assessment of model forecast temperature bias during cold ......these ‘busted’ cad events failed...
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AssessmentofModelForecastTemperatureBiasDuringColdAirDammingintheCentral
AppalachianMountains
SuzannaLindeman
ThesissubmittedtothefacultyoftheVirginiaPolytechnicInstituteandStateUniversityinpartialfulfillmentoftherequirementsforthedegreeof
MasterofScienceIn
Geography
AndrewEllisStephenKeightonDavidCarroll
April2018Blacksburg,VA
Keywords:cold-airdamming,ModelOutputStatistics,temperaturebias,central
AppalachianMountains
Copyright©bySuzannaLindeman
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AssessmentofModelForecastTemperatureBiasDuringColdAirDammingintheCentralAppalacianMountains
SuzannaLindeman
Abstract
Cold-airdamming(CAD)isaprevalentMid-AtlanticUnitedStatesweatherphenomenonthatoccurswhencold,denseairisdammedalongsidetheeasternslopesoftheAppalachianMountains.Lower-than-normalmaximumtemperatures,increasedandprolongedcloudcover,andprecipitationthatproduceshazardousimpactsarecommonfeaturesofthisweatherevent,whicharewellknownforpresentingdifficultiestobothhumanforecastersandweatherpredictionmodels.ThisstudyexploresCADeventsbetween2007and2016archivedinaBlacksburgNationalWeatherService‘bust’database–instanceswhenforecasterserredbyatleast8°F(4.4°C)oneithermaximumorminimumdailyairtemperature.ThedatabaseincludesthetemperatureerrorwithinModelOutputStatistics(MOS)guidanceinassociationwiththeseforecast‘busts.’Duringthe10-yearstudyperiod,MOSguidanceproducedwarm-biasedmaximumtemperaturesandcold-biasedminimumtemperaturesformostoftheproblematicCADevents,suggestingMOSguidancetendedtounderestimatethestrengthofCADinthesecases,seemingtostrugglewithweakerCADevents.DuringCADerosion,MOStendedtoprematurelyerodeCADscenariosatnightandpredictedthemtopersistfortoolongduringtheday.Hourlysurfacemeteorologicalandsynopticatmospherecompositesduringthese‘busted’CADeventsfailedtorevealobviousdifferencesfromwhatisexpectedforcentralAppalachianCAD.However,acomparisontowell-forecastclassiccold-seasonCADeventssuggestthatbustedcasesofthissametypeofCADmaybedrierthanistypical.AstheatmosphericpatternsassociatedwithbustedCADeventsaretypicalofthephenomenon,butabitweakerormoremarginal,forecasterrorsappeartostemfromsubtlemodelerrorsratherthanforecastererror.Itispossiblethatthemodelsmayinadequatelycharacterizelow-levelmoisture,butfurtherresearchisneededtoisolatethesourceofmodelforecasterror.Nonetheless,theresultsofthisresearchserveasguidanceforoperationalforecastersastheyconsidermodelguidanceduringweakCADevents.
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AssessmentofModelForecastTemperatureBiasDuringColdAirDammingintheCentralAppalacianMountains
SuzannaLindeman
GeneralAudienceAbstract
Cold-airdamming(CAD)isacommonweatherpatternthataffectstheBlueRidgeMountainregionoftheeasternUnitedStates,inwhichcoldairattheatmosphere’ssurfaceisdirectedfromtheNortheastandisdammedagainsttheeasternAppalachianMountains.Thisweathereventcauseslower-than-normaltemperaturesovertheregionandisoftencharacterizedbyprolongedcloudyskiesandprecipitation.CADisverydifficultforforecasterstoaccuratelypredict,astheyrelyonweatherforecastmodelsthatoftensimulatethesesituationspoorly.CADalsostrainsemergencymanagerswhorelyonaccurateforecaststosupportpublicsafetyduringCAD.ThisstudyexploresCADeventsbetween2007and2016archivedinaBlacksburgNationalWeatherService‘bust’database–instanceswhenforecasterserredbyatleast8°F(4.4°C)oneithermaximumorminimumdailyairtemperature.ThedatabaseincludesthetemperatureerrorwithinModelOutputStatistics(MOS)guidanceinassociationwiththeseforecast‘busts.’Duringthe10-yearstudyperiod,MOSguidanceforecastedmaximumtemperaturestoohighandminimumtemperaturestoolowformostoftheproblematicCADevents,suggestingMOSguidancetendedtounderestimatethestrengthofCADinthesecases,seemingtostrugglewithweakerCADevents.DuringinstanceswhereCADdissolvedfromtheAppalachians,MOStendedtoprematurelyerodeCADscenariosatnightandpredictedthemtopersistfortoolongduringtheday.Hourlysurfacemeteorologicalandsynopticatmospherecompositesduringthese‘busted’CADeventsfailedtorevealobviousdifferencesfromwhatisexpectedforcentralAppalachianCAD.However,acomparisontowell-forecastclassiccold-seasonCADeventssuggestthatbustedcasesofthissametypeofCADmaybedrierthanistypical.AstheatmosphericpatternsassociatedwithbustedCADeventsaretypicalofthephenomenon,butabitweakerormoremarginal,forecasterrorsappeartostemfromsubtlemodelerrorsratherthanforecastererror.Itispossiblethatthemodelsmayinadequatelycharacterizelow-levelmoisture,butfurtherresearchisneededtoisolatethesourceofmodelforecasterror.Nonetheless,theresultsofthisresearchserveasguidanceforoperationalforecastersastheyconsidermodelguidanceduringweakCADevents.
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Acknowledgements
Foremost,IexpressmyimmensegratitudetowardsmyadvisorProf.AndrewEllis
forhiscontinuedsupportthroughoutbothmyundergraduateandgraduatecareers
atVirginiaTech.Hispatience,expansiveknowledge,andwizprogrammingskills
wereinvaluableinbothresearchingandwritingthisthesis.Icouldnothave
imaginedabetter-suitedmentorformyMaster’sthesisandwouldnothave
succeededwithouthisimmeasurablehelp.
Mygratitudeextendstotheothermembersofmythesiscommittee,Stephen
KeightonandDavidCarroll.Theirkindwordsandinstrumentalfeedbackgreatly
guidedandmotivatedmethroughoutthisresearchprocess.
IalsospreadthankstotheNationalWeatherServiceForecastOfficeinBlacksburg,
VirginiafortheirconstantloveandsupportinhelpingmethroughmyMaster’s
degree.Feedbackfromexpertoperationalmeteorologistshasbeenmassively
helpfulinthisresearch,andIespeciallythankRobertStonefieldforhiskindheart
andextremelyvaluablehelpasacold-airdammingexpert.
Lastly,Ithankmyfamilyandfriends.Withoutthem,noneofthiswouldhavebeen
possible.
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TableofContentsChapter Page Abstract……………………………………………………………………………………………………..GeneralAudienceAbstract………………………………………………………………………….
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Acknowledgements……………………………………………………………………………………. ivListofFigures……………………………………………………………………………………………. viiListofTables……………………………………………………………………………………………...ListofAbbreviations…………………………………………………………………………………..
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1.Introduction………………………………………………………………………………………….
1.1ProblemStatement…………………………………………………………………….16
2.LiteratureReview…………………………………………………………………………………2.1SynopticDriversofCold-airDamming………………………………………...2.2ClimatologyofCold-airDamming.……………………………………………….2.3ClassificationsofCold-airDamming…………………………………………….2.4SensibleWeatherAssociatedwithCold-airDamming…………………..2.5Cold-airDammingErosion………………………………………………………….2.6VerticalSoundingsinCold-airDammingAnalyses……………………….2.7ForecastingCold-airDamming……………………………………………………2.8NumericalWeatherPredictionofCold-airDamming……………………2.9ModelOutputStatisticsForecastsofCold-airDamming……………….2.10Summary…………………………………………………………………………………
99101112131314151618
3.DataandMethods…………………………………………………………………………………3.1NationalWeatherServiceCold-airDamming‘Bust’Database(2007-2016)……………………………………………………………………………………3.2MOSGuidanceArchivedDataRetrieval……………………………………….3.3AbbreviatedClimatologyCompilation…………………………………………3.4AssessingMAVandMETGuidanceBias………………………………………3.5AssessingModelBiasOverTime…………………………………………………3.6HourlySurfaceComposites…………………………………………………………3.7SynopticComposites…………………………………………………………………..3.8UpperAirSoundingComposites…………………………………………………3.9ClassicCold-airDammingClimatology………………………………………..
19192223232324242526
4.ResultsandDiscussion…………………………………………………………………………4.1Reviewof‘Bust’DatabaseForecasterComments…………………………4.2ClimatologyofCold-airDamming‘Busts’……………………………………..
4.2.1AnnualDistribution………………………………………………………..
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4.2.2MonthlyDistribution………………………………………………………4.2.3Maximumvs.MinimumTemperatures……………………………..4.2.4SpatialDistribution………………………………………………………..
4.3MAVandMETGuidanceTemperatureBias…………………………………4.3.1TemperatureBiasResultsbyCold-airDammingClassification…………………………………………………………………………4.3.2TemperatureBiasResultsDuringCold-airDammingOnset.4.3.3TemperatureBiasResultsDuringCold-airDammingErosion………………………………………………………………………………….4.3.4StatisticalSignificance……………………………………………………
4.4SpatialMAVandMETGuidanceBias…………………………………………...4.4.1SpatialTemperatureBiasofCold-airDammingClassifications………………………………………………………………………..4.4.2SpatialTemperatureBiasDuringCold-airDammingOnset.4.4.3SpatialTemperatureBiasDuringCold-airDammingErosion………………………………………………………………………………….
303233373845465151525557
4.5MAVandMETTemperatureBiasOverTime………………………………..4.6HourlySurfaceComposites…………………………………………………………
4.6.1WarmSeasonHourlySurfaceComposites………………………..4.6.2ColdSeasonHourlySurfaceComposites…………………………..
4.7SynopticComposites………………………………………………………………….4.7.1SynopticCompositesofCold-airDammingClassifications…4.7.2SynopticCompositesofCold-airDammingOnset………………4.7.3SynopticCompositesofCold-airDammingErosion…………...4.7.5Summary………………………………………………………………………4.7.4SynopticDifferenceCompositesBasedonClassicScenarios.4.7.5SynopticCompositesofDaysBeforeCold-airDammingEvents…………………………………………………………………………………..
5964657075768588909094
4.8SoundingComposites…………………………………………………………………4.8.1SoundingComparisonofBustedClassic&SSC-IdentifiedClassicCold-airDammingEvents……………………………………………
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5.Conclusions………………………………………………………………………………………......5.1Summary…………………………………………………………………………………...5.2LimitationsandFutureWork……………………………………………………...5.4FinalThoughts…………………………………………………………………………...
References………………………………………………………………………………………………..
103103108110112
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ListofFiguresFigure1.1Figure1.2
Classiccold-airdammingwedgeexampleadaptedfromBaileyetal.(2003).…………………………………………………………..ReferencemapofgeographicalregionswithintheBlacksburgNWSFOCWA.AdaptedfromNOAA–NationalWeatherServiceForecastOfficeBlacksburg(2017).………….
24
Figure1.3 Examplesofweak,moderate,andstrongcoldairwedgingduringcentralAppalachiancold-airdammingepisodes;hatchedlinesrepresentcoldairwedgecoverageovertheCWAaccordingtowedgestrength.AdaptedfromNOAA(2017).……………………………………………………………………………..
5
Figure1.4 BlacksburgNationalWeatherServiceForecastOfficecountywarningarea.……………………………………………………………………
7
Figure2.1Figure3.1
AtmosphericflowduringAppalachiancold-airdammingadaptedfromBaileyetal.(2003).……………………………………...TheBlacksburgNWSFOcountywarningarea(CWA;greenshading)andthesixTAFsitesforwhichtemperatureforecastsareanalyzed.………………………………………………………
920
Figure4.1 Theannualoccurrenceofbustedcold-airdammingforecasts,2007to2016.…………………………………………………….
29
Figure4.2 Theannualfrequencyofbustedcold-airdammingforecastsstratifiedbyclassificationtype,2007to2016.……………………
30
Figure4.3 Thefrequencyofbustedcold-airdammingforecastsbymonth,2007to2016.………………………………………………………..
31
Figure4.4 Themonthlyfrequencyofbustedcold-airdammingforecastsstratifiedbytype,2007to2016.………………………….
32
Figure4.5 Thedaytimeandnighttimefrequencyofbustedcold-airdammingforecastsstratifiedbyclassification,2007to2016.
33
Figure4.6 Thefrequencywithwhichabustedcold-airdammingforecastwasevidentatasingleTAFsiteoratmultipleTAFsitessimultaneously,2007to2016.…………………………………...
34
Figure4.7 Thefrequencyofbustedcold-airdammingforecastsstratifiedbyTAFsitelocation,2007to2016.……………………..
35
Figure4.8 Thedistributionofbustedcold-airdammingcasesateachTAFsitesegregatedbyclassificationtype,2007to2016.……
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Figure4.9 Thefrequencyofproblematiccold-airdammingforecastsstratifiedbyTAFsitelocationandcold-airdammingclassification,2007to2016.………………………………………………
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Figure4.10a-b
Boxplotsof(a)MAVand(b)METmaximumtemperatureguidanceforecasterrorofallclassified(classic,hybrid,in-situ)problematicCADeventsbyforecastcycle(-12hoursto-60hours),2007to2016.Thetopsofthepurpleandbottomsofthegreenboxesrepresenttheupperandlowerquartilesofforecasterror,respectively,foreachperiod,andwhiskerbarsindicateminimumandmaximumdatavaluesacrosseachperiod’sdatarange.Betweenthetwoboxesliesthemedianofeachforecastcycle’serror,andmeanerrorvaluesaremarkedwithablackcircleandconnectedwithasmoothedtrendline.………………………………………………………..
42
Figure4.11a-b
AsinFigure4.11a,b,exceptfor(a)MAVand(b)METminimumtemperatureguidanceerror.……………………………...
44
Figure4.12a-b
Boxplotsof(a)MAVand(b)METmaximumtemperatureguidanceforecasterrorofproblematicCADeventsassociatedwitherosionbyforecastcycle(-12hoursto-60hours),2007to2016.Thetopsofthepurpleandbottomsofthegreenboxesrepresenttheupperandlowerquartilesofforecasterror,respectively,foreachperiod,andwhiskerbarsindicateminimumandmaximumdatavaluesacrosseachperiod’sdatarange.Betweenthetwoboxesliesthemedianofeachforecastcycle’serror,andmeanerrorvaluesaremarkedwithablackcircleandconnectedwithasmoothedtrendline.…………………………………………………………
47-48
Figure4.13a-b
AsinFigure4.12a-b,exceptfor(a)MAVand(b)METminimumtemperatureguidanceerror.……………………………...
49-50
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Figure4.14a-b
BoxplotsofyearlyMAV(a)andMET(b)guidancemaximumtemperatureforecasterrorsforallproblematiccold-airdammingeventsregardlessoftypeinthefirstforecastcycle(-12h)throughtheperiod2007to2016.Thetopsofthepurpleandbottomofthegreenboxesrepresenttheupperandlowerquartiles,respectively,offorecasterrorforeachperiod,andwhiskerbarsindicateminimumandmaximumdatavaluesacrosseachyear’sdatarange.Betweenthetwoboxesliesthemedianofeachyear’sforecasterror,andmeanerrorvaluesaremarkedwithablackcircleandconnectedwithasmoothedtimeseriesline.Thesamplesizeofeachyearislistedabovethex-axis.……….
60-61
Figure4.15 Themedianofannualmaximumtemperatureforecasterrorvalues,MAVvs.MET,12hoursinadvanceoftheforecastperiod,2007to2016.………………………………………………………..
62
Figure4.16 Theannualmedianmaximumtemperatureerrordifferentials(MAV-MET)byforecastcycle(-12hoursto-60hoursinadvanceoftheforecastperiod)betweenMAVandMETguidanceforecasterror,2007to2016.………………..
63
Figure4.17 Hourlysurfacecompositesofairtemperature(°F)duringproblematicwarmseasoncold-airdammingeventsateachofthesixTAFsites(Blacksburg(BCB),Bluefield(BLF),Danville(DAN),Lewisburg(LWB),Lynchburg(LYH),andRoanoke(ROA)).Forreference,theclimatologicalnormaltemperaturebetweenMayandOctoberfortheten-yearperiodatBlacksburgisplottedasadashedline.………………...
66
Figure4.18 AsasFigure4.17,exceptforrelativehumidity(%).………….... 67Figure4.19 AsinFigure4.17,exceptforwinddirection(degrees).………. 68Figure4.20 AsinFigure4.17,exceptforcloudceilingheights(kmagl).... 70Figure4.21 Hourlysurfacecompositesofairtemperature(°F)during
problematiccoldseasoncold-airdammingeventsateachofthesixTAFsites(Blacksburg(BCB),Bluefield(BLF),Danville(DAN),Lewisburg(LWB),Lynchburg(LYH),andRoanoke(ROA)).Forreference,theclimatologicalnormaltemperaturebetweenNovemberandAprilfortheten-yearperiodatBlacksburgisshownasadashedline.………………….
71
Figure4.22 AsinFigure4.21,exceptforrelativehumidity(%).………….... 72Figure4.23 AsinFigure4.21,exceptforwinddirection(degrees).………. 73Figure4.24 AsinFigure4.21,exceptforcloudceilingheights(kmagl).... 74
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Figure4.25a-c
Synopticcompositesfromthe14daysofcoldseasonclassicCADthatresultedinbustedforecasts.Shownare(a)sealevelpressure(Pa),(b)surfaceairtemperature(K),and(c)2-meterrelativehumidity(%).…………………………………............
76-77
Figure4.26a-f
Synopticcompositesfromthe14daysofcoldseasonclassicCADthatresultedinbustedforecasts.Shownare(a)925mband(b)850mbgeopotentialheight(m),(c)925mband(d)850mbairtemperature(K),and(e)925mband(f)850mbvectorwinds(m/s).…………………………………………………………..
77-78
Figure4.27a-c
Asin4.25a-f,exceptforthe12daysofproblematicwarmseasonhybridCADcases.…………………………………………………..
79
Figure4.28a-f
AsinFigure4.26a-f,exceptforthe12problematicwarmseasonhybridCADscenarios.……………………………………………
80-81
Figure4.29a-c
Asin4.25a-f,exceptforthe7daysofproblematiccoldseasonhybridCADcases.…………………………………………………..
81-82
Figure4.30a-c
Synopticcompositesfromthe14daysofcoldseasonclassicCADthatresultedinbustedforecasts.Shownare(a)925mbgeopotentialheight(m),(b)925mbairtemperature(K),and(c)925mbvectorwinds(m/s).…………………………………...
82-83
Figure4.31a-cFigure4.32a-cFigure4.33a-c
Asin4.25a-c,exceptforthe7daysofproblematicwarmseasonin-situCADscenarios.…………………………………………….AsinFigure4.30a-c,exceptforthe7warmseasonin-situproblematicCADscenarios.……………………………………………….Synopticcompositesfromthe11daysofcoldseasonCADonsetthatresultedinbustedforecastsduringthecoldseason.Shownare(a)sealevelpressure(Pa),(b)surfaceairtemperature(K),and(c)2-meterrelativehumidity(%).……
848586
Figure4.34a-c
Synopticcompositesfromthe11daysofCADonsetthatresultedinbustedforecastsduringthecoldseason.Shownare(a)925mbgeopotentialheight(m),(b)925mbairtemperature(K),and(c)925mbvectorwinds(m/s).…………
87
Figure4.35a-c
Synopticcompositesfromthe40daysofcoldseasonCADerosionthatresultedinbustedforecasts.Shownare(a)sealevelpressure(Pa),(b)surfaceairtemperature(K),and(c)2-meterrelativehumidity(%).………………………………………….
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Figure4.36a-c
Synopticcompositesfromthe40daysofcoldseasonCADerosionthatresultedinbustedforecasts.Shownare(a)925mbgeopotentialheight(m),(b)925mbairtemperature(K),and(c)925mbvectorwinds(m/s).…………………………….
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Figure4.37a-e
Synopticdifferencecompositesofsurfaceairtemperature(K)for(b)hybrid,(c)in-situ,(d)onset,and(e)erosionscenariosbasedonthatof(a)problematicclassiccold-airdammingevents.………………………………………………………………
91-92
Figure4.38a-e
AsinFigure4.35a-f,exceptformeansealevelpressure(mb).………………………………………………………………………………..
93-94
Figure4.39a-b
Soundingcompositesofactualatmosphericconditionsduringbustedcold-airdammingforecastsassociatedwith(a)onsetand(b)erosion,2007to2016.…………………………….
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Figure4.40 Soundingcompositesofactualatmosphericconditionsduringwarmseasonhybridandin-situbustedcold-airdammingforecasts,2007to2016..…………………………………….
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Figure4.41 Soundingcompositesofactualatmosphericconditionsduringcoldseasonclassicandhybridbustedcold-airdammingforecasts,2007to2016.……………………………………...
100
Figure4.42 Atmosphericsoundingcompositesofactualconditionsduringcoldseason(NovembertoApril)bustedclassiccold-airdammingcasesversuswell-forecastedclassiccasesatBlacksburg(KRNK)to500mb,2007to2016.……………………..
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ListofTablesTable4.1 Occurrencesofproblematiccold-airdammingforecastsby
classificationtype.Datesarelistedasmonth/day/year(last2digits).Theerroneoustemperatureforecastisindicatedparentheticallyaseithermaximum(Hi)orminimum(Lo)dailytemperature.…………………………………………………………………………...
