版权说明:本文档由用户提供并上传,收益归属内容提供方,若内容存在侵权,请进行举报或认领
文档简介
Section12
AirQualityForecastingTools1工作报告BackgroundForecastingtoolsprovideinformationtohelpguidetheforecastingprocess.Forecastersuseavarietyofdataproducts,information,tools,andexperiencetopredictairquality.Forecastingtoolsarebuiltuponanunderstandingoftheprocessesthatcontrolairquality.Forecastingtools:SubjectiveObjectiveMoreforecastingtools=betterresults.2工作报告BackgroundPersistenceClimatologyCriteriaStatisticalClassificationand
RegressionTree(CART)RegressionNeuralnetworksNumericalmodelingPhenomenologicaland
experiencePredictorvariablesFewerresources,loweraccuracy
Moreresources,potentialforhigheraccuracy3工作报告SelectingPredictorVariables(2of3)Selectobservedandforecastedvariables.Predictorvariablescanconsistofobservedvariables(e.g.,yesterday’sozoneorPM2.5concentration)andforecastedvariables(e.g.,
tomorrow’smaximumtemperature).Makesurethatpredictorvariablesareeasilyobtainablefromreliablesource(s)andcanbeforecast.Consideruncertaintyinmeasurements,particularlymeasurementsofPM.5工作报告SelectingPredictorVariables(3of3)Beginwithasmanyas50to100predictorvariables.Usestatisticalanalysistechniquestoidentifythemostimportantvariables.Clusteranalysisisusedtopartitiondataintosimilaranddissimilarsubsets.Unique(i.e.,dissimilar)variablesshouldbeusedtoavoidredundancy.Correlationanalysisisusedtoevaluatetherelationshipbetweenthepredictand(i.e.,
pollutantlevels)andvariouspredictorvariables.Step-wiseregressionisanautomaticprocedurethatallowsthestatisticalsoftware(SAS,
Statgraphics,Systat,etc.)toselectthemostimportantvariablesandgeneratethebestregressionequation.Humanselection
isanothermeansofselectingthemostimportantpredictorvariables.6工作报告CommonOzonePredictorVariablesVariableUsefulnessConditionforHighOzoneMaximumtemperatureHighlycorrelatedwithozoneandozoneformationHighMorningwindspeedAssociatedwithdispersionanddilutionofozoneprecursorpollutantsLowAfternoonwindspeedAssociatedwithtransportofozone-CloudcoverControlssolarradiation,whichinfluencesphotochemistryFewRelativehumiditySurrogateforcloudcoverLow500-mbheightIndicatorofthesynoptic-scaleweatherpatternHigh850-mbtemperatureSurrogateforverticalmixingHighPressuregradientsCauseswinds/ventilationLowLengthofdayAmountofsolarradiationLongerDayofweekEmissionsdifferences-MorningNOxconcentrationOzoneprecursorlevelsHighPreviousday’speakozoneconcentrationPersistence,carry-overHighAloftwindspeedanddirectionTransportfromupwindregion-7工作报告AssemblingadatasetDeterminewhatdatatouseWhatdatatypesareneededandavailableWhatsitesarerepresentativeWhatairqualitymonitoringnetwork(s)touse(forexample,continuousversuspassiveorfilter)Whattypeofmeteorologicaldataareavailable(surface,upper-air,satellite,etc.)Howmuchdataisavailable(years)9工作报告AssemblingadatasetAcquirehistoricaldataincludingHourlypollutantdataDailymaximumpollutantmetrics,suchasPeak1-hrozonePeak8-hraverageozone24-hraveragePM2.5orPM10HourlymeteorologicaldataRadiosondedataModeldataMeteorologicaloutputsMM5/TAPMOtherSurfaceandupper-airweatherchartsHYSPLITtrajectories10工作报告AssemblingadatasetQualitycontroldataCheckforoutliersLookattheminimumandmaximumvaluesforeachfield;aretheyreasonable?Checkrateofchangebetweenrecordsateachextreme.TimestampsHasalldatabeenproperlymatchedbytime?TimeseriesplotscanhelpidentifyproblemsshiftingfromUTCtoLST.MissingdataIsthesameidentifierusedforeachfield?I.e.,–999.UnitsAreunitsconsistentamongdifferentdatasets?I.e.,m/sorknotsforwindspeeds.ValidationcodesArevalidationcodesconsistentamongdifferentdatasets?Dothevalidationcodesmatchthedatavalues?I.e.,aredatavalues
