１0.1００7／s007０3－00８—０32２-7［８]TａfｆeｒｎｅrA.,KｅisF．2０12：NowcａstingwinteｒweatｈeraｔＭｕniｃhａirpoｒt．In：TheDLＲPｒoｊectWｅｔter＆Fliegeｎ，eｄs。Ｔ。Ｇeｒz＆C。Scｈwarｚ，FinalＲeｓeａｒcｈRepoｒｔDＬＲ－FＢ2012－0２,46-5７.

附录（翻译)论准时定制的冬季天气信息系统与城市交通道路管理摘要最近，在技术上，确定冬季开始、持续时间、降水量、积雪和结冰条件等研究有了新的发展报告和进程，而这个技术，还仍然处于开发阶段，将来能在短短的3０分钟到几日小时内用来预测在短期至中期的天气情况。（即实时天气报告）这种技术旨在从一个数值模型以及在高空间分辨率地面气象站，检测地区潜在的降雪反射率数据相结合的降水和地表温度数据.另一方面用此技术结合分析测量结果从而预测天气预报.(如：衡量飞机数据和偏振雷达数据)关键词:类型和降水量实时预测预测天气一.介绍不好的天气会导致交通堵塞，交通事故和交通延误。道路交通尤其被不利的天气如雪、冰、雾、雨、大风所耽误和影响.交通的发展使得交通更加容易受到不利天气条件影响。如今，当恶劣天气时的反应已经发生或即将发生时，与交通运输有关的人和参与运输者(包括空运的或地面）大部分的时间才有受到影响.而未来的道路管理系统应该主动预见破坏性天气元素和大大提前预报的尺度，以避免或减轻对交通流的影响。然而“天气”本身不是一个可以简单轻松地解决的技术问题。天气的预报是一个困难和复杂的任务，某些程度上会受到相当大的限制.因此非常有必要尽快观察和预测大气的变化状况,提高天气预报的准确程度。此外,措施的要求是能预测到“天气"及其影响和减少这些影响对交通流及其管理的管理。交通参与者和交通管理中心在合适的时间