28
Table4.2 Errorstatisticsforforecastsofproblematicclassiccold-airdammingeventsbyforecastcycle,2007to2016.StatisticsarepresentedforhighandlowtemperatureforecastsforMAVandMET.Meanerror(°F),meanabsoluteerror(°F),andbiasstatistic(unitless)arepresented.……………………………………………..
39
Table4.3 AsinTable4.2,exceptforproblematichybridCADevents.……….. 39Table4.4 AsinTable4.2,exceptforproblematicin-situCADeventsand
onlyformaximum(high)temperature.…………………………………….39
Table4.5 Errorstatisticsforforecastsofallclassic,hybrid,andin-situproblematiccold-airdammingeventsbyforecastcycle,2007to2016.StatisticsarepresentedforhighandlowtemperatureforecastsforMAVandMET.Meanerror(°F),meanabsoluteerror(°F),andbiasstatistic(unitless)arepresented.………………..
40
Table4.6 Errorstatisticsforforecastsofcold-airdammingeventscharacterizedbyproblematiconsetbyforecastcycle,2007to2016.StatisticsarepresentedforhighandlowtemperatureforecastsforMAVandMET.Meanerror(°F),meanabsoluteerror(°F),andbiasstatistic(unitless)arepresented.……………….
45
Table4.7 Errorstatisticsforforecastsofcold-airdammingeventscharacterizedbyproblematicerosionbyforecastcycle,2007to2016.StatisticsarepresentedforhighandlowtemperatureforecastsforMAVandMET.Meanerror(°F),meanabsoluteerror(°F),andbiasstatistic(unitless)arepresented.………………..
46
Table4.8 Two-Samplet-testresultsofsignificanceforMAVandMETmaximumandminimumforecastsoverthefive12-hourforecastcyclesinadvanceofproblematiccold-airdammingevents,2007to2016.Statisticsarepresentedforbothmaximumandminimumtemperatureforecasts,irrespectiveofCADtype,forMAVandMET-basedguidancethroughallforecastcycles.Samplesize,samplemean(°F),standarddeviation(°F),standarderror(°F),andp-value(unitless)arepresented.…………
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Table4.9 Errorstatisticsforforecastsofproblematiccold-airdammingeventsattheBlacksburgTAFsitebyforecastcycle,2007to2016.StatisticsarepresentedforhighandlowtemperatureforecastsforMAVandMET.Meanerror(°F),meanabsoluteerror(°F),andbiasstatistic(unitless)arepresented.……………….
52
Table4.10 Errorstatisticsforforecastsofproblematiccold-airdammingeventsattheBluefieldTAFsitebyforecastcycle,2007to2016.StatisticsarepresentedforhighandlowtemperatureforecastsforMAVandMET.Meanerror(°F),meanabsoluteerror(°F),andbiasstatistic(unitless)arepresented.………………………………..
53
Table4.11 AsinTable4.10,exceptforerrorstatisticsattheLynchburgTAFsite.…………………………………………………………………………………………
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Table4.12 AsinTable4.10,exceptforerrorstatisticsattheLewisburgTAFsite.………………………………………………………………………………….
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Table4.13 AsinTable4.10,exceptforerrorstatisticsattheRoanokeTAFsite.…………………………………………………………………………………………
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Table4.14Table4.15Table4.16Table4.17Table4.18Table4.19
Errorstatisticsforforecastsofproblematiccold-airdammingeventsassociatedwitherosionattheBluefieldTAFsitebyforecastcycle,2007to2016.StatisticsarepresentedforhightemperatureforecastsforMAVandMET.Meanerror(°F),meanabsoluteerror(°F),andbiasstatistic(unitless)arepresented.….AsinTable4.14,exceptforerrorstatisticsattheLewisburgTAFsite.…………………………………………………………………………………………AsinTable4.14,exceptforerrorstatisticsattheDanvilleTAFsite.…………………………………………………………………………………………AsinTable4.14,exceptforerrorstatisticsattheLynchburgTAFsite.…………………………………………………………………………………………AsinTable4.14,exceptforerrorstatisticsattheRoanokeTAFsite.…………………………………………………………………………………………AsinTable4.14,exceptforerrorstatisticsattheBlacksburgTAFsite.…………………………………………………………………………………………
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ListofAbbreviationsInorderofappearance:CADNAM
Cold-airDammingNorthAmericanMesoscaleForecastSystem
RAP RapidRefreshModelGFS GlobalForecastSystemModelECMWF EuropeanMediumRangeWeatherForecastModelNWSFO NationalWeatherServiceForecastOfficeCWAMOS
CountyWarningAreaModelOutputStatistics
MAV GFSMOSShort-rangeTextProductMETQPFTAFNCEINCEPNARRNOAAESRLSSCMPGISLWBBLFBCBLYHROADAN
NAMMOSTextProductQuantitativePrecipitationForecastTerminalAerodromeForecastNationalCentersforEnvironmentalInformationNationalCentersforEnvironmentalPredictionNorthAmericanRegionalReanalysisNationalOceanicandAtmosphericAdministrationEarthSystemsResearchLaboratorySpatialSynopticClassificationMoistPolarGeospatialInformationSystemLewisburgBluefieldBlacksburgLynchburgRoanokeDanville
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Chapter1.Introduction Mesoscalemeteorologygenerallyreferstoweatherphenomenathatoccuron
aspatialscalebetween10and1,000kilometers.Someofthemostimpactful
weathereventsareclassifiedasmesoscale,includingsqualllinethunderstorms,
straight-linewinds,andcold-airdamming.Mesoscalemeteorologyishighly
influencedbytopographicalfeaturesthatcandictateatmosphericforcesthatdrive
airflowandprecipitationpatterns,introducingcomplexitythatmakesforecasting
difficult.
Meteorologistsrelyonconceptualknowledge,forecastingexperience,real-
timeobservations,andweatherpredictionmodelscollectivelytoproduceforecasts.
Forecastingmesoscalemeteorologicalphenomenahasbecomemoresuccessfulin
recentdecadesasaresultoftheintroductionandimprovementofmesoscale
numericalweatherpredictionmodels.Suchmodels,includingtheNorthAmerican
MesoscaleForecastSystem(NAM)andtheRapidRefreshModel(RAP),havefine
verticalandhorizontalgridresolutions.Theincreasedresolutionbetterrepresents
thedetailsoftheatmosphereandtheunderlyingphysicalgeographythando
coarserresolutionglobalmodelsliketheGlobalForecastSystemModel(GFS)and
theEuropeanMediumRangeWeatherForecastModel(ECMWF);however,despite
moderntechnologicaladvancements,forecastingmesoscaleweathereventsin
mountainousregionscanprovetobeadifficulttask.Complextopographical
variationsinfluencemountainweatherpatterns,generatingsmall-scaleatmospheric
vagariesthatweatherpredictionmodelsdonotalwaysaccuratelyrepresent.
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Thestructureofmountainlandscapesisknownworldwidetohavecomplex
interactionswithatmosphericflow,influencingbothsmallandlarge-scaleweather
patterns(Stauffer&Warner,1987;Rackley&Knox,2016).Oneexampleisthatof
stablesurfaceaircirculatingaroundaparentanticycloneandbecomingentrenched
alongtheslopesofmountainrangesthatactasorographicbarriers(Richwien,
1980;Bell&Bosart,1988).Whenrelativelycoldsurfaceaircollectsagainstan
orographicbarriersuchastheAppalachianMountainsintheeasternUnitedStates,
itistermedcold-airdamming(CAD),acoldairwedge,orcolddome.This
phenomenonisidentifiableinsea-levelisobarplotsbyaninvertedhigh-pressure
ridgeintheshapeofa‘U’(Stauffer&Warner,1987;Bell&Bosart,1988;Koch,
2001)(Figure1.1).
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Figure1.1.Classiccold-airdammingwedgeexampleadaptedfromBaileyetal.(2003).
Thispressure‘wedge’resultsinlowertemperatureswithinthedammingregion
thatcansurpassa20°Fdeparturefromsurroundingareas(Bell&Bosart,1988;Xu,
1990;Baileyetal.,2003).Thepressurewedgeisoftenmaintainedbycold-air
advectionatthesurfaceandisstrengthenedbydiabaticprocessesresultingfrom
evaporativecoolingandcloudcover(Bell&Bosart,1988;Rackley&Knox,2016).
ForecastingprecipitationtypeduringCADiswell-knownwithinthemeteorological
communityasadifficulttask;thecomplextopographyoftheAppalachians,for
example,oftendictatesprecipitationtypeduringadammingeventthatcanresultin
hazardousconditions,especiallyduringwinter,stressingtheimportanceofthese
forecasts(Stauffer&Warner,1987;Bell&Bosart,1988;Koch,2001).Despite
modernimprovementsinhigh-resolutionmesoscalemodels,theevolutionand
erosionoftheseeventsarenotwellrepresentedbynumericalweatherprediction
models(Stauffer&Warner,1987;Baileyetal.,2003;Stanton,2003;Mahoney,2006,
Rackley&Knox,2016).
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Figure1.2.ReferencemapofgeographicalregionswithintheBlacksburgNWSFOCWA.AdaptedfromNOAA–NationalWeatherServiceForecastOfficeBlacksburg(2017). AtypicalAppalachianCADwedgewillsettleintotheBlueRidgeMountains
fromnortheastoftheBlacksburgNationalWeatherServiceForecastOffice
(NWSFO)countywarningarea(CWA)(SeeFigure1.2),poolingsouthwestward
againsttheAppalachianmountainsascoldairisadvectedfromthenortheast
(Figure1.3).
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Figure1.3.Examplesofweak,moderate,andstrongcoldairwedgingduringcentralAppalachiancold-airdammingepisodes;hatchedlinesrepresentcoldairwedgecoverageovertheCWAaccordingtowedgestrength.AdaptedfromNOAA(2017).Aweakcolddome,oftencharacterizedbylightwindsanddiabaticcoolingfrom
precipitation,iswedgedintotheregionalongtheeasternAppalachianMountain
slopes,settlingbelowtheAlleghenyHighlandsintotheShenandoahValleyand
nearbyfoothills.Amoderatewedge,generallydrivenbybothprecipitativecooling
andcold-airadvection,willfillthePiedmontandBlueRidgeMountainvalleystothe
EasternContinentalDivide,andthestrongestCADscenarioswillengulfthemajority
oftheNWSFOCWAupthroughitshighestelevations(about2,500feetforthecold
airtoovercometheContinentalDivide).ThesestrongCADcasesaredriven
primarilybycold-airadvectionsynopticallyforcedbyastrongparentanticycloneto
thenortheast.Erosionprocesses,commonlyspurredbyanapproachingcoldfront
orradiationalheating,areusuallyreversed,inwhichthewedgedissolvesfrom
southwesttonortheast.Subsequently,thelastplacestoridofthecolddomeare
generallythefirsttowelcomeCADonset.
Relativelylowtemperatures,increasedcloudcover,andvarying
precipitationintensityandtypearecharacteristicofatypicalCADevent.Strong
dammingcasesarecapableofproducinghazardousconditions,mostnotablyduring
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thewinter,whichcanbringAppalachiancommunitiestoahaltduetolowcloud
cover,reducedvisibility,andfrozenprecipitation(Forbesetal.,1987).The
uncertaintyofforecastingthetypeandamountofprecipitationduringtheseevents
notonlyincreasesrisktothegeneralpublic,butitalsopressuresemergency
managerswhorelyonthisinformationtomitigatetheimpactsofsnowandice
accumulation(Forbesetal.,1987;Mahoney,2006).AsForbesetal.(1987)noted
withintheirwork,albeit30yearsago,“astudyofextendedoutagesbysixutility
companiesrevealedthatonlyoneofsixstormswaspredictedsufficientlyin
advanceforcrewstobeplacedonstand-by”(p.564).Meteorologistserred
significantlyon70percentoftheseCADtemperatureandprecipitationforecasts24
hoursinadvance(Forbesetal.,1987),androadcrewsheavilyrelyonthese
forecastsfortreatingroadsinpreparationforCADstorms.WhiletheworkofForbes
etal.wasconductedover30yearsago,manyoftheforecastingchallenges
presentedbyCADremaintoday.
1.1ProblemStatement Researchduringthelatterhalfofthe20thcenturyyieldedanextensive
literaturefocusedonCAD;however,thesubjecthasbeensomewhatabsentfromthe
bodyofliteratureofthepasttwodecades.Manythoroughstudieshavebeen
conductedforAppalachianCAD,yetmostfocusontheentireextentofthe
SoutheasternUnitedStates,andsomeevenstemasfarsouthasFloridawhilegiving
littlefocusedattentiontothenorthernextentofthisphenomenon.TheBlueRidge
MountainsregionofVirginiaisnotedforpersistentCADepisodes,makingthe
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BlacksburgNWSFOCWA(seeFigure1.4)anattractivestudydomainforCAD
research.
Figure1.4.BlacksburgNationalWeatherServiceForecastOfficecountywarningarea.Additionally,thisnarrowedspatialdomainforCADhasjustrecentlybeguntogain
interestthroughoutliterature,notablybyEllisetal.(2017).Investigatingthe
accuracyofmodel-derivedairtemperatureforecastsinthisareaduringCADevents,
whichhasnotbeenpreviouslypublished,intheformofMAV(GFSMOSShort-range
TextProduct)andMET(NAMMOSTextProduct)-basedMOS(ModelOutput
Statistics)willaidforecasters.Furthermore,asMahoney(2006)explains,
“If a forecaster recognizes that a model forecast of aspecific meteorological feature has been stronglyinfluenced by a given physical parameterizationpackage in a givenmodel, heor she is in aposition tobetter evaluate the uncertainty associated with thataspect of the forecast… this type of knowledge allows
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forecasterstobestdeterminewhentheyarecapableofaddingvaluetoamodelforecast”(p.466).
AnimprovedunderstandingofMOSguidanceminimumandmaximumtemperature
biasesduringCADeventsintheBlueRidgeMountainsisimportantbecause
operationalmeteorologistswillbeabletoaddvaluetotheirforecastingdecisions
wheninterpretingweathermodelforecastsintheformofMAVandMEToutput
statistics.Forecasterscanusethisinformationtoproduceimprovedforecastsand,
inturn,supportpublicsafetyduringCADscenarios.Inlightofthebasicandapplied
researchneedsoutlinedhere,theproposedthesisresearchwillengagethe
followingresearchquestions:
1. HowinaccurateareMAVandMET-derivedMOSforecastsforair
temperatureduringcentralAppalachianMountaincold-air
dammingeventsidentifiedasproblematicbyforecastersbetween
2007and2016?
2. Havethesemodelsexhibitedtemperaturebiasesintheforecasts,
andwhymighthavethisoccurred?
3. Howmightforecastersintroduceanimprovedunderstandingofany
MAVandMETguidancetemperaturebiasduringcold-airdamming
scenariosinthecentralAppalachianMountainstoimprove
forecasts?
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Chapter2.LiteratureReview2.1SynopticDriversofCold-airDamming
AuniqueinteractionexistsbetweentheAppalachianMountains,theAtlantic
Ocean,andatmosphericcirculationatbothmesoscaleandsynopticscales(Keeteret
al.,1995).WhenananticyclonepropagatesovertheNortheastregionoftheUnited
StatesorEasternCanada,coldandstableairatthesurfaceadvectstowardthe
southwestandisdammedalongeasternAppalachianMountainslopes(Stauffer&
Warner,1987;Keeteretal.,1995);ageostrophicnortherlyflowfunnelsmoreair
southwardintothedammingregionthroughcoldairadvection,thusstrengthening
thewedge(Stauffer&Warner,1987)(Figure2.1).
Figure2.1.AtmosphericflowduringAppalachiancold-airdammingadaptedfromBaileyetal.(2003).
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AnearlygeostrophicbalancebetweenCoriolisandpressuregradientforcewithin
thecolddome,aidedbyfrictionatthesurface,directsflownormaltothe
Appalachians(Xu,1990)anddeepensthespatialextentofthedammingregion(Bell
&Bosart,1988).Thehighestpressurewithinawedgeresultswheresurfaceairisat
itscoolestanddeepest,whereinliesacorrelationtotheevent’sinversionheights
(Stauffer&Warner,1987).Overrunningwarmairaloftreinforcesasubsidence
inversionthatremainsfortheentiretyoftheevent’slifecycle(Bell&Bosart,1988),
whichisfortifiedbyevaporativecoolingandadiabaticcoolingthatcausesair
alongsidethemountainbarriertocoolanddescend(Stauffer&Warner,1987;Koch,
2001).Thecoldsurfaceconditionsrelativetooutsidethewedgeareenhancedby
theblockingofinsolationfromcloudsformingwithintheupperportionofthe
inversion(Stanton,2003).
2.2ClimatologyofCold-airDamming
CADisaprevalentyear-roundmesoscalephenomenonthatoccurseastofthe
Appalachiansonaverageforover50daysayear(Rackley&Knox,2016)with
roughly3to5strongdammingscenarioseachcoldseason(Bell&Bosart,1988).As
RackleyandKnox(2016,p.431)state,“fiftydaysperyearisnontrivialfora
phenomenonwiththepotentialtosignificantlyimpactforecastsofsensible
weather.”StaufferandWarner(1987)founddammingscenariostolasttypically
around30hours,whileBellandBosart(1988)foundweakereventstolastabouta
day.Baileyetal.(2003)identifiedtheseCADcasesusingadetectionalgorithm
basedonwedgecharacteristics,whileEllisetal.(2017)morerecentlyusedthe
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spatialdistributionofsynopticweathertypesovercentralAppalachiatoidentify
daysinwhichCADwaspresenthistorically.
Aneventisconsideredstrongifitpersistsforover36hourswithinthe
recordofsurfaceobservations(Stanton,2003).Althoughthephenomenonoccurs
year-round,CADisstrongestandmostprevalentinthewintermonths(Stauffer&
Warner,1987;Bell&Bosart,1988;Rackley&Knox,2016).Strongspringand
summerCADeventscanalsooccur,potentiallyprovidingatriggerforconvection
alongthewedgeboundaryduringerosion(Rackley&Knox,2016).
2.3ClassificationsofCold-airDamming
CADvariesbysize,intensity,andduration,andcanbeclassifiedintothree
broadcategoriesbasedonformationmechanisms:classic,hybrid,andin-situ
(Baileyetal.,2003).AlthoughBaileyetal.(2003)developedanexpansive
classificationscheme,thisresearchusesasimplifiedspectrumofdammingtypes
thatismoreintuitivetooperationalforecasters.Theprimaryfactorsinclassification
arethenatureoftheparenthigh-pressuresystemasareflectionofsynopticforcing
anddiabaticprocessesintermsofprecipitationenhancement(Baileyetal.,2003;
Stanton,2003).Themostcommon,andtypicallystrongest,typeofsituationis
classicCAD,inwhichthecentralhigh-pressurecenterispositionedoverthe
NortheastoreasternCanada,withapressureofgreaterthanorequalto1030
millibars(Baileyetal.,2003).Theseeventsmustexceed24hourstorule-outthe
typicalprogressionofasurfacehigh,andalthoughprecipitationcanintensifythe
onsetofaclassicalwedge,mostarenotenhanceddiabatically(Baileyetal.,2003).
Incontrast,hybridCADinvolvesaparenthightothenortheastthatisweakerthan
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1030millibarsandisaidedbydiabaticprocessestostrengthenthecolddome
(Baileyetal.,2003).Lastly,in-situeventsareformedfromamoreeasterlyhigh-
pressurecentertowardtheAtlanticthatinduceslessersynopticforcing(Baileyet
al.,2003).Thesesituationsoftenresultfromapassingcoldairmassthatleaves
behindcoldconditions(Baileyetal.,2003),andaredependentonevaporative
coolingandcloudprocessesovertheirduration(Baileyetal.,2003;Koch,2001).
2.4SensibleWeatherAssociatedwithCold-airDamming
StrongCADeventsarecommonlyassociatedwithbelownormal
temperatures,increasedcloudcover,andprecipitation.Intheclimatologygenerated
byBaileyetal.(2003),roughly4/5ofallclassiceventsdramaticallyimpacted
sensibleweather.Evaporativecooling,fueledbycloudsandprecipitation,is
significantintermsofstrengtheningaCADevent.Stabilityofthesurfacelayer,
wedgestrength,andmountainblockingallincreasewithevaporativeprocesses
(Stanton,2003),whichcancontributetonearlyone-thirdofthecoolingthatoccurs
withinthecolddome(Bell&Bosart,1988).Evaporativecoolingplaysthelargest
roleindomestrengtheningduringtheonsetofanevent,typicallywaningaslow-
levelairbecomessaturated(Stauffer&Warner,1987);however,nearlysaturated
airthatisorographicallyblockeddescendsandwarmsadiabatically,allowing
evaporativeprocessestocontinuethataidinthepersistenceofthecoldwedge(Bell
&Bosart,1988).Relativesurfacetemperaturedifferencesinsideandoutsideofthe
dammingregioncanheavilyinfluenceprecipitationtype(Stanton,2003),andasub-
freezinglayeratthesurfaceenhancedbyevaporativecoolingcanconvertliquid
precipitationintofreezingrain.CADsituationscansometimesresultinmultiple
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transitionsbetweenfrozenandliquidprecipitation(Mahoney,2006),andthe
uncertaintyinforecastingthesetypesofeventspointstoaneedforfurther
research.