of–999flaggedasmissing?11工作报告ForecastingToolsandMethods
(2of2)ForeachtoolWhatisit?Howdoesitwork?ExampleHowtodevelopit?StrengthsLimitationsOzone=WS*10.2+…13工作报告Persistence(1of2)Persistencemeanstocontinuesteadilyinsomestate.Tomorrow’spollutantconcentrationwillbethesameasToday’s.Bestusedasastartingpointandtohelpguideotherforecastingmethods.Itshouldnotbeusedastheonlyforecastingmethod.Modifyingapersistenceforecastwithforecastingexperiencecanhelpimproveforecastaccuracy.MondayTuesdayWednesdayUnhealthyUnhealthyUnhealthyPersistenceforecast14工作报告Persistence(2of2)Sevenhighozonedays(red)Fiveofthesedaysoccurredafterahighday(*)Probabilityofhighozoneoccurringonthedayafterahighozonedayis5outof7daysProbabilityofalowozonedayoccurringafteralowozonedayare20outof22daysPersistencemethodwouldbeaccurate25outof29days,or86%ofthetime
DayOzone(ppb)DayOzone(ppb)18016120*25017110*350188047019805802070610021607110*2250890*23509802470108025801180268012702770138028801490296015110*3070Peak8-hrozoneconcentrationsforasamplecity15工作报告Persistence–LimitationsPersistenceforecastingcannotPredictthestartandendofapollutionepisodeWorkwellunderchangingweatherconditionswhenaccurateairqualitypredictionscanbemostcritical17工作报告ClimatologyClimatologyisthestudyofaverageandextremeweatherorairqualityconditionsatagivenlocation.Climatologycanhelpforecastersboundandguidetheirairqualitypredictions.18工作报告Climatology–ExampleAveragenumberofdayspermonthwithozoneineachAQIcategoryforSacramento,California19工作报告DevelopingClimatologyConsideremissionschangesForexample,fuelreformulationItmaybeusefultodividetheclimatetablesorchartsinto“before”and“after”periodsformajoremissionschanges.McCarthyetal.,200521工作报告ClimatologyHighpollutiondaysoccurmostoftenonWednesdayHighpollutiondaysoccurleastoftenatthebeginningoftheweekDayofweekdistributionofhighPM2.5days22工作报告ClimatologyDistributionofhighozonedaysbyupper-airweatherpattern23工作报告Climatology–LimitationsClimatologyIsnotastand-aloneforecastingmethodbutatooltocomplementotherforecastmethodsDoesnotaccountforabruptchangesinemissionspatternssuchasthoseassociatedwiththeuseofreformulatedfuel,alargechangeinpopulation,forestfires,etc.Requiresenoughdata(years)toestablishrealistictrends25工作报告CriteriaUsesthresholdvalues(criteria)ofmeteorologicalorairqualityvariablestoforecastpollutantconcentrationsForexample,iftemperature>27°Cand
wind<2m/sthenozonewillbeintheUnhealthyAQIcategorySometimescalled“rulesofthumb”CommonlyusedinmanyforecastingprogramsasaprimaryforecastingmethodorcombinedwithothermethodsBestsuitedtohelpforecasthighpollutionorlowpollutionevents,orpollutioninaparticularairqualityindexcategoryrangeratherthananexactconcentration26工作报告DevelopingCriteria(2of2)
Determinethethresholdvalueforeachparameterthatdistinguisheshighandlowpollutantconcentrations.Forexample,createscatterplotsofpollutantvs.weatherparameters.Useanindependentdataset(i.e.,adatasetnotusedfordevelopment)toevaluatetheselectedcriteria.29工作报告Criteria–StrengthsEasytooperateandmodifyAnobjectivemethodthatalleviatespotentialhumanbiasesComplementsotherforecastingmethods30工作报告Criteria–LimitationsSelectionofthevariablesandtheirassociatedthresholdsissubjective.Itisnotwellsuitedforpredictingexactpollutantconcentrations.31工作报告Classificationand
