AssessmentofModelForecastTemperatureBiasDuringColdAirDammingintheCentral

AppalachianMountains

SuzannaLindeman

ThesissubmittedtothefacultyoftheVirginiaPolytechnicInstituteandStateUniversityinpartialfulfillmentoftherequirementsforthedegreeof

MasterofScienceIn

Geography

AndrewEllisStephenKeightonDavidCarroll

April2018Blacksburg,VA

Keywords:cold-airdamming,ModelOutputStatistics,temperaturebias,central

AppalachianMountains

Copyright©bySuzannaLindeman

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.

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.

iv

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.

v

TableofContentsChapter Page Abstract……………………………………………………………………………………………………..GeneralAudienceAbstract………………………………………………………………………….

iiiii

Acknowledgements……………………………………………………………………………………. ivListofFigures……………………………………………………………………………………………. viiListofTables……………………………………………………………………………………………...ListofAbbreviations…………………………………………………………………………………..

xiixiv

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………………………………………………………..

27272729

vi

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……………………………………………

95100

5.Conclusions………………………………………………………………………………………......5.1Summary…………………………………………………………………………………...5.2LimitationsandFutureWork……………………………………………………...5.4FinalThoughts…………………………………………………………………………...

References………………………………………………………………………………………………..

103103108110112

vii

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.……

35

viii

Figure4.9 Thefrequencyofproblematiccold-airdammingforecastsstratifiedbyTAFsitelocationandcold-airdammingclassification,2007to2016.………………………………………………

36

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

ix

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

x

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(%).………………………………………….

88

Figure4.36a-c

Synopticcompositesfromthe40daysofcoldseasonCADerosionthatresultedinbustedforecasts.Shownare(a)925mbgeopotentialheight(m),(b)925mbairtemperature(K),and(c)925mbvectorwinds(m/s).…………………………….

89

xi

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.…………………………….

97

Figure4.40 Soundingcompositesofactualatmosphericconditionsduringwarmseasonhybridandin-situbustedcold-airdammingforecasts,2007to2016..…………………………………….

98

Figure4.41 Soundingcompositesofactualatmosphericconditionsduringcoldseasonclassicandhybridbustedcold-airdammingforecasts,2007to2016.……………………………………...

100

Figure4.42 Atmosphericsoundingcompositesofactualconditionsduringcoldseason(NovembertoApril)bustedclassiccold-airdammingcasesversuswell-forecastedclassiccasesatBlacksburg(KRNK)to500mb,2007to2016.……………………..

101

xii

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.…………

51

xiii

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.…………………………………………………………………………………………

53

Table4.12 AsinTable4.10,exceptforerrorstatisticsattheLewisburgTAFsite.………………………………………………………………………………….

53

Table4.13 AsinTable4.10,exceptforerrorstatisticsattheRoanokeTAFsite.…………………………………………………………………………………………

54

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.…………………………………………………………………………………………

575758585858

xiv

ListofAbbreviationsInorderofappearance:CADNAM

Cold-airDammingNorthAmericanMesoscaleForecastSystem

RAP RapidRefreshModelGFS GlobalForecastSystemModelECMWF EuropeanMediumRangeWeatherForecastModelNWSFO NationalWeatherServiceForecastOfficeCWAMOS

CountyWarningAreaModelOutputStatistics

MAV GFSMOSShort-rangeTextProductMETQPFTAFNCEINCEPNARRNOAAESRLSSCMPGISLWBBLFBCBLYHROADAN

NAMMOSTextProductQuantitativePrecipitationForecastTerminalAerodromeForecastNationalCentersforEnvironmentalInformationNationalCentersforEnvironmentalPredictionNorthAmericanRegionalReanalysisNationalOceanicandAtmosphericAdministrationEarthSystemsResearchLaboratorySpatialSynopticClassificationMoistPolarGeospatialInformationSystemLewisburgBluefieldBlacksburgLynchburgRoanokeDanville

1

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.

2

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).

3

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).

4

Figure1.2.ReferencemapofgeographicalregionswithintheBlacksburgNWSFOCWA.AdaptedfromNOAA–NationalWeatherServiceForecastOfficeBlacksburg(2017). AtypicalAppalachianCADwedgewillsettleintotheBlueRidgeMountains

fromnortheastoftheBlacksburgNationalWeatherServiceForecastOffice

(NWSFO)countywarningarea(CWA)(SeeFigure1.2),poolingsouthwestward

againsttheAppalachianmountainsascoldairisadvectedfromthenortheast

(Figure1.3).

5

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

6

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

7

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

8

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?

9

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).

10

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

11

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

12

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

13

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

14

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

15

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

16

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

17

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).

18

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.

19

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).

20

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

21

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

22

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.

23

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

24

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

25

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

26

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.

27

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

28

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)

29

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

30

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

31

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

32

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

33

(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

34

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

35

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

36

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

37

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

38

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.

39

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

40

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

41

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.

42

Figure4.10a-b.Boxplotsof(a)MAVand(b)METmaximumtemperatureguidanceforecasterrorofallclassified(classic,hybrid,in-situ)problematicCADeventsbyforecastcycle(-12hoursto-60hours),2007to2016.Thetopsofthepurpleand

43

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.

44

Figure4.11a-b.AsinFigure4.11a,b,exceptfor(a)MAVand(b)METminimumtemperatureguidanceerror.

45

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.

46

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

47

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

48

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

49

temperatureforecasts,andthepoorestaccuracyforallMETlowtemperature

forecasts.TheslightwarmbiasexhibitedinMAVandMETlowtemperature

guidancesuggeststheNAMandGFSmaymorecommonlytendprematurelyerode

thewedgeatnight,possiblystemmingfrominadequatemodelingofovernightcloud

coverormoisturelevels.

50

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.

51

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

52

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

53

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

54

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

55

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

56

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

57

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

58

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

59

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

60

METforecasterrorsappearingtobeslightlymoreconservative.Thoughthe

dispersionoferrorappearstobeslightlylargerinMAVforecasts,thetwooutput

statisticsfollowthesamewarmandcoldbiaspatternsthroughoutthestudyperiod.

61

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.

62

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

64

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

65

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

66

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).

70

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;

110

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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