2.5Cold-airDammingErosion
TheerosionofCADisnotwellunderstoodandisoftenprematurewithin
model-drivenforecasts.AccordingtoastudybyStanton(2003),theprimary
mechanismsthatcancontributetoerosionincludedifferentialthermaladvection,
solarheating,lower-tropospheredivergence,shear-inducedmixing,andthe
advancementofacoastalwarmfrontontheeasterncolddomeedge.Stanton(2003)
foundthatCADonsetmechanismsareindependentoferosiontype.After
developingsynopticcomposites,Stanton(2003)alsodeterminedcoastalcyclones,
cold-frontpassages,residualcoldpoolsaffectedbysolarheating,anda
northwesternlowthatproducesacoldfrontwhichspursshear-inducedmixingat
thetopofthewedgeboundaryaretheprimarysynopticpatternsunderwhich
erosionoccurs.Noadditionalstudieshavefocusedsolelyonthedriversthatend
CADevents,andalthoughitiswellknownthatnumericalweatherpredictionpoorly
handleserosion,theaccuracyofforecastmodelpredictionsduringthedemiseof
CADeventshasnotbeenexplicitlyexamined.
2.6VerticalSoundingsinCold-airDammingAnalyses
Verticalatmosphericsoundingsareaninvaluabletoolmeteorologistsuse
duringdailyforecasting;however,mostCADresearchlacksextensivesounding
analyseswheninvestigatingthenatureofthisevent,primarilyduetothesparse
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distributionofobservationsitesthroughoutthecountry.AsKeeteretal.(1995)
describe,
“The temporal and spatial limitations of the currentupper-air observational network, consisting ofsoundings taken only at 12-h intervals, at stationsscattered hundreds of kilometers apart, impacts theaccuracyandresolutiontowhicheventssuchascold-airdammingcanbedepictedbydynamicalmodels”(p.46).
Furthermore,small-scaleandweakeventsoftenprogressundetectedbysounding
dataresultingfromthenetwork’sspatialandtemporallimitations(Baileyetal.,
2003).InvestigatingCADwithintheBlacksburgNWSFOCWAoffersabundantupper
airdatacenteredwithinthestudyarea,highlightingthesesoundingsasavaluable
toolforstudyingtheverticalatmosphericprofilesofCADscenarios.
2.7ForecastingCold-airDamming
Overthepasthalf-century,theabilityofforecasterstopredicttheweather
hasimprovedsignificantly,yetCADremainsoneofthemostdifficultforecasting
challengeseastoftheAppalachians(Koch,2001;Bosart,2003;Rackley&Knox,
2016).Theshallownatureofthewedge,typicallycappedbyaninversionataround
the850millibarpressureleveloftheatmosphere,vastlylimitsthetoolsavailableto
forecastersduringtheseevents(Bell&Bosart,1988;Koch,2001).Surface
observations,thoughcriticalduringtheevolutionofadammingscenario,provide
spatially-limitedenvironmentalinformationtointerpret,forcingmeteorologiststo
relyontheirconceptualknowledgeofCADtoresolvetheprogressionofthecold
dome(Stauffer&Warner,1987;Koch,2001).Predictingprecipitationtypecanbe
oneofthemostdifficulttasksofforecastersduringCAD,asevaporativecoolingcan
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causequickchangesinhydrometertypethataredifficulttodetect(Forbesetal.,
1987;Keeteretal.,1995;Baileyetal.,2003).Additionally,quantitativeprecipitation
forecasting(QPF)andmaximumtemperatureforecastingarealsochallengingto
resolveasawedgepersists,evenwhenprecipitationhaslittletonoinfluence
(Keeteretal.,1995;Mahoney,2006).Inwintermonths,bothprecipitationtypeand
accumulationforecastsarecritical,astheyimpacthowemergencymanagers
preparefortheseevents(Forbesetal.,1987).Ontheotherhand,duringthewarm
season,CADwedgescaninitiateconvectiveeventsalongtheboundaryofcoldair,
resultingindifficult-to-predictsevereweather(Rackley&Knox,2016).While
operationalmeteorologistsarebecomingincreasinglydependentonweather
models(Bosart,2003),theweaknessofmodelsinforecastingthecharacteristicsof
CADstillpresentsahurdleforforecasters(Mahoney,2006).
2.8NumericalWeatherPredictionofCold-airDamming
Mesoscaleweathermodelsprovetobemoreusefulthanglobalmodelsat
resolvingthespatialandtemporalnatureofCAD(Keeteretal.,1995)duetotheir
abilitytogeneratehigherresolutionsolutions(Koch,2001);however,mesoscale
modelsstilltendtounderestimatewedgedurationandimpactsdespitemodern
computingpower(Rackley&Knox,2016)andadvancementsinbothhorizontaland
verticalresolutions(Bell&Bosart,1988;Koch,2001).Small-scaleandweakCAD
setupscanoftenprogressundetectedevenbymesoscaleweathermodels(Baileyet
al.,2003).Thetendencyofmodelstoprematurelyerodethecolddomeoftenresults
inawarmbias,whereasover-predictedprecipitationduringonsetcanresultina
coldbias(Baileyetal.,2003).Diabatically-enhancedeventsarenotaccurately
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handledwhenonsetisnotprimarilydrivenbyprecipitation(Baileyetal.,2003),
andtheparameterizationofheavyprecipitationandorganizedconvectionare
commonsourcesofforecasterrorforweatherpredictionmodels(Baileyetal.,
2003;Mahoney,2006).Boundarylayerdynamics,windforecasts,andsurface
pressureallexhibitsubstantialmodelerrorwithinthewedge,andincreasedvertical
resolutionisnecessarytobetterresolvetheseparameters(Stauffer&Warner,
1987).Forecastuncertaintyincreaseswhennumericalweatherpredictionmodels
showdisagreementthroughdifferentsolutionsforevents,whichcommonlyoccurs
duringCAD.
ParameterizingtheerosionofCADistypicallyviewedasthemostdifficult
aspectofforecastingthisphenomenon.Modelsareunreliableduringthedemiseof
CADeventsandtendtoprematurelyerodethewedge(Koch,2001;Stanton,2003).
Smoothingofterrainandinadequateverticalresolutioncontributetopoor
modelingthatcausesfailuresduringerosionforecasts(Stauffer&Warner,1987;Xu,
1990;Koch,2001).Modelsalsoexhibitanoverabundanceofsurfaceheatingdueto
problematicinterpretationofinteractionsbetweensolarradiationandshallow
cloudcover,andwhencombinedwithprematureadvancementofcoldairadvection
aloft,lowtemperatures,precipitation,andcloudcoverareoftenprematurely
terminated(Stanton,2003).
2.9ModelOutputStatisticsForecastsofCold-airDamming
AccordingtoGlahn&Lowry(1972),
“Model Output Statistics is an objective weatherforecastingtechniquewhichconsisistsofdeterminingastatistical relationship between a predictand andvariables forecast by a numerical model… The MOS
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technique is, in effect, the determination of ‘weather-related’statisticsofanumericalmodel”(2018).
ModelOutputStatistics(MOS)representthelocation-specificnumericaloutput
fromrawnumericalforecastmodeloutputincludingtheGFSandNAM(Glahn&
Lowry,1972).MOSisdrivenbystatistical-dynamicalmultiplelinearregression
analysis(Glahn&Lowry,1972;NOAA–NationalWeatherService,2018)thatuses
variablesfromrawmodeloutputincludingtemperatureatthesurface,saturation
defecit,and500mbheight(Glahn&Lowry,1972)toyieldpredictionsofminimum
andmaximumtemperaturesevery12hoursintheformofstatisticalguidance.MOS
alsoestimatessurfaceparameterslike3-hourlytemperatures,quantitative
precipitationforecasts,cloudcover,andmanymore(Glahn&Lowry,1972;Bell&
Bosart,1988;Carteretal.,1989).Eachsite-specificforecasthasauniqueequation
todetermineMOSminimumandmaximumforecastguidanceinordertoreduce
biasacrosssites(Glahn&Lowry,1972).Itsdiagnosticequationsaremodified
seasonallybasedonpost-accuracyassessmentofallpreviousseasons(Carteretal.,
1989;Keeteretal.,1995),resultinginMOStemperatureandprecipitationforecasts
thatrivalthoseofexperiencedmeteorologists(Bosart,2003).However,numerical
predictionmodelaccuracydirectlyaffectsthesuccessofMOSguidance(Carteretal.
1989).WhileMOSgenerallycorrectsformodelbiases,anaccurateassessmentof
sensibleweathervariablesfromparentmodelsisnecessaryforMOStoproduce
successfulstatisticalguidance(Carteretal.1989).
DuringCADsituations,MOStemperatureforecastscommonlyinitially
predicttemperaturesthataretoohighwithinthedammingregionandtoolow
towardthecoastoutsideofthewedge(Forbesetal.,1987;Bell&Bosart,1988).
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Historically,suchsystematicerrorsareoftenovercorrectedinthenextforecast
cycleandMOSpredictsconditionsmuchtoocoldwithinthewedgethenextday
(Forbesetal.,1987).Insufficientmodelingofverticalresolutionandphysical
processesbyparentmodelsrenderMOSguidanceoflittlevalueduringcolddome
forecasting(Forbesetal.,1987;Bell&Bosart,1988),whereasthistoolstandsoutin
modernforecastsofmostotherweathersituationsasbeinghighlyvaluable.
2.10Summary
AppalachianCADisasynopticallydrivenphenomenonaffectedbylocal-scale
physicalprocessesthatcontributetothecomplexityofthisweatherevent.When
wedgedintotheBlueRidge,CADcancausesocietalissues,particularlyinwinter
monthswhenemergencymanagementdependsonCADforecaststosupportpublic
safety.CADisnotoriouslyproblematicforforecasters,especiallygiventhedifficulty
withwhichmodelsrepresentthecolddomeandassociatedcharacteristics.Nostudy
hasbeenconductedwherebymodeloutputduringpoorlyforecastCADeventshas
beenanalyzed,andthisresearchseekstoassesspotentialreasonsastowhybiases
intheGFS-basedMOS(MAV)andNAM-basedMOS(MET)outputmayskew
operationalmeteorologists’forecastsduringparticularlyproblematicinstancesof
thisweatherevent.
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Chapter3.DataandMethods3.1NationalWeatherServiceCold-airDammingBustDatabase(2007-2016)
Since2007,forecastersattheNWSFOinBlacksburg,Virginiahavearchived
CADeventsthatresultedinwhattheyconsidertobe‘busted’(inforecaster
vernacular)maximumorminimumtemperatureforecasts–thoseconsistingof
errorsof8°F(4.4°C)orhigher–inaninternaldatabaseinattemptstocapture
synopticallydrivencasesofthisphenomenon(RobertStonefield–NWSFO
Blacksburg,Personalcommunication,September2017).Eacharchivedeventspans
12hours,capturingobservedminimumtemperaturesbetween00Zand12Zor
maximumtemperaturesoccurringbetween12Zand00Z,dependingontheforecast
timeperiod.Eacharchivedeventincludesthedateandlocation(s)oftheerroneous
forecastfromamongthesixterminalaerodromeforecast(TAF)siteswithinthe
CWAforwhichMOSguidancedataareavailable(Blacksburg,VA;Roanoke,VA;
Lynchburg,VA;Danville,VA;Bluefield,WV;andLewisburg,WV)(SeeFigure3.1).
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Figure3.1.TheBlacksburgNWSFOcountywarningarea(CWA;greenshading)andthesixTAFsitesforwhichtemperatureforecastsareanalyzed.Ineachinstance,theofficialNWSforecastwasatleast8°F(4.4°C)warmerorcooler
thantheobservedtemperature,signalingthattheseCADcaseswereparticularly
challenging.ItisthecasethatMOSforecastsmayhaveproventobemoreaccurate
thanNWSforecasts,butitismoretypicalthattheyarelessaccurate.Foreachsiteat
whichthetemperatureforecast‘busted’duringthe12-hourforecastperiod,the
databasecontainsnumericalforecastsfromboththeNAM-MOS(MET)andGFS-MOS
(MAV)andBlacksburgNWSOfficeofficialforecasts12to60hoursinadvance,
stratifiedintofive12-hourforecastcycles.Areaforecastdiscussionsandforecaster
commentsarealsoincludedinthedatabaseforeachevent.Thedatabasecontains
225casesspanning10yearsbetweenJanuary2007andDecember2016,segregated
bythetypeofdammingevent(classic,hybrid,andin-situ)and/orwhethertheerror
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occurredattheonsetorerosionoftheevent.Therearelikelymanymore
problematicCADeventsthatimpactedthecentralAppalachianMountainsthan
recordedinthisdatabase;thesearchivedcaseswereparticularlydifficultfor
forecasters,buttherearemanymoreCADeventsthatoccurredduringthistime
periodthatwereeitherwell-forecastedorwerebelowthe8°Fbustthreshold.An
emphasisonforecastingtheseeventsusingMOSguidancemakesthisofficialNWS
bustdatabaseanidealstartingpointforthisresearch.
TheCADbustedforecastdatabaseprovidedbytheNWSoffersanarrayof
potentialeventstoanalyzethatproveddifficultforforecasterstoaccuratelypredict.
Eacheventhasbeenvisuallyverifiedbythisinvestigatorandre-classifiedusingthe
WeatherPredictionCenter’sSurfaceAnalysisArchive
(http://wpc.ncep.noaa.gov/archives/web_pages/sfc/sfc_archive.php).Criteriafor
verifyingeachcaseincludedaclearlydefinedpressure‘wedge’,easterlyor
northeasterlysurfacewindsovertheBlueRidgeregionofVirginia,relativelylowair
anddewpointtemperaturescomparedtosurroundingareas,andcloudcoveror
precipitationovertheCWA.Thestrongesteventswerethosere-classifiedasclassic,
exhibitingaparenthighpressureanticycloneofgreaterthan1030millibars.Hybrid
CADcasesexhibitedsimilarcharacteristicsbutwithcentralpressureslessthan
1030millibarsfortheanticyclonegenerallypositionedslightlytothesouthoverthe
NortheastUnitedStates.In-situcaseswerelesswell-definedintermsofsurface
pressurewedgingandshowedevidenceofdiabaticenhancement.Thesecaseswere
typicallysituatedmoreeasterlytowardstheMid-Atlanticcoastwithmore
southeasterlywinds.Alackofaclearly-definedwedgeorthedisplacementof
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synoptichighpressurepassingdirectlyoverthearearatherthantothenorthas
withatypicalwedgeeventcaused110erroneousforecasteventstobediscarded
fromthefinaldatabase.Manyofthesediscardedeventsthatappearedtobeclassic
orhybridweredismissedas“backdoorfronts”–coldfrontspassingthroughthe
areafromthenorth/northeast–thatgavethefalseappearanceofaCADdome.The
5archivedcaseswithmissingspatialandnumericalforecastdatawerealso
discardedfromthisstudy.
Oncethisinvestigatorvisuallyverifiedtheseevents,forecastersatthe
NationalWeatherServicealsovisuallyverifiedthecaseschosenfromthebust
databasetoreducethesubjectivityofthesescenarios.Usingtheirexpertise,the
datasetwasfurthernarrowedto110eventstouseforthefinalbiasandcomposite
analyses.
Lastly,forecastercommentsintheNWSFOBlacksburgCADbustdatabase
werereviewedandarediscussedtosetthetonefortheunderlyingcomplexityof
thesecases.
3.2MOSGuidanceArchivedDataRetrieval
Ofthe110casesofCADcharacterizedbypoortemperatureforecasts,13CAD
bustsweremissingMAVandMETguidanceforecastsfromtheofficialNWSbust
databaseacrossallforecastcycles.ThesemissingdatawereobtainedusingtheIowa
StateUniversityEnvironmentalMesonetNWSMOSDownloadInterface
(https://mesonet.agron.iastate.edu/mos/fe.phtml)andwereintegratedintothe
bustdatabasewhenperformingbiascalculationstoensurenocasesweremissing
anyforecastdata.
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3.3AbbreviatedClimatologyCompilation
UsingthefinallistofbustedCADevents,anabbreviated(10-year)
climatologywascompiledforthecases.Thesedataarecharacterizedintermsof
frequencyperyearandmonth,maximumvs.minimumtemperatureforecasts,and
spatiallocationbasedontheTAFsitesusedinthebustdatabase.
3.4AssessingMAVandMETGuidanceBias
ModelbiasstatisticswerecalculatedforeachclassificationusingFortran
programming.Forproblematicmaximumandminimumairtemperatureforecasts,
meanerror,absolutemeanerror,andbias(forecasttemperaturedividedby
observedtemperature)werecomputedtocharacterizethenatureoftheerrorin
MAVandMETtemperatureforecasts.BoxandwhiskerplotsgeneratedinMicrosoft
ExcelareusedtographicallyrepresentanybiasesforeachmodelwithintheCAD
classifications.Atwo-sampet-testusingdatafromMETandMAVforecasts
throughoutallbustedCADcaseswasrunforbothmaximumandminimum
temperatureforecaststotestwhetheronemodelproducedsignificantlymore
accurateforecaststhantheother.Theabovestatisticswerecomputedforeachof
thesixTAFsitestoaccountforthespatialdistributionofforecasterrors,aslongas
therewereenoughdatapointsateachTAFsitetoproducemeaningfulstatistics.
3.5AssessingModelBiasesOverTime
AnnualboxplotsofbothminimumandmaximumtemperatureMAVandMET
biaseswereplottedthroughtimeinMicrosoftExceltoexaminehowMAVandMET
guidancebiaseschangedthroughthe10-yearperiod.Thepurposeistoascertain
whetherseasonallyupdatedMOSguidanceparametershadasystematiceffecton
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modelimprovementthroughtime.AnnualmedianforecasterrorforMAVandMET
wasplottedinExceltoassessguidanceerrorcovariance.Thiswasdoneforeachof
thefivemaximumtemperatureforecastcycles(-12hoursto-60hours).Any
significantdifferencesbetweenMAVandMETforecasterrorwerecross-referenced
withNCEPCentralOperations’ChangestoNCEPModels/ImplementationDatesto
NOAAPORTDatabase(http://www.nco.ncep.noaa.gov/pmb/changes/),which
providesarecordofMAVandMETguidanceequationupdates.
3.6HourlySurfaceComposites
Hourlymeteorologicalsurfacevariablesincludingairtemperature,relative
humidity,winddirection,andcloudceilingheightsspanningbetween0Zand23Z
foreachoftheproblematicCADforecastdatesatallsixTAFsitesweregathered
fromtheNationalCentersforEnvironmentalInformation(NCEI)ClimateData
Online(https://www7.ncdc.noaa.gov/CDO/cdoselect.cmd).Thesedatawerethen
usedtogeneratehourlycompositesofatmosphericvariablesduringbustedCAD
forecasteventsateachTAFsite.Further,dailydataweredownloadedforalldays
overthe10-yearstudyperiodfortheBlacksburgTAFsitetoconstructperiodmeans
ofhourlydataforthecoldseason(NovembertoApril)andwarmseason(Mayto
October)atBlacksburg.ThecompositeswerecomputedusingFortranandplotted
inMicrosoftExcel.
3.7SynopticComposites
High-resolution(32km,or0.3degreeslatitude/longitude)synoptic
atmosphericcompositesweregeneratedfromthedatesoftheerroneousforecasts
withineachCADclassificationtocharacterizethedominantlarge-scaleatmospheric
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circulationassociatedwiththeseevents.CompositessegregatedbyCAD
classificationwerecreatedfromtheDailyAverageNCEPNARRComposites
(https://www.esrl.noaa.gov/psd/cgi-bin/data/narr/plotday.pl)availablethrough
NOAA’sEarthSystemResearchLaboratory.Eachcompositerepresentsmean
synopticatmosphericconditionsondayswithintheNWSdatabaseofbusted
forecastsofCAD.Thecompositesdisplaysealevelpressureandgeopotential
heightsat925hectopascals(hPa),850hPa,and500hPa;airtemperatureatthe
surface,925hPa,850hPa,and500hPa;vectorwindsat925hPa,850hPa,and500
hPa;and2-meterdewpointsandrelativehumidityvalues.Thespatialdomainofthe
compositesincludetheeasterntheextentoftheUnitedStatescenteredabovethe
centralAppalachianstudyregion.Thesynopticcompositesforbustedeventsare
comparedtosynopticcompositesavailablewithinthepublishedworkofBelland
Bosart(1988),Baileyetal.(2003),andEllisetal.(2017)toidentifyanymajor
differencesthaymightlendtoforecastingdifficulties.Similarly,composites
displayingsurfaceconditionsonetothreedaysinadvanceofeachCADclassification
werealsocreated,buttheyarenotincludedwithinthisdocument.
Additionalsurfacecompositesofcoarserresolution(2.5degrees
latitude/longitude)showingpressure,airtemperature,andvectorwinddifferences
ofeachclassificationbasedonclassicCADscenarioswereprovidedbythe
NOAA/ESRLPhysicalSciencesDivision,BoulderColoradofromtheirDailyMean
Compositespage(https://www.esrl.noaa.gov/psd/data/composites/day/).The
spatialdomainofthesecompositesisconsistentwiththeNARRdailycomposites.
3.8UpperAirSoundingComposites
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VerticalatmosphericprofiledataforeachoftheCADforecastperiods
examinedinthisstudywereretrievedfromtheUniversityofWyoming’sSoundings
Archive(http://weather.uwyo.edu/upperair/sounding.html)fortheUpperAir
stationlocatedinBlacksburg,VA(KRNK).Thesedatawereusedtocreateasimple
meancompositefromtheproblematicforecastperiodsforeachCADclassificationin
boththewarmandcoldseasonusingFortranprogrammingandMicrosoftExcel.