RegressionTree(CART)CARTisastatisticalproceduredesignedtoclassifydataintodissimilargroups.Similartocriteriamethod;however,itisobjectivelydeveloped.CARTenablesaforecastertodevelopadecisiontreetopredictpollutantconcentrationsbasedonpredictorvariables(usuallyweather)thatarewellcorrelatedwithpollutantconcentrations.AnexampleCarttreeformaximumozonepredictionforthegreaterAthensarea.32工作报告Classificationand
RegressionTree(CART)Ozone(Low–High)
ModeratetoHighModeratetoLowTemplowTemphighWS-strongModerateModerateHighLowWS-calmWS-calmWS-light33工作报告CART–HowItWorks
(1of2)ThestatisticalsoftwaredeterminesthepredictorvariablesandthethresholdcutoffvaluesbyReadingalargedatasetwithmanypossiblepredictorvariablesIdentifyingthevariableswiththehighestcorrelationwiththepollutantContinuingtheprocessofsplittingthedatasetandgrowingthetreeuntilthedataineachgrouparesufficientlyuniform34工作报告CART–HowItWorks(2of2)ToforecastpollutantconcentrationsusingCARTsStepthroughthetreestartingatthefirstsplitanddeterminewhichofthetwogroupsthedatapointbelongsin,basedonthecut-pointforthatvariable;Continuethroughthetreeinthismanneruntilanendnodeisreached.Themeanconcentrationshownintheendnodeistheforecastedconcentration.Note,slightdifferencesinthevaluesofpredictedvariablescanproducesignificantchangesinpredictedpollutantlevelswhenthevalueisnearthethreshold.35工作报告
Variables:T850-12Z850MBtempDELTAP-thepressuredifferencebetweenthebaseand
topoftheinversionMI0-Synopticweatherpotential(scalefrom1-lowto5-high).FAVGTMP-24-houraveragetemperatureatLaPazFAVGRH-24-houraveragerelativehumidityatLaPaz.CARTclassificationPM10inSantiago,Chile
NodexVariableandcriteriaSTD=StandarddeviationAvg=AveragePM10(ug/m3)N=numberofcasesin nodeCassmassi,1999Istheforecastedtemperatureat850mb10.5°C?YesNoCART–Example36工作报告DevelopingCARTDeterminetheimportantprocessesthatinfluencepollution.Selectvariablesthatproperlyrepresenttheimportantprocesses.Createamulti-yeardatasetoftheselectedvariables.ChooserecentyearsthatarerepresentativeofthecurrentemissionprofileReserveasubsetofthedataforindependentevaluation,butensureitrepresentsallconditionsBesurevariablesareforecastedUsestatisticalsoftwaretocreateadecisiontree.Evaluatethedecisiontreeusingtheindependentdataset.37工作报告CART–StrengthsRequireslittleexpertisetooperateonadailybasis;runsquickly.Complementsothersubjectiveforecastingmethods.Allowsdifferentiationbetweendayswithsimilarpollutantconcentrationsifthepollutantconcentrationsarearesultofdifferentprocesses.SincePMcanformthroughmultiplepathways,thisadvantageofCARTcanbeparticularlyimportanttoPMforecasting.38工作报告CART–LimitationsRequiresamodestamountofexpertiseandefforttodevelop.Slightchangesinpredictedvariablesmayproducelargechangesinthepredictedconcentrations.CARTmaynotpredictpollutantconcentrationsduringperiodsofunusualemissionspatternsduetoholidaysorotherevents.CARTcriteriaandstatisticalapproachesmayrequireperiodicupdatesasemissionsourcesandlandusechanges.39工作报告RegressionEquations–
HowTheyWork(1of5)RegressionequationsaredevelopedtodescribetherelationshipbetweenpollutantconcentrationandotherpredictorvariablesForlinearregression,thecommonformis
y=mx+bAtright,maximumtemperature(Tmax)isagoodpredictorforpeakozone[O3]=1.92*Tmax–86.8r=0.77r2=0.5940工作报告Morepredictorscanbeadded(“stepwiseregression”)sothattheequationlookslikethis: y=m1x1+m2x2+m3x3+……mnxn+bEachpredictor(xn)hasitsown“weight”(mn)andthecombinationmayleadtobetterforecastaccuracy.Themixofpredictorsvariesfromplacetoplace.RegressionEquations–
HowTheyWork(2of5)41工作报告OzoneRegressionEquationforColumbus,Ohio8hrO3=exp(2.421+0.024*Tmax+0.003*Trange-0.006*WS1to6+0.007*00ZV925-0.004*RHSfc00-0.002*00ZWS500)VariableDescriptionTmaxMaximumtemperatureinºFTrangeDailytemperaturerangeWS1to6Averagewindspeedfrom1p.m.to
