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
- 下半造价师工程计价知识点缺陷责任期考试试题
- 公开课英语单词快速记忆
- 高中语文第3单元古思今赏第8课寡人之于国也课件新人教版必修
- 窗帘布艺:团队卓越之旅-项目管理能力与团队合作精进之路
- 独树一帜的中国画 课件 2024-2025学年人教版初中美术九年级上册
- 高中语文10蜀道难登高课件苏教版必修
- 2024至2030年中国控天线弹簧数据监测研究报告
- 2024至2030年中国引线式石英晶体振荡器行业投资前景及策略咨询研究报告
- 2024至2030年中国差速器十字轴行业投资前景及策略咨询研究报告
- 2024至2030年中国大小鼠灌胃针行业投资前景及策略咨询研究报告
- 2024高考物理一轮复习 第13讲 牛顿第二定律的基本应用(课件)
- 【九上沪科版数学】安徽省安庆市2023-2024学年九年级上学期期中数学试题
- 书法鉴赏 (浙江财大版)学习通超星期末考试答案章节答案2024年
- 屋面光伏发电施工方案
- 期中考试卷(试题)-2024-2025学年四年级上册数学人教版
- 师范生的教育调查报告范文(3篇)
- 期中核心素养卷(试题)-2024-2025学年数学四年级上册苏教版
- 043.中国老年心肺复苏急诊专家共识2024
- 浙江省金华市兰溪市2023-2024学年五年级上学期期中数学试卷
- 农业经理人(中级)技能认证考试复习题及答案
- 绿植花卉租摆及园林养护服务投标方案(技术方案)
评论
0/150
提交评论