Atmosphericsoundingcompositescharacterizethemeanconditionofthe
atmospherethroughaverticalcross-section,providinginsightintothevertical
profileoftheatmosphereassociatedwithbustedforecasts.
3.9ClassicCold-airDammingClimatology
Ellisetal.(2017)identifiedthepresenceofCADeventsinthecentral
AppalachianMountainsusingaspatialsynopticclassification(SSC)approach.
Identifyingdaysinwhichmoistpolar(MP)airwasinplacetotheeastofthe
mountainsandnon-MPairtothewestproducedacold-seasonclimatologyof219
classicCADeventsintheregionovera35-yearperiod.Presumably,CADdayswithin
thedatabaseofEllisetal.thatdonotappearwithintheNWSdatabaseof
problematicCADdayswerewell-forecastwedgeevents.Acomparisonofvertical
atmosphericcompositesfromthetwodatabasesmayhelptorevealsourcesof
forecastdifficultyassociatedwiththebustedCADforecasts.
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Chapter4.ResultsandDiscussion
4.1Reviewof‘Bust’DatabaseForecasterComments
AsforecastersattheBlacksburgNWSFOdevelopedthearchiveofbusted
CADforecasts,theyoftenaddedpersonalcommentsandincludedreasonsasto
whatmayhavecausedeachindividualforecasttobust.Thesereasonsgenerally
includeskycover,precipitation,andlow-levelmixing,amongothers.Forthe
problematicforecastsarchivedinthebustdatabase,nearlyallofthem(72of110)
werefurthercomplicatedbyskycoverandalargenumber(46of110)by
precipitation.Only10ofthe110caseshadthesolereasonthataCADsetup
impactedforecasterror.Thesechallengingscenariosalonearedifficulttoaccurately
forecast,andthehighfrequencyofcomplicatingfactorsimplythesecasesmaybe
morecomplexthantypicalCADscenariosfortheregion.
4.2ClimatologyofCold-airDamming‘Busts’ DuetothelimitedtemporalrangeoftheNWSFO-Blacksburg’sdatabaseof
problematicCADforecasts,anabbreviated(10-year)descriptiveclimatologyofCAD
forecastbustsisconstructed.Atotalof110‘busted,’orunsuccessfullyforecast,CAD
eventsarestratifiedintothefollowingclassificationsbasedonsynoptic
characteristics:15classic,18hybrid,12in-situ,18onset,and47erosion(Table4.1).
Erroneousmaximumairtemperaturesforecastat0ZUTC(7or8pmlocal)forthe
subsequent12Zto0Zperiodarereferencedhereas“Hi,”astemperatureforecasts
withinthisperiodtypicallyreflectdailymaximumvalues.Erroneousminimumair
temperatureforecastsat12Z(7or8amlocal)forthesubsequent0Zto12Zperiod
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inwhichtemperatureforecastsinthisperiodtypicallyreflectdailyminimumvalues
arereferencedas“Lo.”
Table4.1.Occurrencesofproblematiccold-airdammingforecastsbyclassificationtype.Datesarelistedasmonth/day/year(last2digits).Theerroneoustemperatureforecastisindicatedparentheticallyaseithermaximum(Hi)orminimum(Lo)dailytemperature.ClassificationsClassic Hybrid In-situ Onset Erosion2/28/15(Hi)12/8/14(Hi)3/18/13(Hi)9/18/11(Hi)4/22/11(Hi)11/29/10(Hi)1/27/09(Hi)4/6/08(Hi)3/30/08(Hi)12/21/07(Hi)4/23/13(Lo)11/19/12(Lo)11/18/12(Lo)9/17/11(Lo)11/19/09(Lo)
10/6/16(Hi)8/4/16(Hi)4/30/16(Hi)2/2/16(Hi)6/4/15(Hi)4/19/15(Hi)9/8/14(Hi)5/5/13(Hi)5/4/13(Hi)11/6/12(Hi)4/18/12(Hi)10/12/09(Hi)9/11/10(Hi)8/14/10(Hi)8/1/10(Hi)4/10/15(Lo)10/13/11(Lo)8/22/08(Lo)8/21/08(Lo)
6/29/14(Hi)7/31/13(Hi)4/15/13(Hi)7/16/11(Hi)3/15/11(Hi)8/2/10(Hi)5/11/10(Hi)4/13/09(Hi)12/28/07(Hi)2/25/07(Hi)5/11/10(Lo)4/11/07(Lo)
8/8/16(Hi)2/22/16(Hi)3/24/15(Hi)3/23/15(Hi)10/11/14(Hi)9/23/14(Hi)3/16/14(Hi)4/4/13(Hi)9/6/11(Hi)3/14/11(Hi)2/22/11(Hi)9/26/10(Hi)7/31/10(Hi)3/19/15(Lo)12/21/14(Lo)7/9/09(Lo)11/29/08(Lo)3/21/07(Lo)
12/12/16(Hi)3/12/16(Hi)2/24/16(Hi)12/29/15(Hi)12/1/15(Hi)3/25/15(Hi)3/20/15(Hi)3/3/15(Hi)12/24/14(Hi)8/10/14(Hi)5/7/14(Hi)4/30/14(Hi)4/7/14(Hi)3/19/14(Hi)2/23/13(Hi)12/20/12(Hi)7/13/12(Hi)1/23/12(Hi)12/27/11(Hi)5/13/10(Hi)4/14/10(Hi)1/24/10(Hi)12/13/09(Hi)12/9/09(Hi)11/19/09(Hi)3/10/09(Hi)1/28/09(Hi)12/9/08(Hi)5/11/08(Hi)3/31/08(Hi)2/17/08(Hi)2/12/08(Hi)2/1/08(Hi)12/23/07(Hi)4/11/07(Hi)3/21/07(Hi)3/28/16(Lo)11/10/15(Lo)3/19/13(Lo)1/12/13(Lo)4/23/11(Lo)5/12/10(Lo)3/11/09(Lo)
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4/4/08(Lo)2/13/08(Lo)10/27/07(Lo)4/12/07(Lo)
TheCADforecastbustsareratherevenlydistributedacrossthe
classifications,withtheexceptionoferredforecastsassociatedwithCADerosion.
Thescenarioofin-situaccountsforthelowestproportionat11%,whereaserosion
makesupthemostat43%.Theproportionallyhighnumberoferredforecasts
duringCADerosionisconsistentwiththeconventionthaterosionprocessespresent
thelargestforecastingchallengeduringthisphenomenon,aserosionmechanisms
arenotfullyunderstoodandmodelstraditionallyendthewedgeprematurely
(Stanton,2003).
4.2.1AnnualDistribution
The110bustedCADforecastsarefairlyevenlydistributedacrossthe10
yearsofthedatabase(2007-2016)(Figure4.1);however,theproportionsofeach
classificationtypearehighlyvariablefromyear-to-year(Figure4.2).
Figure4.1.Theannualoccurrenceofbustedcold-airdammingforecasts,2007to2016.
1014
1113 12
711
13 1310
0
5
10
15
2007200820092010201120122013201420152016
Frequency
Year
AnnualOccurrenceofBustedCold-airDammingForecasts
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Figure4.2.Theannualfrequencyofbustedcold-airdammingforecastsstratifiedbyclassificationtype,2007to2016.
ForecastbustsassociatedwithCADerosioneventswerefrequentin2008and2009,
appearmuchlessfrequentlythereafter,andincreaseagainin2014and2015.This
likelyspeakstothefrequencywithwhichaparticulartypeofdifficult-to-forecast
formofCADerosionoccurredintheseyears.Additionally,forecastbustsassociated
withCADonsetbecameincreasinglyprevalenttowardtheendofthestudyperiod
withapeakin2014.Overall,theredoesnotappeartobeasignificanttrendinthe
distributionofbustedCADforecastsbyclassificationoverthe10-yearstudyperiod.
Thissuggeststhattherehasnotbeenasystematicchangeintheforecastdifficulties
presentedbythedifferentclassesofCADanditsonsetanddemise.
4.2.2MonthlyDistribution
ThedistributionofbustedCADforecastsbymonthsuggeststhatMarchCAD
eventspresentaparticularforecastchallenge(Figure4.3);however,asCAD
climatologicallyoccursmoreoftenduringthemonthofMarch(Bell&Bosart,1988),
0
2
4
6
8
10
12
14
20072008 20092010201120122013201420152016
Frequency
Year
AnnualFrequencyofBustedCold-airDammingForecasts
Erosion
Onset
In-situ
Hybrid
Classic
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thiscouldsimplybeareflectionofthefrequencywithwhichCADoccurs.Juneisthe
monthwiththefewestbustedCADforecastsoverthe10-yearperiodofthedatabase
(Figure6.1.1),butJuneisalsocharacterizedbyrelativelyfewCADevents
climatologically(Bell&Bosart,1988).
Figure4.3.Thefrequencyofbustedcold-airdammingforecastsbymonth,2007to2016.
ThedistributionofbustedCADforecastssegregatedbyclassificationishighly
variablebymonth(Figure4.4),muchlikethatfortheannualdistribution(Figure
4.1).BustedCADforecastsassociatedwitherosioneventsoccurmostlyinMarch
andDecember,whilethosefortheotherCADclassificationsarefairlyconsistent
throughoutthecoldseasonanddwindleduringsummer.
5
11
20 18
82 5
8 6 58
14
0
5
10
15
20
25
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Frequency
Month
MonthlyOccurrenceofCold-airDammingBustedForecasts
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Figure4.4.Themonthlyfrequencyofbustedcold-airdammingforecastsstratifiedbytype,2007to2016.
TheonlytrendinmonthlyoccurrencesofbustedCADforecastssegregatedby
classificationstemsfromerosioncasesthatoccurpredominantlyinthecoldseason
(NovembertoApril),whichmaybeaproductofenhancedwarmairadvectionand
mixingovertheshallowsurfacelayerofcold,stableair.Themajorityofall
problematicCADcasesarefoundinthecoldseason(NovembertoApril),rather
thanthewarmseason(MaytoOctober);thismaybeattributedtostrongerseasonal
pressureandtemperaturegradientsthatamplifypotentialerrorsinmodeland
forecasterjudgement,orsimplythatCADoccursmoreofteninwintermonths.
WarmseasonCADbustsaccountfor37ofthe110cases,while73occurredinthe
coldseason.
4.2.3Maximumvs.MinimumTemperatures
AmajorityofthebustedCADforecastsoccurredduringdaytimehours
(maximumtemperature),orbetween12Zand0Z,accountingfor83ofthe110cases
0
5
10
15
20
25
Jan FebMarAprMayJun Jul Aug Sep Oct NovDec
Frequency
Month
MonthlyFrequencyofCold-airDammingForecastBustsbyClassi^ication
Erosion
Onset
In-situ
Hybrid
Classic
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(75%).Farfewer(27)occurredatnight(minimumtemperature),orbetween0Z
and12ZUTC(Figure4.5).
Figure4.5.Thedaytimeandnighttimefrequencyofbustedcold-airdammingforecastsstratifiedbyclassification,2007to2016.
ThreeoutofeveryfourbustedCADforecastsweredaytimehightemperature
forecasts,andthisisconsistentacrossallCADclassifications.Thissuggeststhat
dailymaximumairtemperaturepresentsaconsistentlygreaterforecastchallenge.
FrequentmaximumtemperatureerrorsduringCADerosionmaybeenhanced
duringdaytimeerosionifawedgescenarioerodesbutisforecasttopersist,causing
temperaturestoriserapidlyandamplifyingforecasterror.
4.2.4SpatialDistribution
AmajorityofthebustedCADforecastswereforonlyoneofthesixTAFsites
intheBlacksburgCWA,withveryfewinstancesofpoorforecastsatmorethanthree
oftheTAFsitessimultaneouslyduringthe12-hourforecastperiod(Figure4.6).
05101520253035404550
Classic Hybrid In-situ Onset Erosion
Frequency
Classi^ication
DaytimeandNighttimeFrequencyofBustedCold-airDammingForecasts
Night/Lo
Day/Hi
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Figure4.6.Thefrequencywithwhichabustedcold-airdammingforecastwasevidentatasingleTAFsiteoratmultipleTAFsitessimultaneously,2007to2016.
Ofthe110totalbustedCADforecastswithinthedatabase,therewere53instances
forwhichthetemperatureforecastbustedatonlyoneofthesixTAFsites,30attwo
ofthesites,19atthreesites,6atfoursites,andtwoatfivesites(Figure4.6).No
CADcasesbustedatallsixTAFsites,whichmayindicatethaterroneousforecasts
tendtobelocalizedratherthanacrosstheentireCWA;thismayresultfrom
mesoscaleidiosyncrasiesproducedbyCADthatarefurthercomplicatedbylocal
physiography-inducedmicroclimates.Overall,bustedtemperatureforecasts
occurred42timesinLewisburg(LWB),36inBluefield(BLF),34inBlacksburg
(BCB),31inLynchburg(LYH),31inRoanoke(ROA),and30inDanville(DAN)
(Figure4.7).
02468101214161820
1 2 3 4 5
Frequency
NumberofTAFSites
FrequencyofTAFSitesperBustedForecast
Classic
Hybrid
In-Situ
Onset
Erosion
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Figure4.7.Thefrequencyofbustedcold-airdammingforecastsstratifiedbyTAFsitelocation,2007to2016.
Figure4.8.Thedistributionofbustedcold-airdammingcasesateachTAFsitesegregatedbyclassificationtype,2007to2016.
34 3630 31
42
31
051015202530354045
BCB BLF DAN LYH LWB ROA
Frequency
TAFSite
FrequencyofTotalForecastBustsateachTAFSite
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Figure4.9.Thefrequencyofproblematiccold-airdammingforecastsstratifiedbyTAFsitelocationandcold-airdammingclassification,2007to2016.
BustedCADforecastsseemtoberatherwelldistributedacrossthesixTAFsites,
despiteaproportionallyhighpercentageofclassicbustedforecastsatBluefieldand
aproportionallylownumberofhybridbustsatDanville(Figure4.8).Thismaybea
productofBLFandDANsituatedontheedgeofthecolddomeboundaryduring
thesecases,producingdifficultforecastscenarios.Thenumberofoccurrencesof
bustsassociatedwithCADerosioniscomparablefortheTAFsitesexceptfor
Lewisburg,whichexperiencesmorefrequentbusts(Figure4.9).Forecastbustsare
alsomorefrequentathigher-elevatedsiteslikeLWB,BLF,andBCB.DANandLYH
experiencethefewestnumberofmis-forecastedCADevents,bothwithrelatively
fewincidencesofbustedforecastsassociatedwithclassicandin-situCADscenarios;
terraininfluencemayaffectthesecasesless,andsincethesesitesaregenerally
someofthelasttoseethewedgeerodefromtheregion,theymayseemore
0
5
10
15
20
25
BCB BLF DAN LYH LWB ROA
Frequency
TAFSite
FrequencyofProblematicCADsbyClassi^icationateachTAFSite
Classic
Hybrid
In-Situ
Onset
Erosion
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consistentconditionscreatedbyCADthatareperhapseasiertopredict.Theredoes
notappeartobeacorrelationbetweenclassificationtypeandTAFsitedistribution.
4.3MAVandMETGuidanceTemperatureBias TheaccuracyofMAVandMETmaximumandminimumtemperature
forecastsduringCADbusteventsbetween2007and2016areevaluatedoutto60
hourslead-timetoidentifyanytemperaturebiasestheGFSandNAMmodelsmay
exhibitintheformofMOSguidancestatistics.TemperaturebiasesforeachCAD
typeregardlessofTAFsiteconsiderthemeanerror,absoluteerror,percenterror,
andbiasstatistic(forecastdividedbyactual)forMETandMAVerroneoushighand
lowtemperatureforecastsouttofiveforecastcycles(-60hours).Meanerrorisused
toassesshowtemperatureforecastsdeviatefromactualtemperatures,identifyinga
warmorcoolbiasbasedonthesignoftheerror.Absoluteerrorrevealstheaverage
departurefromobservedtemperaturesbycalculatingthemagnitudeoferrorfrom
model-drivenforecastsregardlessofthesignoftheerror.Biashighlightsprecision
withintheaveragedeparturefromobservedtemperaturesandgaugesforecast
accuracyregardlessofspatiallocationwithavalueofonerepresentinganideal
forecast(NOAA–WPCVerificationThreatScoreandBiasComputation,2013).
Thesestatistics,whencombined,willrevealanymodelbiasesthatmaybepresent
inMETandMAV-derivedtemperatureguidanceofproblematicCADeventsbetween
2007and2016.
Theproblematicforecastsinthisstudyareinfluencedbyunderlyingfactors
thatcausedforecasterstoproducepoorforecastsusingMOSguidanceinputtohelp
shapetheirforecasts.TypicalCADeventsyielddepressedmaximumtemperatures
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coupledwithelevatedminimumtemperaturesforarelativelysmalldiurnal
temperaturerangeasdiscussedintheliteraturereview.WhenaCADscenario
remainsundetectedbyMOSguidance,MAVandMETsolutionswillproducewarm-
biasedmaximumtemperatureforecastsandcool-biasedminimumtemperature
forecasts.Alternately,ifCADdoesnotoccurbutisforecasttooccurbyGFSandNAM
modelsandisreflectedinstatistically-derivedMOSoutput,MAVandMETsolutions
willbecool-biasedinmaximumtemperatureforecastsandwarm-biasedin
minimumtemperatureforecasts.Additionally,MOSguidancetendstowards
climatologyastimeincreasesaheadoftheforecastperiod,influencingMOS
temperaturebiasesacrossallforecastcycles,butmoreheavilyduringlatercycles
(Carteretal.1989).Thisoftenproduceslessaccurateforecastswithgreaterlead
time(StephenKeighton–NWSFOBlacksburg,Personalcommunication).
4.3.1TemperatureBiasResultsbyCold-airDammingClassification
MAVandMETmaximumtemperatureforecastsforproblematicclassic
(Table4.2)andin-situ(Table4.4)eventsexhibitwarmbiasesacrosshigh
temperatureforecasts.Thestrengthofthebiassignalincreasesaheadofthe
forecastperiod,asdoestheinaccuracyofmodel-derivedforecastswithgreaterlead-
time.METhightemperatureforecastsduringproblematichybridCADeventsfollow
thesametrend,whereasMAVexhibitsaconsistentwarmbiasinadvanceofthe
forecastperiodthroughoutallhybridhightemperaturebusts(Table4.3).METhigh
temperatureforecastsduringproblematicclassicCADreflectasmallpeakinwarm
biaserrorduringthesecondforecastcycle(24hoursaheadoftheforecastperiod),
producinggreaterinaccuracythandothesurroundingforecastcycles.
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Table4.2.Errorstatisticsforforecastsofproblematicclassiccold-airdammingeventsbyforecastcycle,2007to2016.StatisticsarepresentedforhighandlowtemperatureforecastsforMAVandMET.Meanerror(°F),meanabsoluteerror(°F),andbiasstatistic(unitless)arepresented.
ForecastCycle MeanError
AbsoluteError
Bias MeanError
AbsoluteError
Bias
MAV,High MET,High1(-12h),n=13 2.9 9.2 1.07 1.3 5.9 1.042(-24h),n=13 3.2 9.1 1.08 2.2 6.5 1.053(-36h),n=13 3.6 10.4 1.09 1.7 5.5 1.044(-48h),n=13 4.3 10.5 1.11 1.9 5.9 1.055(-60h),n=13 4.6 11.5 1.12 2.7 6.1 1.08
MAV,Low MET,Low1(-12h),n=9 -8.7 8.7 0.82 -7.9 7.9 0.842(-24h),n=9 -9.0 9.0 0.81 -6.0 6.0 0.873(-36h),n=9 -8.4 8.4 0.82 -6.9 6.9 0.854(-48h),n=9 -8.6 8.6 0.82 -7.8 7.8 0.845(-60h),n=9 -8.7 8.7 0.82 -8.0 8.0 0.84
Table4.3.AsinTable4.2,exceptforproblematichybridCADevents.
ForecastCycle MeanError
AbsoluteError
Bias MeanError
AbsoluteError
Bias
MAV,High MET,High1(-12h),n=24 5.8 6.4 1.11 5.5 5.8 1.112(-24h),n=24 6.2 8.1 1.12 5.8 7.4 1.113(-36h),n=24 7.5 9.0 1.14 7.4 7.9 1.144(-48h),n=24 8.5 9.4 1.16 7.0 8.5 1.145(-60h),n=24 9.6 11.1 1.17 9.9 10.0 1.18
MAV,Low MET,Low1(-12h),n=7 0.9 7.1 1.03 -0.7 6.4 1.002(-24h),n=7 1.3 7.6 1.04 0.3 8.6 1.023(-36h),n=7 0.3 9.1 1.02 0.0 7.4 1.024(-48h),n=7 -1.3 9.9 1.00 -1.0 11.0 1.015(-60h),n=7 -0.3 8.9 1.01 -1.3 9.9 1.00
Table4.4.AsinTable4.2,exceptforproblematicin-situCADeventsandonlyformaximum(high)temperature.