6p.m.inknots00ZV925Vcomponentofthe925-mbwindat00ZRHSfc00Relativehumidityatthesurfaceat00Z00ZWS500Windspeedat500mbat00ZRegressionEquations–
HowTheyWork(3of5)42工作报告Thevariouspredictorsarenotequallyweighted,somearemoreimportantthanothers.Itisessentialtoidentifythestrongestpredictorsandworkhardestongettingthosepredictionsright.Tmaxvs.O3PreviousdayO3vs.O3WindSpeedvs.O3RegressionEquations–
HowTheyWork(4of5)43工作报告Inanexamplecase,mostofthevarianceinO3isexplainedbyTmax(60%),withtheadditionalpredictorsadding~15%.Overall,75%ofthevarianceinobservedO3
isexplainedbytheforecastmodel.Ourjobasforecastersistofillintheadditional25%usingothertools.AccumulatedexplainedvarianceRegressionEquations–
HowTheyWork(5of5)44工作报告DevelopingRegression(1of2)Determinetheimportantprocessesthatinfluencepollutant
concentrations.Selectvariablesthatrepresenttheimportantprocessesthatinfluencepollutantconcentrations.Createamulti-yeardatasetoftheselectedvariables.Chooserecentyearsthatarerepresentativeofthecurrentemissionprofile.Reserveasubsetofthedataforindependentevaluation,butensureitrepresentsallconditions.Besurevariablesareforecasted.Usestatisticalsoftwaretocalculatethecoefficientsandaconstantfortheregressionequation.Performanindependentevaluationoftheregressionmodel.45工作报告DevelopingRegression(2of2)Usingthenaturallogofpollutantconcentrationsasthepredictandmayimproveperformance.Donotto“overfit”themodelbyusingtoomanypredictionvariables.An“over-fit”modelwilldecreasetheforecastaccuracy.Areasonablenumberofvariablestouseis5to10.Uniquevariablesshouldbeusedtoavoidredundancyandco‑linearity.Stratifyingthedatasetmayimproveregressionperformance.SeasonsWeekendvs.weekday46工作报告Regression–StrengthsItiswelldocumentedandwidelyusedinavarietyofdisciplines.Softwareiswidelyavailable.Itisanobjectiveforecastingmethodthatreducespotentialbiasesarisingfromhumansubjectivity.Itcanproperlyweightrelationshipsthataredifficulttosubjectivelyquantify.Itcanbeusedincombinationwithotherforecastingmethods,oritcanbeusedastheprimarymethod.47工作报告Regression–LimitationsRegressionequationsrequireamodestamountofexpertiseandefforttodevelop.Regressionequationstendtopredictthemeanbetterthanthetails(i.e.,thehighestpollutantconcentrations)ofthedistribution.Theywilllikelyunderpredictthehighconcentrationsandoverpredictthelowconcentrations.Regressioncriteriaandstatisticalapproachesmayrequireperiodicupdatesasemissionsourcesandlandusechanges.Regressionequationsrequire3-5yearsofmeasurementdataintheregionofapplication,includingmanyinstancesofairpollutionevents,todevelop.48工作报告NeuralNetworksArtificialneuralnetworksarecomputeralgorithmsdesignedtosimulatethehumanbrainintermsofpatternrecognition.Artificialneuralnetworkscanbe“trained”toidentifypatternsincomplicatednon-lineardata.Becausepollutantformationprocessesarecomplex,neuralnetworksarewellsuitedforforecasting.However,neuralnetworksrequireabout50%moreefforttodevelopthanregressionequationsandprovideonlyamodestimprovementinforecastaccuracy(Comrie,1997).49工作报告NeuralNetworks–HowItWorksNeuralnetworksuseweightsandfunctionstoconvertinputvariablesintoaprediction.Aforecastersuppliestheneuralnetworkwithmeteorologicalandairqualitydata.Thesoftwarethenweightseachdatumandsumsthesevalueswithotherweighteddatumateachhiddennode.Thesoftwarethenmodifiesthenodedatabyanon-linearequation(transferfunction).Themodifieddataareweightedandsummedastheypasstotheoutputnode.Attheoutputnode,thesoftwaremodifiesthesummeddatausinganothertransferfunctionandthenoutputsaprediction.Comrie,199750工作报告DevelopingNeuralNetworksDeterminetheimportantprocessesthatinfluencepollutant