ForecastCycle MeanError
AbsoluteError
Bias MeanError
AbsoluteError
Bias
MAV,High MET,High1(-12h),n=20 6.3 7.6 1.11 4.7 5.7 1.082(-24h),n=20 6.0 9.6 1.11 4.3 7.3 1.073(-36h),n=20 7.5 8.8 1.13 5.3 7.2 1.094(-48h),n=20 7.0 8.0 1.12 7.8 9.1 1.145(-60h),n=20 8.3 9.7 1.14 7.9 8.3 1.14
BothMAVandMETguidanceforecastsofminimumtemperatureproduce
valuestoolowforallclassicCADforecastcycles(Table4.2),aswellasthroughout
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thesinglenighttimein-situevent(notshown).Thestrengthofthecold-biasedsignal
increasesaheadoftheforecastperiodforbothCADtypes,asdoesmodelguidance
error.Alternately,MAVlowtemperatureforecastsduringproblematichybridCAD
situationsexhibitaninitialwarmbiasthattransitionsintoacoolbiasfartherahead
oftheforecastperiodastheaccuracyofMAVlowtemperatureforecastsrather
consistentlydecreasesaslead-timeincreases(Table4.3).METlowtemperature
forecastsforhybridbustsillustrateaninconsistenttemperaturebiasthroughthe
forecastcycles,trendingfromacooltowarmbiasandbacktoacoolbiasmoving
backintimeaheadoftheforecastperiod.Inthethirdforecastcycle(36hoursahead
oftheforecastperiod),themeanMETbiasforlowtemperatureinproblematic
hybridCADcasesiszero;thiscouldresultfromthelimitedsamplesizeoflow
temperatureforecastsassociatedwithproblematichybridCADcases.
METtemperatureforecastsaremoreaccurateforbothhighandlow
temperaturesinallforecastcyclescomparedtotheMAVforclassicandin-situCAD
scenarios.Bothmodelstatisticsforecastedgreater-than-actualhightemperatures
andlesser-than-actuallowtemperaturesinthesescenarios.Whiletheaboveistrue
forhightemperatureforecastsduringproblematichybridCADevents,neitherMAV
norMETguidanceportrayasystematicwarmorcoldbiasinlowtemperature
forecasts.
Table4.5.Errorstatisticsforforecastsofallclassic,hybrid,andin-situproblematiccold-airdammingeventsbyforecastcycle,2007to2016.StatisticsarepresentedforhighandlowtemperatureforecastsforMAVandMET.Meanerror(°F),meanabsoluteerror(°F),andbiasstatistic(unitless)arepresented.
ForecastCycle MeanError
AbsoluteError
Bias MeanError
AbsoluteError
Bias
MAV,High MET,High1(-12h),n=57 5.3 7.4 1.10 4.3 5.8 1.08
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2(-24h),n=57 5.5 8.9 1.11 4.4 7.1 1.093(-36h),n=57 6.6 9.2 1.13 5.4 7.1 1.104(-48h),n=57 7.0 9.1 1.13 6.1 8.1 1.125(-60h),n=57 8.0 10.7 1.15 7.6 8.5 1.14
MAV,Low MET,Low1(-12h),n=17 -4.6 7.9 0.91 -5.0 7.4 0.902(-24h),n=17 -4.5 8.2 0.91 -3.6 7.2 0.933(-36h),n=17 -4.7 8.5 0.91 -4.0 7.1 0.924(-48h),n=17 -5.2 8.7 0.90 -4.8 8.9 0.915(-60h),n=17 -4.9 8.4 0.91 -5.0 8.5 0.91
BothMAVandMEThightemperatureforecastsforallthreeCAD
classifications(classic,hybrid,andin-situ)combinedexhibitanoverallwarmbias
thatconsistentlyincreasesfartheraheadoftheforecastperiod(Table4.5)(Figure
4.10a,4.10b);meanwhile,forecastaccuracyforbothMAVandMETdecrease
throughtheforecastcyclesaslead-timeincreases.
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Figure4.10a-b.Boxplotsof(a)MAVand(b)METmaximumtemperatureguidanceforecasterrorofallclassified(classic,hybrid,in-situ)problematicCADeventsbyforecastcycle(-12hoursto-60hours),2007to2016.Thetopsofthepurpleand
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bottomsofthegreenboxesrepresenttheupperandlowerquartilesofforecasterror,respectively,foreachperiod,andwhiskerbarsindicateminimumandmaximumdatavaluesacrosseachperiod’sdatarange.Betweenthetwoboxesliesthemedianofeachforecastcycle’serror,andmeanerrorvaluesaremarkedwithablackcircleandconnectedwithasmoothedtrendline.Alternately,MAVandMETlowtemperatureforecastsexhibitaconsistentoverall
coldbiasandaslightincreaseinaccuracyinthesecondforecastcycle(24hours
aheadoftheforecastperiod)(Table4.5)(Figure4.11a,4.11b).METforecast
temperaturesaregenerallymoreaccurateforbothhighandlowtemperaturesinall
classifiedCADforecastcycleswhencomparedtotheMAV.Bothmodelstatistics
forecastgreater-than-actualhightemperaturesandlower-than-actuallow
temperaturesinthesescenarios,suggestingtheproblematicCADcaseswererather
marginal.
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Figure4.11a-b.AsinFigure4.11a,b,exceptfor(a)MAVand(b)METminimumtemperatureguidanceerror.
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4.3.2TemperatureBiasResultsDuringCold-airDammingOnset
MAVhightemperatureforecastsforCADeventscharacterizedby
problematiconsetexhibitawarmbiasthatincreasesaheadoftheforecastperiod
(Table4.6).METhightemperatureforecastsfollowasimilarwarmbias,butthebias
isnotmonotonicthroughtheforecastcyclesasfortheMET,whichisgenerallymore
accuratethantheMAV.
Table4.6.Errorstatisticsforforecastsofcold-airdammingeventscharacterizedbyproblematiconsetbyforecastcycle,2007to2016.StatisticsarepresentedforhighandlowtemperatureforecastsforMAVandMET.Meanerror(°F),meanabsoluteerror(°F),andbiasstatistic(unitless)arepresented.
ForecastCycle MeanError
AbsoluteError
Bias MeanError
AbsoluteError
Bias
MAV,High MET,High1(-12h),n=26 3.3 8.4 1.07 3.0 7.1 1.062(-24h),n=26 3.4 9.9 1.07 2.5 8.1 1.063(-36h),n=26 4.8 11.3 1.11 3.0 10.0 1.074(-48h),n=26 5.0 11.2 1.11 3.4 9.0 1.085(-60h),n=26 5.0 11.4 1.11 4.0 9.8 1.09
MAV,Low MET,Low1(-12h),n=13 -5.4 8.2 0.86 -5.2 9.1 0.862(-24h),n=13 -6.1 8.9 0.84 -4.9 8.1 0.873(-36h),n=13 -6.8 9.7 0.82 -5.2 8.6 0.864(-48h),n=13 -6.8 9.1 0.83 -5.9 8.5 0.855(-60h),n=13 -5.5 8.2 0.86 -5.3 10.1 0.85
MAVlowtemperatureforecastsforproblematiconsetofCADeventsreveal
aninconsistentcoldbiasthatincreasesthroughthefirstthreeforecastcyclesand
lessensat60hoursaheadoftheforecastperiod(Table4.6).Thesameistruefor
METforecastaccuracy,whichexhibitsanoverallcoldbiasthatvariesindegreebut
remainsconsistentinsignthroughoutallfiveforecastcycles.TheaccuracyofMET
forecastsgenerallydecreasesaheadoftheforecastperiodasidefromrelativelypoor
forecastsforthecyclejustpriortotheforecastperiod.
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METsolutionsareoverallmoreaccurateforbothhighandlowtemperature
forecastsinallforecastcyclesduringtheonsetofproblematicCADevents.Both
MAVandMETexhibitawarmbiasinhightemperatureforecasts,whilelow
temperatureforecastsarecool-biased.
4.3.3TemperatureBiasResultsDuringCold-airDammingErosion
MAVhightemperatureforecastsduringtheerosionofproblematicCAD
eventsexhibitaweakcoldbiasthatincreasesaheadoftheforecastperiodthatis
oppositeoftheotherCADtypesdiscussedabove(Table4.7)(Figure4.12a);
however,inthefifthforecastcycle(60hoursaheadoftheforecastperiod)itis
evidentthatwarmandcoldbiasesareofequalmagnitude,astherawerrorvalueis
nearzerowhiletheabsoluteerroristhelargestforthefiveforecastcycles.MET
hightemperatureforecastsalsoexhibitacoldbiasthatincreasesaheadofthe
forecastperiod,saveforalargeerrorinthesecondforecastcyclethatmayresult
frommodelsforecastingthetimingofwedgeerosiontoolate(Table4.7)(Figure
4.12b).AslightcoolbiasinmaximumtemperatureforecastsimpliesMOSguidance
mayhaveatendencytoforecastthewedgetopersistmoreoftenthanerodingthe
wedgeprematurely.
Table4.7.Errorstatisticsforforecastsofcold-airdammingeventscharacterizedbyproblematicerosionbyforecastcycle,2007to2016.StatisticsarepresentedforhighandlowtemperatureforecastsforMAVandMET.Meanerror(°F),meanabsoluteerror(°F),andbiasstatistic(unitless)arepresented.
ForecastCycle MeanError
AbsoluteError
Bias MeanError
AbsoluteError
Bias
MAV,High MET,High1(-12h),n=74 -1.0 7.7 1.00 -1.3 6.6 1.002(-24h),n=74 -0.7 8.2 1.01 -5.1 9.7 0.903(-36h),n=74 -0.9 8.6 1.01 -3.2 8.6 0.974(-48h),n=74 -1.2 8.5 1.00 -3.1 9.0 0.975(-60h),n=73 -0.1 9.3 1.03 -5.9 10.6 0.92
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MAV,Low MET,Low1(-12h),n=17 1.8 7.1 1.08 3.9 8.8 1.152(-24h),n=17 2.6 7.2 1.11 2.8 7.8 1.123(-36h),n=17 2.4 6.9 1.09 -6.5 12.2 0.804(-48h),n=17 2.4 7.7 1.10 2.2 6.7 1.095(-60h),n=17 2.1 7.9 1.10 3.0 7.9 1.11
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Figure4.12a,b.Boxplotsof(a)MAVand(b)METmaximumtemperatureguidanceforecasterrorofproblematicCADeventsassociatedwitherosionbyforecastcycle(-12hoursto-60hours),2007to2016.Thetopsofthepurpleandbottomsofthegreenboxesrepresenttheupperandlowerquartilesofforecasterror,respectively,foreachperiod,andwhiskerbarsindicateminimumandmaximumdatavaluesacrosseachperiod’sdatarange.Betweenthetwoboxesliesthemedianofeachforecastcycle’serror,andmeanerrorvaluesaremarkedwithablackcircleandconnectedwithasmoothedtrendline. MAVlowtemperatureforecastsmaintainasomewhatconsistentwarmbias
throughtheforecastcycles,oppositeoftheotherCADcasesdiscussedabove,
increasingslightlyaheadoftheforecastperiod(Table4.7)(Figure4.13a).MAV
forecastaccuracyalsoremainsfairlyconsistentthroughtheforecastcycles,
decreasingaheadoftheforecastperiod.METlowtemperatureforecastsexhibita
warmbiasthatslowlylessensaheadoftheforecastperiodandbecomesmore
accuratewithgreaterleadtime(Table4.7)(Figure4.13b).However,at36hours
priortotheforecastperiod,thereisalargecoolbiasoutlierinMETlow
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temperatureforecasts,andthepoorestaccuracyforallMETlowtemperature
forecasts.TheslightwarmbiasexhibitedinMAVandMETlowtemperature
guidancesuggeststheNAMandGFSmaymorecommonlytendprematurelyerode
thewedgeatnight,possiblystemmingfrominadequatemodelingofovernightcloud
coverormoisturelevels.
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Figure4.13a,b.AsinFigure4.12a-b,exceptfor(a)MAVand(b)METminimumtemperatureguidanceerror. ErosionistheonlyCADclassificationwithacoldbiasforbothMETandMAV
guidanceduringhightemperatureforecastsandawarmbiasforbothMETandMAV
lowtemperatureforecasts.ThissuggeststhatmodelsusedinMOSguidancemay
haveforecastthewedgetopersistduringthedaywheninactualityiteroded
prematureofmodelpredictions;alternately,thesemodelstendtoprematurely
erodethewedgeatnightwhenitpersistsinactuality.Overall,MAVforecastsare
moreaccuratethanMETforecastsforbothhighandlowtemperatures,oppositeof
theaboveCADtypes.BothMAVandMETproducelargeanomalieswithinthe
sequenceof12-hourforecastcyclesforhighandlowtemperaturesthatare
generallylargerthanforotherclassifications,despitehavingthelargestdata
populationofanyoftheclassificationtypes.
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4.3.4StatisticalSignificance
RelativeMAVandMETforecastguidanceaccuracyisassessedusingtwo-
tailedt-testsforbothmaximumandminimumtemperatureforecaststodetermine
whethertheaccuraciesofthetwomodeloutputstatisticsaresignificantlydifferent
whenconsideringtheproblematicCADeventsbetween2007and2016(Table4.8).
Table4.8.Two-Samplet-testresultsofsignificanceforMAVandMETmaximumandminimumforecastsoverthefive12-hourforecastcyclesinadvanceofproblematiccold-airdammingevents,2007to2016.Statisticsarepresentedforbothmaximumandminimumtemperatureforecasts,irrespectiveofCADtype,forMAVandMET-basedguidancethroughallforecastcycles.Samplesize,samplemean(°F),standarddeviation(°F),standarderror(°F),andp-value(unitless)arepresented. MaximumTemperatureForecastCycle
Cycle1(-12h) Cycle2(-24h) Cycle3(-36h) Cycle4(-48h) Cycle5(-60h)MAV MET MAV MET MAV MET MAV MET MAV MET
N-size 156 156 153 153 156 156 156 156 153 153Mean 1.94 1.40 2.08 0.76 2.77 0.93 2.80 1.20 3.90 2.01StDev 8.48 7.28 9.56 8.45 9.94 9.30 10.10 10.10 10.90 9.95SE 0.68 0.58 0.77 0.68 0.79 0.74 0.81 0.81 0.88 0.80P-val 0.553 0.202 0.091 0.180 0.116 MinimumTemperatureForecastCycle
Cycle1(-12h) Cycle2(-24h) Cycle3(-36h) Cycle4(-48h) Cycle5(-60h)MAV MET MAV MET MAV MET MAV MET MAV MET
N-size 47 47 47 47 44 44 47 47 46 46Mean -2.40 -1.74 -2.28 -1.51 -3.30 -2.91 -2.77 -2.45 -2.89 -2.37StDev 7.98 9.06 8.53 8.28 8.49 7.58 8.84 8.58 9.02 9.19SE 1.2 1.3 1.2 1.2 1.3 1.1 0.71 0.69 1.3 1.4P-val 0.709 0.660 0.822 0.859 0.784Testresultsshowthatneithermodelwasstatisticallysignificantlybetterthanthe
otherthroughallfiveforecastcyclesinbothmaximumandminimumtemperature
forecastguidance.Despitealackofsignificantstatisticalfindings,operational
forecasterscanpossiblyapplythenuancedbiasesinMAVandMETmodel-derived
forecastsduringbustedCADeventstoimproveforecastingdecisionswhen
combinedwithconceptualknowledgeandmodelguidanceduringthesescenarios.
4.4SpatialMAVandMETGuidanceBias
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Temperaturebiasesateachofthesixterminalaerodromeforecast(TAF)
sitesintheBlacksburgCWAarecharacterizedusingthemeasuresmeanerror,
absoluteerror,percenterror,andthebiasstatistic(forecastdividedbyactual).
ValuesarecomputedforMETandMAVhighandlowtemperatureforecastsoutto
fiveforecastcycles(-60hours)fromtheforecastperiod.ThisanalysisofTAFsite
biasissegregatedbyCADclassification,CADonset,andCADerosion.Furthermore,a
lackofrobustsamplesizeateachsite,particularlyforlowtemperatureforecast
errorsandforecastsforCADclassificationsatDanville,doesnotallowforstatistical
testsofsignificanceandmanylowtemperatureforecaststatisticsarenotshowndue
tolimitedn-size.Thisdiscussionaimstoprovideguidancetoforecastersasa
nuancetoconsiderwhenformingCADforecastsintheCWA.
4.4.1SpatialTemperatureBiasofCold-airDammingClassifications
BlacksburgMAVandMETforecastsforproblematicCADeventsexhibita
warmbiasevidentinallhightemperatureforecastcyclespriortotheforecast
period(Table4.9)andaweakbiasinlowtemperatureforecasts(notshown),in
whichtheMETismoreaccurateacrosstheboard.
Table4.9.Errorstatisticsforforecastsofproblematiccold-airdammingeventsattheBlacksburgTAFsitebyforecastcycle,2007to2016.StatisticsarepresentedforhighandlowtemperatureforecastsforMAVandMET.Meanerror(°F),meanabsoluteerror(°F),andbiasstatistic(unitless)arepresented.
ForecastCycle MeanError
AbsoluteError
Bias MeanError
AbsoluteError
Bias
MAV,High MET,High1(-12h),n=11 7.4 8.5 1.16 6.1 6.6 1.142(-24h),n=11 9.9 10.6 1.21 8.1 8.8 1.173(-36h),n=11 9.7 11.6 1.20 8.1 8.6 1.174(-48h),n=11 10.2 11.8 1.21 9.1 9.8 1.195(-60h),n=11 12.6 13.8 1.26 10.6 11.1 1.22
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TheproblematicforecastsfortheotherTAFsites(Bluefield,Table4.10;Danville
(notshown);Lewisburg,Table4.11;Lynchburg,Table4.12;andRoanoke,Table
4.13)exhibitawarmbiasforMAVandMEThightemperaturesacrossallforecast
cycles.AcoldbiasisexhibitedacrossDanville,Lewisburg,Lynchburg,andRoanoke
minimumtemperatureforecastsinallforecastcycles,whileBluefielddidnot
experienceanylowtemperatureforecastbustsduringclassic,hybrid,andin-situ
CADevents.TheMETeitherperformsconsistentlywiththeMAVormoreaccurately
duringthesecasessegregatedbyTAFsite.
Table4.10.Errorstatisticsforforecastsofproblematiccold-airdammingeventsattheBluefieldTAFsitebyforecastcycle,2007to2016.StatisticsarepresentedforhighandlowtemperatureforecastsforMAVandMET.Meanerror(°F),meanabsoluteerror(°F),andbiasstatistic(unitless)arepresented.
ForecastCycle MeanError
AbsoluteError
Bias MeanError
AbsoluteError
Bias
MAV,High MET,High1(-12h),n=18 6.8 8.7 1.14 4.3 6.0 1.092(-24h),n=18 6.5 9.2 1.13 4.3 6.3 1.093(-36h),n=18 8.6 10.4 1.17 5.5 6.4 1.114(-48h),n=18 8.9 10.6 1.18 6.4 7.4 1.135(-60h),n=18 8.8 11.4 1.18 6.9 8.2 1.14
Table4.11.AsinTable4.10,exceptforerrorstatisticsattheLynchburgTAFsite.
ForecastCycle MeanError
AbsoluteError
Bias MeanError
AbsoluteError
Bias
MAV,High MET,High1(-12h),n=6 4.2 4.5 1.07 4.3 4.3 1.082(-24h),n=6 2.8 5.8 1.05 3.3 6.7 1.063(-36h),n=6 3.5 5.5 1.06 4.3 6.0 1.084(-48h),n=6 4.7 5.0 1.08 5.7 7.3 1.115(-60h),n=6 5.8 6.8 1.10 7.3 7.3 1.13
MAV,Low MET,Low1(-12h),n=5 -4.0 8.4 0.93 -6.4 8.0 0.892(-24h),n=5 -3.8 8.6 0.93 -4.2 8.2 0.933(-36h),n=5 -4.8 9.6 0.92 -5.0 8.2 0.914(-48h),n=5 -5.6 10.4 0.90 -6.6 10.6 0.885(-60h),n=5 -4.6 9.4 0.92 -6.6 9.8 0.89
Table4.12.AsinTable4.10,exceptforerrorstatisticsattheLewisburgTAFsite.
ForecastCycle MeanError
AbsoluteError
Bias MeanError
AbsoluteError
Bias
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MAV,High MET,High1(-12h),n=11 1.8 6.7 1.02 1.8 5.5 1.032(-24h),n=11 1.6 8.1 1.02 1.1 7.1 1.023(-36h),n=11 2.6 8.5 1.04 2.4 6.9 1.044(-48h),n=11 3.4 8.3 1.05 2.2 7.6 1.045(-60h),n=11 4.5 9.6 1.07 6.6 6.6 1.09
Table4.13.AsinTable4.10,exceptforerrorstatisticsattheRoanokeTAFsite.
ForecastCycle MeanError
AbsoluteError
Bias MeanError
AbsoluteError
Bias
MAV,High MET,High1(-12h),n=8 4.7 8.3 1.09 3.6 6.7 1.072(-24h),n=8 5.8 9.0 1.11 0.9 8.7 1.003(-36h),n=8 5.3 9.1 1.10 3.6 9.1 1.084(-48h),n=8 5.4 9.0 1.11 4.4 9.4 1.105(-60h),n=8 6.8 10.6 1.13 6.0 9.7 1.12
ThewarmbiascoincideswithMOSguidancetendenciestounderestimatethe
strengthofthecoldairwedge(Rackley&Knox,2016),possiblyresultingfromthe
parentGFSandNAMmodelsparameterizinglower-than-actualinversionheights
andunderestimatingtheroleofcold-airadvectioninstrengtheningthewedge.Both
modelslacksufficientverticalresolutiontoaccuratelyresolvesurfaceCAD
conditionsthatarenormallyconfinedto1kilometerabovetheground(NOAA–
NationalWeatherServiceForecastOfficeBlacksburg,2017,slide2),causingthese
forecaststotrendwarm.
LocatedlessthantwomilesfromtheGreenbrierRiverinWestVirginia,the
LewisburgTAFsiteattheGreenbrierValleyAirportexperiencesmorefrequentfog
thantheotherTAFsitesinthebustdatabase(AndrewLoconto–NWSFO
Blacksburg,Personalcommunication).TheproximityofLewisburgtoasourceof
moisturethatproducesfrequentfogattheobservationsitemayslowcoolingand
maintainwarmthatthesurfaceduringthenightandearlymorning,possibly
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causingobservedminimumtemperaturestoremainhigherthanMOSguidance
predictionsduringtheseproblematicCADcases.
Thewarmmaximumtemperaturebiascoupledwithacoldminimum
temperaturebiasreflectsthetendencyofMOS-derivedsolutionstooverestimate
diurnaltemperaturetrends,evidentinwarmer-than-actualhightemperature
forecastswithinthewedgeandcolder-than-actuallowtemperatureforecasts.The
GFSandNAMbehindMAVandMETguidanceinadequatelymodel24-hour
temperaturechanges,exaggeratingdiurnaldifferentialsbetween12Zand0Z
forecasts(Forbesetal.,1987);however,diurnaltrendsbetweenhighandlow
temperaturesduringAppalachianCADscenarioschangeverylittle,oftentimes
remainingwithinacoupledegreesbetweendaytimeandovernighttemperatures
(RobertStonefield–NWSFOBlacksburg,Personalcommunication).
Despitetheirproximitygeographically,thebiasesinbustedlowtemperature
forecastsforBlacksburgandRoanokeareincontrasttooneanother–Blacksburg
experiencesaconsistentwarmbiasseeninallMOStemperatureforecasts,while
lowtemperaturesforecastedforRoanokeshowacoolbias.Risingnearlyone
thousandfeet(from1175feetinRoanoketo2132feetinBlacksburg)across26
miles,therapidelevationchangemayleadMOSguidancetounderestimatecold-air
advectionandwedgestrengthacrosstheCWA.Thismayalsoleadtoundetected
CADconditionsatelevatedareaslikeBlacksburg,Bluefield,andLewisburg,
potentiallycausingMAVandMETguidancebasedonrawGFSandNAMmodel
outputtoforecasttemperaturesthataretoohighinthesehigherelevations.
4.4.2SpatialTemperatureBiasDuringCold-airDammingOnset
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Despiterathersmallsamplesizes,problematicCADforecastsatBlacksburg,
Bluefield,Danville,andRoanoke(allnotshown)exhibitawarmbiasinhigh
temperatureguidanceandcoldbiasinlowtemperatureguidance.Thedaytime
warmbiassuggestsMOS-basedGFSandNAMmodelspredicttheonsetofthewedge
toinitializeintheBlueRidgetoolate,andtheoppositegoesforthenighttimecold
bias;however,alimitedsamplesizeofthesecasesminimizestheimplicationof
thesefindings.
Lynchburgalsoexhibitsawarmmaximumtemperaturebias(notshown),yet
theTAFsite’sminimumtemperatureforecastsalsorevealaslightwarmbiasat
night.ThiswouldsuggestthecolddomeencroachingonLynchburgearlierthan
MOSpredictionsduringthedayandatnight,butthelimitedsamplesizeof
nighttimebustsatthesite(2events)isnotenoughtodrawaconclusion.
ThemostfrequentCADbustsassociatedwithonsetoccurredatLewisburg,in
whichthe6daytimeeventsresultedinacoldhightemperaturebias(notshown).
TheincidenceangleofairasacoldairmasspoolsalongsidetheAppalachian
Mountainsmayplayaroleinthissite’sconsistentcoldmaximumtemperaturebias.
Incomingcoldairadvectiondrivenbythewedge’sparentanticycloneinthe
northeastisgeneralizedinGFSandNAMparameterization,smoothingthecoldair
overmuchofthearea’scomplexterrainandresultinginMOSguidanceforecasts
thataretoocoldinLewisburgduringtheday.Dependingontheangleofincidence
ofincomingeasterlyairfromthenortheast,airmayresiduallypoolintohigher
elevationsintheCWAaftercoldsurfaceairhasbeguntopoolalongsidethe
mountainsatlowerelevations(JimHudgins–NWSFOBlacksburg,personal
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communication).Byassumingthecoldairreachesmuchoftheareaataconsistent
time,themodelsmaydepictcoldairreachingtheGreenbrierValleyquickerthanin
actuality.Aswithabove,despiteacoldminimumtemperaturebiasatLewisburg
(notshown),alimitedn-sizelimitsthevalidityoftheseinterpretations.
4.4.3SpatialTemperatureBiasDuringCold-airDammingErosion
MAVandMETforecastsforproblematicCADerosionatBluefieldand
Lewisburgexhibitacoldbiasevidentinallhightemperatureerosionforecastcycles
priortotheforecastperiod(Table4.14,Table4.15),aswellasacoldbiasinlow
temperatureforecasts(notshown).TheMETperformswithhigheraccuracyat
Bluefield,whereastheMAVprovidesbetterpredictionsduringerosionat
Lewisburg.AconsistentcoldbiasindicatesthatthecolddomeerodesfromtheBlue
RidgebeforeNAMandGFSpredictions.
Table4.14.Errorstatisticsforforecastsofproblematiccold-airdammingeventsassociatedwitherosionattheBluefieldTAFsitebyforecastcycle,2007to2016.StatisticsarepresentedforhightemperatureforecastsforMAVandMET.Meanerror(°F),meanabsoluteerror(°F),andbiasstatistic(unitless)arepresented.
ForecastCycle MeanError
AbsoluteError
Bias MeanError
AbsoluteError
Bias
MAV,High MET,High1(-12h),n=11 -6.0 7.5 0.91 -6.6 7.0 0.902(-24h),n=11 -5.9 8.1 0.91 -6.2 6.4 0.903(-36h),n=11 -6.7 8.2 0.90 -7.2 8.3 0.894(-48h),n=11 -7.6 8.5 0.88 -6.9 7.1 0.895(-60h),n=11 -7.9 9.0 0.88 -8.3 8.3 0.87
Table4.15.AsinTable4.14,exceptforerrorstatisticsattheLewisburgTAFsite.
ForecastCycle MeanError
AbsoluteError
Bias MeanError
AbsoluteError
Bias
MAV,High MET,High1(-12h),n=16 -6.2 8.1 0.90 -7.0 8.4 0.892(-24h),n=16 -6.3 7.9 0.90 -7.8 9.4 0.883(-36h),n=16 -6.3 8.4 0.90 -8.9 10.3 0.864(-48h),n=16 -7.3 9.4 0.88 -9.6 11.1 0.845(-60h),n=16 -5.6 8.9 0.92 -12.8 14.0 0.78
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Danville,Lynchburg,andRoanokerevealwarmbiasesinMAVhigh
temperatureerosionforecasts,andMETforecastsintheseeventsfailtoexhibita
consistenttrend(Tables4.16-4.19).
Table4.16.AsinTable4.14,exceptforerrorstatisticsattheDanvilleTAFsite.ForecastCycle Mean
ErrorAbsoluteError
Bias MeanError
AbsoluteError
Bias
MAV,High MET,High1(-12h),n=14 1.4 9.1 1.05 2.9 7.6 1.082(-24h),n=14 2.6 9.9 1.08 -2.4 12.6 0.963(-36h),n=14 2.5 11.2 1.08 -0.7 8.7 1.014(-48h),n=14 2.9 10.1 1.08 -0.4 10.0 1.035(-60h),n=14 3.0 11.6 1.09 -5.5 13.1 0.94
Table4.17.AsinTable4.14,exceptforerrorstatisticsattheLynchburgTAFsite.
ForecastCycle MeanError
AbsoluteError
Bias MeanError
AbsoluteError
Bias
MAV,High MET,High1(-12h),n=11 5.0 7.2 1.11 2.0 4.0 1.052(-24h),n=11 4.1 8.6 1.10 -1.7 9.4 0.983(-36h),n=11 4.7 7.8 1.11 2.0 6.4 1.054(-48h),n=11 3.2 7.9 1.08 1.8 7.1 1.065(-60h),n=11 5.7 8.6 1.13 0.4 4.4 1.03
Table4.18.AsinTable4.14,exceptforerrorstatisticsattheRoanokeTAFsite.
ForecastCycle MeanError
AbsoluteError
Bias MeanError
AbsoluteError
Bias
MAV,High MET,High1(-12h),n=12 0.9 7.6 1.04 0.6 6.4 1.032(-24h),n=12 2.9 7.6 1.07 -9.3 12.7 0.843(-36h),n=12 1.7 8.5 1.05 -0.5 10.2 1.034(-48h),n=12 1.9 8.4 1.05 1.4 10.4 1.075(-60h),n=12 4.1 10.1 1.10 1.4 8.9 1.06
Moreover,neitherMAVnorMETmaximumtemperatureforecastsforBlacksburg
duringproblematicCADportrayasystematicbias.
Table4.19.AsinTable4.14,exceptforerrorstatisticsattheBlacksburgTAFsite.ForecastCycle Mean
ErrorAbsoluteError
Bias MeanError
AbsoluteError
Bias
MAV,High MET,High1(-12h),n=10 0.4 6.2 1.04 2.4 5.2 1.082(-24h),n=10 -0.6 6.6 1.02 -7.0 10.4 0.853(-36h),n=10 -0.1 6.3 1.03 -1.3 7.5 1.014(-48h),n=10 1.0 5.8 1.05 -1.3 8.9 1.025(-60h),n=10 2.4 7.4 1.09 -7.5 13.7 0.89
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InconsistenttemperaturebiasesduringerosionindicatecomparableNAMandGFS
tendenciestoeithererodethewedgetoosoonorassumethewedgewillpersistfor
toolong.AsBlacksburgispositionedbetweenconsistentcoldbiasesatLewisburg
andBluefieldandwarmbiasesatLynchburg,Danville,andRoanoke,itmakessense
thatthisin-betweensiteexhibitsaninconclusivetemperaturebias.Thespreadof
temperaturebiasesacrosstheCWAsuggeststhatmodelstendtooverestimate
wedgedurationathigherelevations,pointingtoapotentialissueofsufficiently
modelingtopographicalaffectsonCADerosion.
Thoughnotshownduetolimitedsamplesize,minimumtemperaturebiases
duringproblematicCADerosionexhibitmoreofthesame.Lowtemperature
forecastsforBlacksburg,Lynchburg,andRoanokeweretoohigh,whereasminimum
temperatureforecastsweretoolowatBluefield,Danville,andLewisburg.These
biasesimplytheNAMandGFSerodethewedgeprematurelyatnight,though
limiteddataagainchallengesthevalidityoftheseresults.
4.5MAVandMETTemperatureBiasOverTime
Boxplotsofforecasterrorforthedocumentedmaximumtemperature
bustedforecastsofCADbetween2007and2016revealhowmaximumtemperature
biaseshavevariedovertime.Onlythefirstforecastcycle(-12hours)isdiscussed
hereforbrevityandforthepurposeofassessingwhatshouldbethemostaccurate
forecast.Furthermore,thesamplesizeforminimumtemperatureforecastsacross
eachyearwasgenerallynotlargeenoughtoperformananalysisthroughtime.High
temperatureforecasterrorsthroughthe10-yearspanofthisstudyrevealsimilar
patternsforboththeMAV(Figure4.14a)andMET(Figure4.14b)guidance,with
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METforecasterrorsappearingtobeslightlymoreconservative.Thoughthe
dispersionoferrorappearstobeslightlylargerinMAVforecasts,thetwooutput
statisticsfollowthesamewarmandcoldbiaspatternsthroughoutthestudyperiod.
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Figure4.14a-b.BoxplotsofyearlyMAV(a)andMET(b)guidancemaximumtemperatureforecasterrorsforallproblematiccold-airdammingeventsregardlessoftypeinthefirstforecastcycle(-12h)throughtheperiod2007to2016.Thetopsofthepurpleandbottomofthegreenboxesrepresenttheupperandlowerquartiles,respectively,offorecasterrorforeachperiod,andwhiskerbarsindicateminimumandmaximumdatavaluesacrosseachyear’sdatarange.Betweenthetwoboxesliesthemedianofeachyear’sforecasterror,andmeanerrorvaluesaremarkedwithablackcircleandconnectedwithasmoothedtimeseriesline.Thesamplesizeofeachyearislistedabovethex-axis. ThemedianvaluesoftheMAVandMETerrorsforthefirstforecastcycle(-
12h)foreachyearofthe10-yearperiodarehighlyco-variable(Figure4.15).The
medianoftheannualerrorvaluesareplottedratherthanstatisticallyassessedto
avoidissuesproducedbyoutlierswithintherelativelysmallpopulationsofdata.
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Figure4.15.Themedianofannualmaximumtemperatureforecasterrorvalues,MAVvs.MET,12hoursinadvanceoftheforecastperiod,2007to2016.
Basedonmedianmaximumtemperatureforecasterror,neithermodelsignificantly
outperformedtheotherthroughouttheten-yearperiod.Annualmedianvaluesof
MAVandMEThightemperatureforecasterrorforthesecondthroughfifthforecast
cycles(-24hthrough-60h)werealsoplottedandrevealthesamedegreeofco-
variability.
TakingthedifferencebetweenMAVandMET(MAVminusMET)annual
medianerrorforeachmaximumtemperatureforecastcyclerevealsanincreasefor
forecastcyclesofgreaterleadtime(Figure4.16);itisnotsurprisingthatthemodels
tendtoconvergeclosertotheforecastperiod.
-6
-4
-2
0
2
4
6
8
10
-8 -6 -4 -2 0 2 4 6 8 10
MAVMedianError(°F)
METMedianError(°F)
AnnualMedianHighTemperatureErrorValues,MAVvs.MET
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Figure4.16.Theannualmedianmaximumtemperatureerrordifferentials(MAV-MET)byforecastcycle(-12hoursto-60hoursinadvanceoftheforecastperiod)betweenMAVandMETguidanceforecasterror,2007to2016.ThefirstforecastcycleshowsthesmallestdifferencebetweenmedianerrorofMAV
andMETguidanceforecasts,illustratinghowthemodelsperformedsimilarlywith
theleastamountofleadtime(-12hours).Thoughdifferencesinmedianerror
generallyfluctuatebyseveraldegreesovertime,forecastcyclesfartheroutfromthe
forecastperiodrevealgreatervariancebetweenMETandMAVmedianforecast
error.ThismaypotentiallystemfromMOStendenciestoweighclimatologically
normaltemperaturesmoreheavilyinpredictionsfartheroutfromtheforecast
period(StephenKeighton–NWSFOBlacksburg,Personalcommunication).
MAVforecastsbetween2010and2015consistentlypredictedtemperatures
higherthanMETforecasts,mostnotablyinforecastcyclesofgreaterleadtime;this
maystemfrommanymorewarm-seasonCADcasesthancool-seasonin2010,and
-10
-8
-6
-4
-2
0
2
4
6
8
10
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
MAV-METMedianDifferential(°F)
Year
MAV-METAnnualMedianHighTemperatureErrorDifferentialsbyForecastCycle
-12Hours
-24Hours
-36Hours
-48Hours
-60Hours
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lesserresolutioninGFS-basedMAVguidancemayhaveledtoahighproportionof
weakerhybridandin-situcasestoprogressundetectedwhilehigherresolutionsin
NAM-basedMETguidancemayhavedetectedtheseeventsandforecasted
accordingly.Additionally,onMarch3rd,2010,theGFS-basedMOSforecastequations
underwentupdatesto“produceimprovedforecastguidanceatshort-rangeand
long-rangeprojectionsfromthe0Zand12Zmodelrunsofdaytimemaximumand
nighttimeminimumtemperature”(NOAA–NCEPCentralOperations,2018).It
shouldbenotedthattheseupdatesweremadeforthecontiguousUnitedStates,and
whilethesechangesinnear-andlong-termforecastsmayhaveimprovedforecast
accuracyacrossmuchofthecountry,theseupdatescouldhavepotentially
introducednewproblemstoMAVguidanceduringAppalachianCAD.Similar
updatesweremadeonApril1st,2015,inwhichtheGFS-basedwarmandcool
seasonequationswererefreshed(NOAA–NCEPCentralOperations,2018).This
equationupdatecoincideswithashiftinguidancebias,inwhichtheMETbegan
consistentlypredictingtemperatureslowerthantheMAV.Thelatterhalfofthe
studyperiodlacksanoverallpatternofrelativemodelperformancebasedonMAV
andMETmedianerrordifferentials.
Overall,bothguidancestatisticspoorlyforecasttheproblematicCADevents
toasimilardegree,indicatingthatneithermodelshouldbeusedpreferentiallyover
theotherwhenformulatingCADforecasts.Thisalsosuggeststhatincorporating
bothformsofguidancewhenproducingforecastsmayprovidemoreuseful
informationwhenconsideringMOSguidancetemperaturebias.
4.6HourlySurfaceComposites
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Hourlysurfaceatmosphericcompositesofobservedairtemperature,relative
humidity,winddirection,andcloudceilingheightareplottedusingallproblematic
CADcasesateachTAFsiteregardlessofCADclassification.Surfaceconditionsat
eachofthesixTAFsitesareplottedforeachdatewithinthebustdatabase
regardlessofwhichspecificsitesamongthesixresultedinbustedforecasts;datafor
anyonesitemaybeskewedifaCADwedgepartiallyerodedorwasnotstrong
enoughtoencompassallsites.Anomalousconditionsmaysimplyresultfromalack
ofpresenceofCADataspecificsiteratherthanforecasterror,asmanysites
includedwithinthecompositeswerelikelywell-forecastdespiteothersiteswithin
theCWAbeingflaggedasaforecastbustduringthatspecificCADscenario. Cloud
ceilingheightsarenotreportedover3,657meters(12,000feet),sodespitethe
possibilityofhighercloudsbeingpresentatthetimeofhourlyobservations,only
recordedvaluesbelowthisheightareconsideredincompositecalculations.Wind
speedsarenotshownbutremainbetween3and11knotsthroughoutthe24-hour
periodinbothseasons,denotingweakflowduringtheseCADepisodes.The
compositesaredividedintowarmseason(MaythroughOctober)andcoldseason
(NovemberthroughApril)andprovidevisualinsightastowhetherthemean
surfaceatmosphereduringtheproblematicCADeventsisunusualrelativetowhat
istypicalforaCADevent.
4.6.1WarmSeasonHourlySurfaceComposites
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Ofthe37CADeventswithbustedforecaststhatoccurredduringthewarm
season(MaytoOctober)between2007and2016,thediurnaltemperaturerange
didnotexceed10°FacrossallTAFsites(Figure4.17).
Figure4.17.Hourlysurfacecompositesofairtemperature(°F)duringproblematicwarmseasoncold-airdammingeventsateachofthesixTAFsites(Blacksburg(BCB),Bluefield(BLF),Danville(DAN),Lewisburg(LWB),Lynchburg(LYH),andRoanoke(ROA)).Forreference,theclimatologicalnormaltemperaturebetweenMayandOctoberfortheten-yearperiodatBlacksburgisplottedasadashedline.ClimatologicalminimumandmaximumtemperaturesatBlacksburgrange18°Fover
thecourseof24hours,whereasmostTAFsitediurnaltemperaturesremainwithin
an8°Frange.Lewisburgisanexceptionandremainscoldestatnightbyseveral
degrees,butstayswithina10°Fdiurnalrange.Thecompresseddiurnaltemperature
trendduringproblematicCADeventsshowsanarrowerrangethanclimatologically
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normalatBlacksburgfortheregioncoupledwithdepresseddaytimetemperatures
roughly10°FlowerrelativetonormalatBlacksburgthanatmostsites,typicalof
CAD.Danville’srelativelyhightemperaturesmaybearesultofitssouthernlocation,
orthesite’stendencytobeontheoutsideofthewedgeboundary–morenotably
duringsummermonthswhenthecolddomeisoftenweaker(RobertStonefield–
NWSFOBlacksburg,Personalcommunication).
WarmseasoncompositerelativehumidityvaluesforproblematicCADevents
reflectthenormaldiurnalpattern,butconservedrelativetotherangein
climatologicalvaluesforBlacksburg(Figure4.18).
Figure4.18.AsasFigure4.17,exceptforrelativehumidity(%).
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RelativehumiditylevelsateachTAFsiteareslightlylowerrelativetoBlacksburg’s
climatologicallynormalvaluesatnight.Theoppositeoccursduringtheday,
indicatingamoistatmospherethatisnearsaturation,asischaracteristicofCAD.
RelativelylowerhumidityvaluesatBluefieldcouldpossiblybeattributedtoerosion
processesensuingatthissite,reducinghumidityvaluesasapotentialresultof
risingtemperaturesanddryairentrainingintotheareaasthewedgeerodes
(RobertStonefield–NWSFOBlacksburg,Personalcommunication).Tothispoint,
themajorityofsitesshowadominanteasterlycomponenttowinddirectionduring
problematicCADevents,withanexceptionatBluefield,whereconsistent
southwesterlyflowisevident(Figure4.19).