concentrations.Selectvariablesthatrepresenttheimportantprocesses.Createamulti-yeardatasetoftheselectedvariables.Chooserecentyearsthatarerepresentativeofthecurrentemissionprofile.Reserveasubsetofthedataforindependentevaluation,butensureitrepresentsallconditions.Besurevariablesareforecasted.Trainthedatausingneuralnetworksoftware.SeeGardnerandDorling(1998)fordetails.Testthetrainednetworkonatestdatasettoevaluatetheperformance.Iftheresultsaresatisfactory,thenetworkisreadytouseforforecasting.51工作报告NeuralNetworks–StrengthsCanweightrelationshipsthataredifficulttosubjectivelyquantifyAllowsfornon-linearrelationshipsbetweenvariablesPredictsextremevaluesmoreeffectivelythanregressionequations,providedthatthenetworkdevelopmentalsetcontainssuchoutliersOncedeveloped,aforecasterdoesnotneedspecificexpertisetooperateitCanbeusedincombinationwithotherforecastingmethods,oritcanbeusedastheprimaryforecastingmethod52工作报告NeuralNetworks–LimitationsComplexandnotcommonlyunderstood;thus,themethodcanbeinappropriatelyappliedanddifficulttodevelopDonotextrapolatedatawell;thus,extremepollutantconcentrationsnotincludedinthedevelopmentaldatasetwillnotbetakenintoconsiderationintheformulationoftheneuralnetworkpredictionRequire3-5yearsofmeasurementdataintheregionofapplication,includingmanyinstancesofairpollutionevents,todevelop.53工作报告NumericalModelingMathematicallyrepresentstheimportantprocessesthataffectpollutionRequiresasystemofmodelstosimulatetheemission,transport,diffusion,transformation,andremovalofairpollutionMeteorologicalforecastmodelsEmissionsmodelsAirqualitymodels54工作报告NumericalModeling–HowItWorks55工作报告ProcessesTreatedinGridModelsEmissionsSurfaceemittedsources(on-roadandnon-roadmobile,area,
low-levelpoint,biogenic,fires)Pointsources(electricalgeneration,industrial,other,fires)Advection(Transport)Dispersion(Diffusion)ChemicalTransformationVOCandNOxchemistry,radicalcycleForPMaerosolthermodynamicsandaqueous-phasechemistryDepositionDrydeposition(gasandparticles)Wetdeposition(rainoutandwashout,gasandparticles)BoundaryconditionsHorizontalboundaryconditionsTopboundaryconditions56工作报告PhotochemicalGridModelConcept57工作报告EulerianGridCellProcesses58工作报告CouplingBetweenGridCells59工作报告NumericalModeling–Example60工作报告DevelopingaNumericalModelDesignandplanthesystemIdentifyandallocatetheresourcesAcquirerequiredgeophysicaldataImplementthedataacquisitionandprocessingtools,componentmodels(emissions,meteorological,andairquality),andanalysisprograms.DeveloptheemissioninventoryTesttheoperationofalldataacquisitionprograms,preprocessorprograms,componentmodels,andanalysisprogramsasasystemIntegratedataacquisitionandprocessingtools,componentmodels,andanalysisprogramsintoanoperationalsystemTest,evaluate,andimprovetheintegratedsystem61工作报告DevelopingaNumericalModel(1of7)DesignandplanthesystemDecideonwhichpollutantstoforecast.Definemodelingdomainsconsideringgeographyandemissionssources.Selectcomponentmodelsconsideringforecastpollutants,domains,componentmodelcompatibility,availabilityofinterfaceprograms,andavailableresources.Determinehardwareandsoftwarerequirements.Identifysourcesofmeteorological,emissions,andairqualitydata.Prepareadetailedplanforacquiringandintegratingdataacquisition,modeling,andanalysissoftware.Planforcontinuousreal-timeevaluationofthemodelingsystem.