Figure4.19.AsinFigure4.17,exceptforwinddirection(degrees).
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DanvilleandLynchburgexperiencenortheasterlyflow,oftentimesasaresultofa
northeasterlycross-barrierjetthatfunnelsdryairintotheBlueRidge(Robert
Stonefield–NWSFOBlacksburg,Personalcommunication)typicalofCADscenarios.
Lewisburgexperiencesastrongdirectionaldiurnalwindshift,indicativeof
anabatic/katabaticflowwithintheGreenbrierValley.Thisistypicalwhen
particularlycoldairispresent(JimHudgins–NWSFOBlacksburg,Personal
communication).Bluefieldexhibitspredominantlysouthwesterlysurfacewinds;
however,only9ofthe37warmseasonCADbustsoccurredatthissitewith3
transpiringduringerosionprocesses.WindsatBluefieldmaywellhavebeenfrom
thetypicaleasterlydirectionduringthenineCADbusts,withthemean
southwesterlywindreflectingconditionsduringtheother28CADbustswithinthe
CWAwhenBluefieldmayhavebeenoutsidethewedge.
Thoughcloudceilingheightsarevariablethroughouttheday,warmseason
cloudceilingsdescendbybetween15and20kilometersovernightandriseback
duringtheday,hoveringonaveragearound30to35kmagl.Fairlysteadyceilings
areproducedbyanarrowdiurnaltemperaturerangethatresultsinlimitedchange
inatmosphericthicknessthroughouttheday(Figure4.20).
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Figure4.20.AsinFigure4.17,exceptforcloudceilingheights(kmagl). AtmosphericconditionsduringwarmseasonCADeventsthatproduced
bustedforecastsappearconventional,thoughrelativelyhighertemperaturesand
lowerrelativehumidityvaluesatBluefieldsuggesttheseproblematiccasesare
weakscenariosthatdonotinfluenceallsixTAFsitesintheCWA.Atypicalwind
directionatBluefieldagainsuggeststhistobethecase.Thoughmodelsstruggle
withsummerwedgesetupsingeneralandoverestimatewedgestrengthduringthe
warmseason,thesecasesmayhavebeenparticularlyweak,resultinginacute
conditionsthatMAVandMETguidancefailedtohandlesuccessfully.
4.6.2ColdSeasonHourlySurfaceComposites
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Hourlycompositesgeneratedfromthe73casesofproblematicCADduring
thecoldseason(NovembertoApril)between2007and2016exhibitadiurnal
temperaturerangeof11°ForlessacrossallTAFsites(Figure4.21),lessthanthe
climatologicallynormalrangeatBlacksburgof14°F.
Figure4.21.Hourlysurfacecompositesofairtemperature(°F)duringproblematiccoldseasoncold-airdammingeventsateachofthesixTAFsites(Blacksburg(BCB),Bluefield(BLF),Danville(DAN),Lewisburg(LWB),Lynchburg(LYH),andRoanoke(ROA)).Forreference,theclimatologicalnormaltemperaturebetweenNovemberandAprilfortheten-yearperiodatBlacksburgisshownasadashedline.OvernighttemperaturesattheTAFsitesremainhigherrelativetonormalvaluesfor
Blacksburgbybetween4°Fand9°F,likelyasaproductofincreasedcloudcoverand
humidity,whiledaytimetemperaturesstaynearBlacksburg’sclimatologicalaverage
atmostTAFsiteswithanexceptionofhighertemperaturesatDanville.Bluefield
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andDanvilleareoftenthelasttwositesintheCWAtoexperiencetheonsetofan
evolvingwedgeofcoldairandthefirsttoseeiterode;bothremainwarmerthanthe
othersites,onceagainsuggestingthetwolocationsarenotalwayswithinthecold
domebasedonwedgestrength.
Muchlikewarmseasonrelativehumidity,valuesduringcoldseasonCAD
bustsincreaseovernightandfallduringtheday(Figure4.22),followingthetypical
diurnalpatternbutinaconservedway.
Figure4.22.AsinFigure4.21,exceptforrelativehumidity(%).Onceagain,relativehumiditylevelsateachTAFsitearesimilartoclimatologically
normalvaluesatnightandremainhigherduringtheday,reflectingamoist,near-
saturatedatmospherecharacteristicofAppalachianCAD.
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CoolseasonwinddirectionatthesixTAFsitesisnearlyidenticaltowind
directionduringthewarmseasonwithmostexhibitingadominanteasterly
component(Figure4.23).
Figure4.23.AsinFigure4.21,exceptforwinddirection(degrees).Aswiththewarmseason,Bluefieldischaracterizedbyacompositesouthwesterly
windflow,inwhich27ofthe73coldseasonCADbustsoccurredatthissitewith11
transpiringduringerosion.WinddirectionatBluefieldmaypotentiallybeskewed
bythehighfrequencyoferosioncasesinthecoldseason,wherebyBluefielderodes
outofthewedgebeforeotherlocations,andbyweakCADoccurrencesoverthe
regionthatmaynotencompassBluefield. WindsatLewisburgdepictan
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anabatic/katabaticflowregime,asinsummer,indicativeofacolddomeinplaceat
thatsite.
Coldseasoncloudceilingsslowlydropbybetween10and15kilometersin
themorningandriseagainatnight,remainingfairlysteadyaround25kmaglon
averageoverthe24-hourperiod(Figure4.24).
Figure4.24.AsinFigure4.21,exceptforcloudceilingheights(kmagl).Coldseasonceilingsarelowerthanduringthewarmseason,resultingfroma
shallowerboundarylayerduringcoldermonths.Likethewarmseason,limited
ceilingheightchangereflectsthenarrowrangeofdiurnaltemperaturestypicalof
CADcaseswhilelowertemperaturescreateathinneratmospheredrawingcloud
celilingsclosertotheground.
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Asforthewarmseason,meansurfaceatmosphericconditionsduringcold
seasonCADeventscharacterizedbybustedforecastsappearconventional,with
relativelylowtemperatureandhighhumidityvaluescoupledwithlowceilingsand
easterlywindsatmostTAFsites.WindsatBluefieldflowsouthwesterly,suggesting
theseproblematiccasesareweakerscenariosthatdonotencompassallsixsitesin
theCWA.WeakerCADsituationsarenotablyproblematicforMOSguidance,and
mayposedifficultiescausingforecasterror.
4.7SynopticComposites Thelarge-scaleatmosphericcharacteristicsoftheproblematicCADevents
areexaminedduringbothwarmandcoldseasonscenariosusingcompositesofthe
synopticatmosphere.Compositedvariablesincludemeansealevelpressure,air
temperature,relativehumidity,anddewpointtemperature(notshown)atthe
surface;geopotentialheights,airtemperature,andwindspeedanddirectionatthe
925and850millibarpressurelevels;andgeopotentialheightatthe500millibar
pressurelevel(notshown).Compositesfromthedaysinwhichwarmseasonclassic
andcoldseasonin-situCADforecastsbustedarenotshownduetothelimited
numberofdateswithineachseason.Examiningthenuanceswithinsynoptic
compositesofthedifferenttypesofproblematicCADcasesmayprovideinsightasto
whythesecasesweredifficulttosuccessfullyforecast.
Next,synopticcompositesofproblematicclassicCADarethenusedasa
standard,astheyaregenerallythepurestCADscenariossynoptically,togenerate
differencecompositesbetweenclassicandotherwedgeclassifications.By
subtractingatmosphericcompositevaluesofaspecificvariableforeach
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classificationfromclassiccomposites,disparitiesbetweenthetwoscenariosare
highlightedinsubsequentdifferencemapsofsurfacetemperature(K)andsealevel
pressure(mb).
Lastly,compositesfordays1through3inadvanceofpoorlyforecastCAD
days(-24hoursto-72hours)aregeneratedtoassesswhethertheseevents
initializedrathersuddenly,leavingmodelslittletimetopredicttheapproaching
colddome,orifmodelsgenerallyhadsufficienttimetoanticipatetheapproaching
wedgeepisodes.
4.7.1SynopticCompositesofCold-airDammingClassifications
Forthe14daysofcoldseasonclassicCADonwhichforecastsbusted,the
compositesealevelpressurefieldrevealswedgingalongtheAppalachians(Figure
4.25a),asastrong(1032millibar)parentanticyclonepositionedinthenearly-
saturated(Figure4.25c)centralNortheastdrivescoldairsouthwardalongthe
easternsideofthemountains(Figure4.25b).
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Figure4.25a-c.Synopticcompositesfromthe14daysofcoldseasonclassicCADthatresultedinbustedforecasts.Shownare(a)sealevelpressure(Pa),(b)surfaceairtemperature(K),and(c)2-meterrelativehumidity(%).Atthe925millibarpressurelevel,windsveereasterly(Figure4.26e)andthe
evidenceofwedgingweakensalongthemountains(Figure4.26a,Figure4.26c).
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Figure4.26a-f.Synopticcompositesfromthe14daysofcoldseasonclassicCADthatresultedinbustedforecasts.Shownare(a)925mband(b)850mbgeopotentialheight(m),(c)925mband(d)850mbairtemperature(K),and(e)925mband(f)850mbvectorwinds(m/s).Thewedgesignaturenearlydisappearsatthe850millibarlevel(Figure4.26b,
Figure4.26d)andsoutherlygeostrophicwindsdominateatmosphericflow(Figure
4.26f).Zonalflowat500millibars(notshown)furtherhighlightstheshallownature
ofthisphenomenon.
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Compositesforthe12hybridCADdaysonwhichforecastsbustedduringthe
warmseasonrevealasimilarpatterntothatofproblematicclassicCAD,showing
weakerpressure(Figure4.27a)andtemperature(Figure4.27b)gradientsbut
similarpositioning.
Figure4.27a-c.Asin4.25a-f,exceptforthe12daysofproblematicwarmseasonhybridCADcases.Aweaker(1021millibar)anticycloneatthesurfaceisassociatedwithless
temperaturewedgingalongthemountains.Higherrelativehumidity(Figure4.27c)
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nearthesurfacedenotesmoresaturatedconditionsthanclasicsetups.Pressureand
temperaturewedgingatthe925millibarlevellessenalongtheeasternslopesofthe
mountains(Figure4.28a,Figure4.28c)amongeasterlywindflow(Figure4.28e).
Thewedgesignatureremainsat850millibarsandisevidentinweakwedging
(Figure4.28b,Figure4.28d)despitesoutherlywindsovertheregion(Figure4.28f).
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Figure4.28a-f.AsinFigure4.26a-f,exceptforthe12problematicwarmseasonhybridCADscenarios. Synopticcompositesforthe6daysofproblematiccoldseasonhybridCAD
revealasimilarsurfacepressurepatterntothatofclassicbusts,thoughtheparent
highpressurecenteroverthenortheasternUnitedStatesbranchesoutintoeastern
Canada(Figure4.29a).
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Figure4.29a-c.Asin4.25a-f,exceptforthe7daysofproblematiccoldseasonhybridCADcases.Apartfromabroadenedanticyclone,temperaturewedging(Figure4.29b)and
relativehumidityvalues(Figure4.29c)atthesurfaceofcoldseasonhybridbusts
remainconsistenttothatofwarmseasonhybridCADbustcompositesandare
similartocoldseasonclassicproblematicCADcomposites.
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Figure4.30a-c.Synopticcompositesfromthe14daysofcoldseasonclassicCADthatresultedinbustedforecasts.Shownare(a)925mbgeopotentialheight(m),(b)925mbairtemperature(K),and(c)925mbvectorwinds(m/s).ThewedgesignatureofcoldseasonhybridCADbustsdissolvesby925millibarsasa
pressureridge(Figure4.30a)overthenortheastdirectssoutheasterlywindflow
overthecentralAppalachians(Figure4.30c).Temperaturewedgingappearsto
diminishat925millibars(Figure4.30b),suggestingthehybridCADscenarios
duringthecoldseasoninwhichforecastersstruggledweremarginalcasesofthe
phenomenon.
Synopticcompositesforthe7daysofproblematicin-situCADduringthe
warmseasonrevealabroaderparentanticyclone(Figure4.31a)positionedlower
overthemid-Atlanticwithacentralstrengthaveraging1024millibars.
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Figure4.31a-c.Asin4.25a-c,exceptforthe7daysofproblematicwarmseasonin-situCADscenarios.SurfacetemperaturewedgingisevidentfromPennsylvaniasouthwardthrough
westernNorthCarolina(Figure4.31c).Relativehumidityvalues(Figure4.31c)are
thehighestamongthethreeCADclassifications,suggestingthehighestsaturation
levelsamongCADclassesarefoundinin-situscenarios.Southeasterlywindsdictate
flowat925millibars(Figure4.32c),asdammingsignaturesatthisleveldissipate
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(Figure4.32a,Figure4.32b);thisreflectstheextremelyshallownatureofthe
problematicin-situcases.
Figure4.32a-c.AsinFigure4.30a-c,exceptforthe7warmseasonin-situproblematicCADscenarios.4.7.2SynopticCompositesofCold-airDammingOnset
Synopticcompositesgeneratedfromthe11daysonwhichbustedcold
seasonforecastswereassociatedwithCADonsetlackstrongwedgingalongthe
AppalachianMountains(Figure4.33a,Figure4.33b),implyingthesecomposites
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occurearly-onintheonsetprocess.Furthermore,highpressureatthesurfaceis
centeredmuchfarthertothewestthantheaboveclassifications,confirmingthatthe
daysclassifiedasonsetoccurduringtheinitializationofCAD.Weakevidenceof
shallowpressurewedginginitializingatthesurfacesuggestnortherlysurfacewinds
havenotyetbeguntodamagainstthemountains.
Figure4.33a-c.Synopticcompositesfromthe11daysofcoldseasonCADonsetthatresultedinbustedforecastsduringthecoldseason.Shownare(a)sealevelpressure(Pa),(b)surfaceairtemperature(K),and(c)2-meterrelativehumidity(%).
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Apressureridgeformsovertheregionat925millibars(Figure4.34a)while
temperaturewedgingalongtheeasternAppalachianslopesisnotyetevident
(Figure4.34b),andclockwiserotatingwindsatthe925(Figure4.34c)and850
millibarlevels(notshown)reinforcethepresenceofananticyclonepropogating
towardsthenortheast.
Figure4.34a-c.Synopticcompositesfromthe11daysofCADonsetthatresultedinbustedforecastsduringthecoldseason.Shownare(a)925mbgeopotentialheight(m),(b)925mbairtemperature(K),and(c)925mbvectorwinds(m/s).
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Synopticcompositesfromthe7daysofCADonsetthatresultedinbustedforecasts
inthewarmseason,althoughnotshown,exhibitmoreofthesamecharacteristics
typicalofCADonset.
4.7.3SynopticCompositesofCold-airDammingErosion
Surfacecompositesgeneratedfromthe40daysonwhichbustedforecasts
wereassociatedwiththeerosionofCADduringthecoldseasonshowastrong
pressuregradientaheadofanapproachinglowpressurecenter(Figure4.35a).This
advectionpattern(Figure4.35b)verifiesCADerosionduringthesedays.
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Figure4.35a-c.Synopticcompositesfromthe40daysofcoldseasonCADerosionthatresultedinbustedforecasts.Shownare(a)sealevelpressure(Pa),(b)surfaceairtemperature(K),and(c)2-meterrelativehumidity(%).Relativelystrongsoutherlyflowdictatedbyanortheast-southwestpressure
gradient(Figure4.36a,Figure4.36c)lacksevidenceofCADat925millibars;
however,aweakresidualcoldpoolpersistsalongthemountainsatthislevel(Figure
4.36c)yetdissolvesby850millibars(notshown).
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Figure4.36a-c.Synopticcompositesfromthe40daysofcoldseasonCADerosionthatresultedinbustedforecasts.Shownare(a)925mbgeopotentialheight(m),(b)925mbairtemperature(K),and(c)925mbvectorwinds(m/s).Thissuggestsacommontop-downerosionpatternduringthebustedCADevents.
Synopticcompositesfromthe7daysofCADerosionthatresultedinbusted
forecastsinthewarmseason,althoughagainnotshown,exhibitmoreofthesame
characteristicstypicalofCADerosion.
4.7.4Summary
Thecompositeswithineachclassificationareconsistentwithsynopticset-
upsdescribedinpreviousliteratureincludingEllisetal.(2017),Baileyetal.(2003),
andBellandBosart(1988),withoutanymajordifferences.Meanwhile,synoptic
compositesoftheonsetofproblematicCADreflectthosestudiedbyBaileyetal.
(2003)andBellandBosart(1988),andtheerosioncompositesinthisstudyare
consistentwithcold-frontalpassageandnorthwesternlowerosionmechanisms
studiedbyStanton(2003).Overall,whilethecompositesseemtoconfirmthe
assignedCADtype(classic,hybrid,in-situ,onset,erosion),theylackobviouscluesas
towhytheseproblematicCADeventswouldhavebeenparticularlydifficultto
forecastatasynopticscale.
4.7.5SynopticDifferenceCompositesBasedonClassicScenarios DifferencesinsurfaceairtemperatureforeachCADclassificationrelativeto
classicCADindicatewarmerconditionsacrosstheCWA,suggestingclassicCADas
thestrongestwedgetypeamongthepopulationofproblematicCADforecast
scenarios.
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Figure4.37a-e.Synopticdifferencecompositesofsurfaceairtemperature(K)for(b)hybrid,(c)in-situ,(d)onset,and(e)erosionscenariosbasedonthatof(a)problematicclassiccold-airdammingevents.Onaverage,hybridcasesareconsidereablywarmer(11K)acrosstheCWAthan
classicCADcases(Figure4.37b),whilein-situcasesarealsomuchwarmer(8K)
(Figure4.37c).Thisimpliesthesecasesweremuchweakerthantheirclassic
counterparts.
DifferencesinsealevelpressurecompositesrevealthatclassicCADcases
havemuchstrongerparentanticycloniccenters(byatleast10millibars)overthe
northeastUnitedStatesthandotheotherclassifications(Figure4.38b,Figure
4.38c).
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Figure4.38a-e.AsinFigure4.37a-f,exceptformeansealevelpressure(mb).Largesealevelpressuredifferencesassociatedwithonsetanderosioncomposites
revealawedgeeitherhasnotfullyinitializedintheBlueRidgeorhaserodedfrom
theregionatthetimeofthecomposites(Figure4.38d,Figure4.38e).Muchweaker
hybridandin-situpatternssuggestmorelocalizedimpactsthataffectedthesemis-
forecastedcasesasopposedtobroad-scaleatmosphericflow.Additionally,in-situ
casesappeartobereinforcedbyalowpressuresystemtothewestthatisnot
presentintheotherclassifications.Whilemodelsmayhaveanticipatedstronger
pressurepatternsinhybridandin-situcases,asizeabledifferenceinsurface
pressuresuggestsmodelsmayhavealsoentirelyundetectedweakeventsand
forecasttemperaturesaccordingly.
4.7.6SynopticCompositesofDaysBeforeCold-airDammingEvents
Surfacepressurecompositesofdays1through3beforeeachoftheCAD
classificationswitherredforecasts(notshown)revealthewedgebeginstoformin
theBlueRidgeonaverage2to3daysbeforeeachforecastperiod,providingmodels
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withsufficienttimetodetectawedgescenario.Alackofsuddenonsetpairedwith
nosignificantdifferentiatingfeaturesfromCADscenariosthroughoutliterature
suggeststhesebustedcaseswerenotparticularlyproblematicatasynopticscale,
andlocalizedfactorsmayhaveplayedalargerroleintheseerredforecasts.
4.8SoundingComposites Compositeverticalatmosphericprofilesofobservedairanddewpoint
temperaturesandwindprofilesduringwarm-season(MaytoOctober)andcold-
season(NovembertoApril)bustedCADeventsattheBlacksburgNWSFOupperair
site(KRNK)areplottedtohelpidentifyanyatmosphericcharacteristicsatypicalof
CAD.Profilesnotshown,includingcoldseasonin-situandwarmseasonclassicCAD
busts,didnothavesufficientsamplesizestoproducemeaningfulresultsintheform
ofsynopticcomposites.Duetotheshallownatureofthewedge,a25-meter
resolutionwasusedinthesecompositestoobservethelowestlevelsofthe
atmosphereatahighresolutionthatstillyieldsareasonablen-sizeonobservations
usedtocreateeachcompositevalue.Sincemaximumdailytemperaturesgenerally
fallbetween12Zand0Z,upperairsoundingslaunchedatbothofthesetimeperiods
areusedforerredmaximumtemperatureforecastcomposites;thesamegoesfor
minimumtemperatureforecastscommonlyfallingbetween0Zand12Z.These
upperairballoonlaunchesareperformedat654metersabovesealevel,exhibiting
asurfaceatmosphericpressuregenerallylessthan950millibarswherethe
compositesbegin.Thisanalysisobservesconditionsupto500millibars,asCADis
confinedtotheloweratmosphere.
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Thecompositeairanddewpointtemperaturesofthe14problematicclassic
CADscenariosduringthecoldseasonarethenplottedagainstthe71coldseason
classicdammingcasesbetween2007and2016identifiedbyEllisetal.(2017)
throughsynopticweathertyping(SSC)analysis.SincetheseparticularCADdays
identifiedbyEllisetal.arenotintheNWSFOBlacksburgbustdatabase,the
argumentcanbemadethattheseeventswerewellforecast.Anyduplicatedates
betweenthebustedCADeventsanddatesidentifiedbyEllisetal.(2017)inwhicha
wedgewaspresentattheBlacksburgobservationsitearewithdrawnfrom
calculationsfortheSSC-identified,orwellforecasted,composites.Thoughonly14
bustedclassicscenariosareused,thisanalysisaimstodiagnosepossiblereasonsas
towhyMAVandMETexhibitcertainbiasesbycomparingthedifferencesbetween
bustedscenariosandanavailabledatabaseofseeminglywell-forecastcentral
AppalachianCADcases.
VerticalatmosphericcompositesofbothwarmandcoldseasonCADforecast
bustsassociatedwithonsethighlightdryairatthesurfacebeingadvectedintothe
BueRidgebynortheasterlysurfacewinds(4.39a).Southwesterlyflowcapsanearly
isobarictemperatureprofilebetween875hPaand850hPainwarmseasononset,
andthiswarmcappingflowbecomeswesterlyby725hPa.Coldseasoncomposites
ofonsetbustsrevealaslightsubsidenceinversionbetween900hPaand875hPa,
indicativeofcoldairbeginningtopoolalongsidetheeasternslopesofthe
Appalachians.
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Figure4.39a-b.Soundingcompositesofactualatmosphericconditionsduringbustedcold-airdammingforecastsassociatedwith(a)onsetand(b)erosion,2007to2016.AsfortheCADerosionbustcomposites,anear-saturatedloweratmosphereinboth
seasons,butmoresoduringsummermonths,iscappedbywesterlywarmair
advectionoverthewedge(Figure4.39b).Thissignaturealsoindicatesmixingaloft,
typicalofanencroachingcoldfront.Thesescenariosarecharacteristicofboththe
onsetanddemiseofcentralAppalachianCAD.
Verticaltemperatureprofilesofproblematicwarmseasonhybridandin-situ
CADeventsshowmoreofwhatistypicalforthisphenomenon(Figure4.40).
Figure4.40.Soundingcompositesofactualatmosphericconditionsduringwarmseasonhybridandin-situbustedcold-airdammingforecasts,2007to2016.
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Hybridandin-situcompositesrevealsimilarmoistlayersatthesurface,andin-situ
bustsexperienceaweaksubsidenceinversionat925hPa.Hybridtemperatures
remainnearlyisothermalbetween900hPaand825hPa,suggestingtheseCAD
caseswererathermarginal.Alternately,asthesamplesizeforwarmseasonhybrid
compositesonlyincludes12cases,anysurfaceinversionsmayhavebeenmasked
withinthecomposites.In-situprofilesremainslightlywarmerthanthatofhybrid
CAD,indicatingthesecasesmaybeweakerthanhybridbustscenarios.Westerly
cappingflowisapparentacrosstheboard,thoughin-situwinddirectiondisplays
strongersoutherlycomponentsatthelowestlevelsoftheatmosphere.
Coldseasonclassiceventsarecolderanddrierthanthatofcoldseason
hybridcasesandexhibitastrongerandmoreconsistentsubsidenceinversion
(Figure4.41).
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Figure4.41.Soundingcompositesofactualatmosphericconditionsduringcoldseasonclassicandhybridbustedcold-airdammingforecasts,2007to2016.Compositesrevealaverymoist,near-saturatedsurfacelayerincoldseasonhybrid
eventsandastrongsubsidenceinversioninclassicCADcases.Hybridcasesexhibit
anerratictemperatureprofilewithseveralinversionsthatmayresultfromalimited
samplesizeof6cases.WesterlycappingflowabovetheCADwedgeisevidentin
bothcoldseasonclasses,thoughclassicCADbustsexperienceslightlystrongerwind
shearintheatmosphere’slowestlevels.Bothwarmandcoldseasoncomposites
reflectconditionsstandardofBlueRidgeCADepisodes,lackinganystrongevidence
astowhatsettheseproblematiccasesapartfromwell-forecastCADevents.
4.8.1SoundingComparisonofBustedClassic&SSC-IdentifiedWell-ForecastClassic
Cold-airDammingEvents
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VerticalatmosphericprofilesofbustedclassicCADcasesrevealconditionsto
beslightlycolderandmoderatelydrierthanthatofwell-forecastedwedge
scenarios,whilecompositewindspeedanddirectionofthetwoarenearlyidentical
(Figure4.42),indicatingasimilardegreeofcoldairadvectionforbothscenarios.
Figure4.42.Atmosphericsoundingcompositesofactualconditionsduringcoldseason(NovembertoApril)bustedclassiccold-airdammingcasesversuswell-forecastedclassiccasesatBlacksburg(KRNK)to500mb,2007to2016.Pairedwithdepressedrelativehumidityvaluesincomparisontowell-forecast
cases,classicbustcompositesalsoconsistentlyexhibitlowermixingratiovalues
(notshown)thanopposingSSC-identifiedcases,indicativeofadrierlower
atmosphereduringtheseforecastbusts.Adrieratmosphereduringcold-season
problematicclassiccasessuggestsmodelsstrugglewithdrierwedgescenarios,
potentiallyoriginatingfrommodeldifficultiesinparameterizingatmospheric
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moistureduringthesesetupsandpredictingatmosphericconditionstobemore
moistthaninactuality.InsufficientmodelingofAtlanticmoisturefetchwhencold
airisadvectedfromtheNortheastmayperhapscausethismodelerror,whilean
overestimationofdiabaticprocessesinwedgeinitiationandmaintenancemayalso
providesomesourceofinaccuracy.However,withoutacomprehensivecomparison
ofCADscenariosthatdidnotbustintheCWAbetween2007and2016,no
conclusionscanbedrawn.SincethecasesidentifiedbyEllisetal.(2017)detectthe
purest,mostobviousinstancesofCAD(i.e.,classicCAD),thewell-forecastwedgesin
thiscomparisonmaysimplybestrongerandmoremoistbynature.Thedriernature
ofclassicCADbustsallignswithMAVandMETtemperatureerrors,condusiveofa
warmbiasduringthedayinmaximumtemperatureforecastsandacoldbiasat
nightinminimumtemperatureforecasts.
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Chapter5.Conclusions
5.1Summary
Cold-airdammingisaprevalentMid-Atlanticweatherphenomenonthat
occurswhencold,denseairisorographicallyblocked,ordammed,alongsidethe
easternslopesofthecentralAppalachianMountains.Lower-than-normalmaximum
andhigher-than-normalminimumtemperatures,increasedandprolongedcloud
cover,andprecipitationthatproduceshazardousimpactsarecommonfeaturesof
thisweatherevent,whicharewellknownforpresentingdifficultiestobothhuman
forecastersandweatherpredictionmodels.TheBlueRidgeMountainsregionhas
notbeenthefocusofpreviouslypublishedCADresearch,andnostudyhas
specificallyexaminedtheforecastingaccuracyofMOSguidanceduringdifferent
CADscenariosorwedgeonsetanderosion.
MeteorologistsinthecentralAppalachianMountainsoftenfacechallenges
whenformulatingCADforecasts.Limitedsurfaceobservationsandpoormodel
guidancecombinedwithdifficultiesinpredictingthetimingofonsetanderosion
givethisshallowsynoptic-to-mesoscalephenomenonaproblematicreputation
amongforecasters.Numericalweatherpredictionmodelsdonotalways
parameterizeCADaccurately,andMOSguidanceisnotoriousforinconsistent
forecastswhenawedgeenterstheBlueRidge.TheNWSFOinBlacksburg,Virginia
archivesespeciallydifficult-to-forecastCADcasesusingan8°Fthresholdforerred
temperatureforecasts.ThisCADbustdatabasereflectstheforecastchallengethis
weathersituationpresents,andusingthisdatabasetoassesshowMOSMAVand
METguidancehavehandledtheseforecastsinwhichoperationalmeteorologists
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struggledshedslightonthebiasesthatoccurintheirparentmodels.Thisstudy
focusedonbiasesinproblematicMAVandMETminimumandmaximumair
temperatureforecasts,howtheychangedbetween2007and2016,andwhythey
mighthaveoccurred.Thisstudyalsostipulateshowforecastersmightusethisbias
informationoperationallyduringcentralAppalachianCAD.
TheCADcasesintheBlacksburgNWSFObustdatabasewerefairlyevenly
distributedannually,suggestingnosystematicchangesinforecastdifficultyduring
thestudyperiod.MonthlyfrequencyofbustedCADforecastsischaracterizedbya
peakinthespringanddeclineduringsummermonths,consistentwithprevious
researchconductedbyBellandBosart(1988)thatindicatedagreaterfrequencyof
springtimeCADcases.Brokenintoclassifications,erosionaccountedforahigh
proportionoftotalcases(47of110),reinforcingthedifficultnatureofforecasting
thedemiseofCAD.Frequenciesofbustedforecastsassociatedwithclassic,hybrid,
in-situ,andonsettypesofCADacrosstheten-yearperiodremainedrelatively
similarthroughtimewithamajorityofthesecasesfallingwithincold-season
months.Threeoutoffourbustedforecastsoccurredduetoerroneoushigh
temperatureforecastsduringthedaybetween12Zand0Z,presentinghigh
temperaturesasagreaterforecastchallengethannighttimelows,especiallyduring
CADerosion.Thenumberofforecastbustsremainedspatiallyconsistentacrossall
sixTAFsites,lackingacorrelationbetweenwedgetypeandTAFsitedistribution.
MostbustedforecastswithinaCADeventinvolvedonlyoneofthesixTAFsitesin
theCWA,suggestingthatthecomplexterrainoftheregionproduceslocalized
inconsistenciesthatofferchallengesforbothforecastersandmodels.
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Acrosstheboard,themostaccurateforecaststendedtobeproduced12
hoursaheadoftheforecastperiod.Thismaybeaproductofmorerecentsurface
observationsenhancingforecastaccuracy,orapossibleresultofMOStendenciesto
weighclimatologicallynormaltemperaturesmoreheavilyduringearlierforecast
cycleswithgreaterleadtime.Warmbiaseswereevidentinclassic,hybrid,in-situ,
andonsetmaximumtemperatureforecasts,aswellasforminimumtemperature
forecastsassociatedwithCADerosion.Alternately,coldbiaseswereexhibitedin
classic,in-situ,andonsetminimumtemperatureforecasts,butmaximum
temperatureforecastsassociatedwithCADerosion.MOStendenciesto
underestimateorevenfailtodetectCADscenariosinthecentralAppalachiansare
reflectedinprevalentwarmhigh-temperatureandcoldlow-temperatureforecast
biases.Meanwhile,MOSmayhavetendedtooverpredictthelifetimeofthecold
domeduringerosionscenariosreflectedincool-biasedmaximumandwarm-biased
minimumtemperatureforecasts.METsolutionswereslightlymoreaccurateforall
classificationsexcepterosion,inwhichMAVpredictionsoutperformedMET
forecasts.ThiscouldpossiblystemfromMOS-derivedGFSandNAMtendenciesto
underestimatewedgestrength,inwhichGFS-MOSguidanceseemstoparticularly
strugglewith.Thoughneithermodelguidanceperformedstatisticallybetterthan
theother,forecastersmaywanttoconsiderthesenuanceswhenformulating
forecastsusingMOSinput.
BiasesacrossthesixTAFsiteswerehighlyvariable,notablyduetothe
region’sdiversetopographicalfeatures.Warmminimumandmaximum
temperaturebiasesatBlacksburgmaybeanoutcomeoflimitedverticalresolution
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inMOSparameterization,underestimatingwedgestrengthathighersites.Thisis
alsoreflectedinthedifferenceinlowtemperaturebiasesbetweenBlacksburgand
Roanoke,stressingthepossibilityoflackingverticalproficiencyinmodel
parameterization.Warmmaximumandcoldminimumtemperaturebiasesat
Danville,Lewisburg,Lynchburg,andRoanokereflectapossibleover-exaggerationof
diurnaltemperaturechangesinMOSpredictions,implyingthatMAVandMET
solutionsforecastclimatologicallynormalconditionsdespiteapresentCADset-up.
AffectedbyfrequentcloudcoverandamplemoisturefromtheGreenbrierRiverthat
sustainsovernightwarmth,thecoldminimumtemperaturebiasatLewisburgmay
beenhancedbyMOSparentmodelterrainsmoothingduringonsetandincorrect
assumptionsthatcoldairencompassestheregionuniformlyacrosstherough
terrain.DuringCADerosion,higherelevationslikeBluefieldandLewisburg
exhibitedaconsistentcoldbiaswhilelowersitestrendedwarm,highlighting
potentialerrorsinhandlingmountainousterrain.MOSsolutionswererather
inconsistentduringCADerosion,indicatingthattheGFSandNAMtendto
prematurelyerodethewedgeandpredictittopersistnearlyequally.Again,despite
smallsamplesizes,theseTAFsitebiasesproposelocation-basednuancesto
considerwhenproducingforecastsduringcentralAppalachianCADscenarios.
Acrossthe10-yearstudyperiod,bothMAVandMETguidancefollowthe
samebiastrendswhenforecastingtheseproblematicCADcases.Neithermodel
performedsignificantlybetterthantheother,suggestingneithermodelshouldbe
favoredovertheotherandbothMETandMAVsolutionsshouldbeincorporated
intofutureCADforecasts.
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InattemptstodiagnosewhythesebiasesmaybepresentinMAVandMET
bustedforecasts,atmosphericcompositesduringtheseCADcasesweregeneratedat
thesurfaceusinghourlydata,significantsynopticlevels,andverticallythrough
archivedupperairsoundings.Whilesynopticsoundingsshowedlittleevidenceof
characteristicsdissimilartothatofthetypicalnatureofAppalachianCAD,hourly
surfacecompositesindicatedthatBluefieldandDanvillewerecommonlyoutsideof
thecolddomeboundary.Synopticdifferencecompositesbasedonclassictypesalso
denotethatmanyofthesecaseswereveryweakintermsofsurfacepressureand
temperature.Thecommonalityofweakwedgescenariosamongtheproblematic
CADcasesmayhaveledtheseepisodestoprogresspoorlydetectedbytheNAMand
GFS,resultinginlargemodelandhumanforecasterror.
ThoughseasonalverticalatmosphericcompositesofCADclassifications
failedtorevealdifferencesfromwhatmightbeexpectedfromacold-airwedge,
comparisonsofcold-seasonclassicbuststowell-forecastedcasesidentifiedbyEllis
etal.(2017)implythatproblematiccasesaredrierwedgescenarios.Thispointstoa
potentialmoistureparameterizationissueinMOSequationsthatfailtoproperly
predictatmosphericmoisturecontentduringmoredryCADsetups,possibly
stemmingfrompoorlymodeledAtlanticmoisturefetch.However,thismayalso
resultfromthenatureofSSC-identifiedCADeventsasbeingparticularlymoist,and
theseresultsareinconclusiveastoexplicitlyidentifyingaproblemwithinMOS
forecastequations.However,sincetheseCADforecastbustslookliketypicalcentral
Appalachianwedgescenarios,theseforecastproblemsarelikelynotmadefrom
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humanerrorandperhapsstemfromwithinMAVandMETguidanceforecast
equations.
5.2LimitationsandFutureWork
WithoutacomprehensivedatabaseofCADeventsthatoccurredinthe
BlacksburgCWAbetween2007and2016,includingwell-forecastCAD,definitive
comparisonscannotbemadetodeterminewhatspecificallycausedtheseerred
forecastsandtemperaturebiases.Furthermore,asmalln-sizeofcasesingeneralis
furthernarrowedwhenbrokenintoclassifications,causingalackofmeaningful
resultsincertaincalculations.CADisahighlysubjectiveevent,anditsdefinition
maydifferbetweenforecasterswhoarchivetheseeventsorevenbetweendifferent
sourcesofliterature.Lastly,theverticalsoundinganalysiswaslimitedbyalackof
modelforecastsoundingdatainauser-friendlyformat,whichwouldhaveelevated
thisstudy’sfindingsastherewerefew‘well-forecasted’CADcasestousefor
comparison.
ThoughtheCAD‘bust’databasearchivedbytheNWSFOBlacksburgisagreat
startingpointforresearchingthebiasesinMAVandMETtemperatureforecasts
duringcentralAppalachianCADepisodes,thecompilationofadatabaseofallCAD
casesthatoccurredbetween2007and2016wouldprovidemoremeaningful
results.Afurtherinvestigationofmodelsuccessinforecastingprecipitationandsky
covermayenhancefutureresultsastheseatmosphericvariablesheavilyinfluence
temperature,andtheuseofmodelsoundingswouldprovidemoreinsighton
characteristicsofupperairsoundingcompositesduringthesebustedcases.
Investigatingthebiasesofmoremodelswouldhelpforecastersunderstandavariety
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ofnumericalweatherpredictiontoolstousewhenincorporatingthemintowedge
forecasts,andagriddedresolutionoftemperatureversussite-specificdatamay
providemoredetailtodepictlocalizedidiosyncrasieswithintheCWA.Thisresearch
presentsasoliddatabaseofpoorlyforecastedCADscenarios,andtherearemany
differentpossibilitiesastowheretotakefurtherresearchtoimproveforecasts
duringthisphenomenon.Mostnotably,theunderlyingequationsofMAVandMET
maximumandminimumforecastsnecessitatefurtherresearchtoassesswhyMOS
exhibitscertainbiasesduringCADepisodesinthecentralAppalachianMountains.
Despitealackofconclusivesourcesofforecasterrorduringtheproblematic
CADcasesinthisstudy,numerouschangesmayhaveprovedusefultobetter
understandtheconundrumthatisAppalachianCADforecasting.Thecompilationof
adatabaseofwell-forecastCADcasesacrossthestudyareabetween2007and2016
alongsidetheverificationofbustedforecastswouldprovideasolidfoundationin
whichtomakemeaningfulcomparisonsbetweendatasetsthatcouldpotentially
identifymorelocalizedsourcesofforecasterror.Furthermore,theavailabledataset
ofCADbustsuseshumanforecaststodetermineerredCADforecasts,whereasthis
studyfocusesonmodel-drivenforecasterror.ThoughinmanycasestheMAVand
METforecastswereoutperformedbyhumanpredictions,thiswasnotalwaysthe
case.ThebestapproachtoexposemodelforecasterrorduringAppalachianCAD
wouldfocuspurelyonMAVandMETerrorsof8°Formoreratherthanrelyingon
officialNWSforecasterrortoflagaforecastbust.Additionally,focusingpurelyon
theforecastaccuracyduringtheerosionofproblematicCADeventswouldprove
highlyimportant,asreflectedinthehighproportionoferosioneventsinthisstudy;
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thiswouldalsoreducethenumberofdatasetswithlimitedsamplesizes.Inaddition,
ifgivenmoretimetomanipulatearchivedforecastmodelsoundingsintoauser-
friendlyformat,theanalysisofGFSandNAMforecastsoundingsateachofthesix
TAFsitescouldprovideusefulinformationintothesubtletiesofCADforecasting
acrosstheCWA’scomplexterrain.
5.3FinalThoughts
ForecastingCADinthecentralAppalachianMountainsisnosimpletask,as
demonstratedbythecreationandupkeepofawedgeforecast‘bust’databasewithin
theNWSFOBlacksburg.MOSguidancegenerallyhandlesthesesituationspoorly,but
animprovedunderstandingofMAVandMETtemperatureforecastbiasesmayaid
inproducingmoreaccurateforecastsduringthisphenomenon.Asageneralrule
basedonthisstudy,it’ssafetoundercutMOS-derivedmaximumtemperaturesby
severaldegreesandraiseguidance-suggestedminimumtemperaturesacrossnearly
allCADtypes.Erosion,ontheotherhand,necessitatestheopposite.ThoughMET
solutionsareslightlymoreaccuratethanMAV,it’sbesttoconsiderbothmodels
whenformulatingtemperatureforecasts.Higherresolutionmodelsmayprovide
moreaccurateinterpretationsofCAD,andconsideringelevationandtopographyis
essentialwhenformulatingforecasts,ashigherelevationsmayskewtowardscolder
MOSbiasesdependingonlocalgeography.Thoughthenuancedresultsinthisstudy
donotidentifyadefinitivesolutiontotheconundrumofCADforecasterrors,they
shouldstillproveusefulwhenforecastingCADeventsintheBlacksburgCWAas
theymayhelptoimprovewedgeforecastsbasedonMOSguidance.
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Ultimately,theCADeventsarchivedintothebustdatabasebyoperational
meteorologistsattheNWSFOBlacksburgappearedtobenormalcasesofthe
phenomenonlackingclearqualitiesdistinguishingthemfromtypicalcases.Errors
withintheseCADforecastsarenotnecessarilyformedbyunusualatmospheric
circumstances,butseemtoresultfromproblemswithintheMOSforecastequations
themselvesthatguideforecasterstoproduceerroneousforecasts.MAVandMET
solutionsmayhavedifficultypredictinglevelsofatmosphericmoistureduringdry
CADscenarios,thoughthisisunsupportedbyacomparabledatabaseofwell-
forecastedwedgepatterns.Furthermore,thecasesinthebustdatabasearefurther
complicatedbyfactorssuchasskycover,precipitation,andevaporativecoolingthat
addelementsofdifficultytothesetemperatureforecasts.Thecomplexityofthese
casesextendsbeyondsimpletemperatureforecaststhatmaybeseverelylimitedby
NAMandGFSverticalresolutions.Forecastererrorislikelynotthesourceofthe
problem,andmoreresearchonhowmodelequationsparameterizeAppalachian
CADisnecessarytodiagnosewhytheseairtemperaturebiasesoccur.
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