62工作报告IdentifyandallocatetheresourcesStaffforsystemimplementationandoperationsComputingandstorageconsistentwiththeselectionofdomainsandmodelsCommunicationsfordatatransferintoandoutofthemodelingsystemAcquirerequiredgeophysicaldataTopographicaldataLandusedataDevelopingaNumericalModel
(2of7)63工作报告Implementthedataacquisitionandprocessingtools,componentmodels(emissions,meteorological,andairquality),andanalysisprograms.Implementeachprogramindividually.Usestandardtestcasestoverifycorrectimplementation.DevelopingaNumericalModel
(3of7)64工作报告DeveloptheemissioninventoryAcquireneededemissioninventoryrelateddata.Reviewtheemissionsdataforaccuracy.Besurethattheemissioninventoryincludesthemostrecentemissionsdataavailable.Updatethebaseemissioninventoryannually.DevelopingaNumericalModel
(4of7)65工作报告TestandEvaluateTesttheoperationofalldataacquisitionprograms,preprocessorprograms,componentmodels,andanalysisprogramsasasystem.Reviewtheprognosticmeteorologicalforecastdataforaccuracyoverseveralweeksundervariousweatherpatterns.Runthecombinedmeteorological/emissions/airqualitymodelingsysteminaprognosticmodeusingavarietyofmeteorologicalandairqualityconditions.Evaluatetheperformanceofthemodelingsystembycomparingitwithobservations.Refinethemodelapplicationprocedures(i.e.,themethodsofselectingboundaryconditionsorinitialconcentrationfields,thenumberofspin-updays,thegridboundaries,etc.)toimproveperformance.DevelopingaNumericalModel
(5of7)66工作报告Integratedataacquisitionandprocessingtools,componentmodels,andanalysisprogramsintoanoperationalsystemImplementautomatedprocessesfordataacquisition,thedailydataexchangefromtheprognosticmeteorologicalmodelandtheemissionsmodeltothe3-Dairqualitymodelandanalysisprograms,andforecastproductproduction.Implementautomatedprocessesbyusingscriptingandschedulingtools.Verifythattheforecastproductsreflecttheactualmodelpredictions.DevelopingaNumericalModel
(6of7)67工作报告Test,evaluate,andimprovetheintegratedsystemRunthemodelinreal-timetestmodeforanextendedperiod.Compareoutputtoobserveddataandnotewhentherearemodelfailures.Afterobtainingsatisfactoryresultsonaconsistentbasis,usethemodelingsystemtoforecastpollutantconcentrations.Documentthemodelingsystem.Continuouslyevaluatethesystem’sperformancebycomparingobservationsandpredictions.Implementimprovementsasneededbasedonperformanceevaluationsandnewinformation.DevelopingaNumericalModel
(7of7)68工作报告NumericalModeling–StrengthsTheyarephenomenologicalbased,simulatingthephysicalandchemicalprocessesthatresultintheformationanddestructionofairpollutants.Theycanforecastforalargegeographicarea.Theycanpredictairpollutioninareaswheretherearenoairqualitymeasurements.Themodelforecastscanbepresentedasmapsofairqualitytoshowhowpredictedairqualityvariesoveraregionhourbyhour.Themodelscanbeusedtofurtherunderstandtheprocessesthatcontrolairpollutioninaspecificarea.Forexample,theycanbeusedtoassesstheimportanceoflocalemissionssourcesorlong-rangetransport.69工作报告NumericalModeling–LimitationsInaccuraciesintheprognosticmodelforecastsofwindspeeds,winddirections,extentofverticalmixing,andsolarinsulationmaylimit3-Dairqualitymodelperformance.Emissioninventoriesusedincurrentmodelsareoftenoutofdateandbasedonuncertainemissionfactorsandactivitylevels.Site-by-siteozoneconcentrationspredictedby3-Dairqualityforecastmodelsmaynotbeaccurateduetosmall-scaleweatherandemissionfeaturesthatarenotcapturedinthemodel.Substantialstaffandcomputerresourcesareneededtoestablishascientificallysoundandautomatedairqualityforecastsystembasedona3-Dairqualitymodel.70工作报告AustralianAirQualityForecastingSystemPeterManinsCSIROMarineandAtmosphericResearchAustraliaWMOGURMESAGmemberDemonstrationProject71工作报告Phenomenological–HowItWorksReliesonforecasterexperienceandcapabilitiesForecasterneedsgoodunderstandingoftheprocessesthatinfluencepollutionsuchasthesynoptic,regional,andlocalmeteorologicalconditions,plusairqualitycharacteristicsintheforecastarea.Forecastersynthesizestheinformationbyanalyzin
温馨提示
- 1. 本站所有资源如无特殊说明,都需要本地电脑安装OFFICE2007和PDF阅读器。图纸软件为CAD,CAXA,PROE,UG,SolidWorks等.压缩文件请下载最新的WinRAR软件解压。
- 2. 本站的文档不包含任何第三方提供的附件图纸等,如果需要附件,请联系上传者。文件的所有权益归上传用户所有。
- 3. 本站RAR压缩包中若带图纸,网页内容里面会有图纸预览,若没有图纸预览就没有图纸。
- 4. 未经权益所有人同意不得将文件中的内容挪作商业或盈利用途。
- 5. 人人文库网仅提供信息存储空间,仅对用户上传内容的表现方式做保护处理,对用户上传分享的文档内容本身不做任何修改或编辑,并不能对任何下载内容负责。
- 6. 下载文件中如有侵权或不适当内容,请与我们联系,我们立即纠正。
- 7. 本站不保证下载资源的准确性、安全性和完整性, 同时也不承担用户因使用这些下载资源对自己和他人造成任何形式的伤害或损失。
最新文档
- 伤寒的诊断和治疗
- 2024年中外技术转让合同模板
- 2024至2030年中国可调式自动捆扎机数据监测研究报告
- 2024至2030年中国防静电架空地板行业投资前景及策略咨询研究报告
- 2024至2030年中国钻石批花刀行业投资前景及策略咨询研究报告
- 2024年焦化甲苯项目成效分析报告
- 2024年石英玻璃纤维套管项目综合评估报告
- 2024年自装卸补给车项目评价分析报告
- 2024至2030年中国紧急锁止三点式安全带行业投资前景及策略咨询研究报告
- 2024至2030年中国盆栽非洲菊数据监测研究报告
- 马渭丽《月光下的中国》
- 专题38事件的相互独立性条件概率与全概率公式(理科)(教师版)
- 陶行知与乡村教育智慧树知到期末考试答案2024年
- SFT 0097-2021 医疗损害司法鉴定指南-PDF解密
- 广播电视编导职业生涯规划
- 《蜀相》课件 统编版高中语文选择性必修下册
- 原地8字舞龙课课件高一上学期体育与健康人教版
- MOOC 大学生创新创业热点问题-福建师范大学 中国大学慕课答案
- (2024年)solidworks完整教程学习课程
- 放射性肠炎中炎症相关细胞因子的作用机制及靶向治疗
- 新能源汽车的市场价格变化趋势
评论
0/150
提交评论