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| hailfinder {bnlearn} | R Documentation |
The HailFinder weather forecast system (synthetic) data set
Description
Hailfinder is a Bayesian network designed to forecast severe summer hail in northeastern Colorado.
Usage
data(hailfinder)
Format
The hailfinder data set contains the following 56 variables:
N07muVerMo(10.7mu vertical motion): a four-level factor with levelsStrongUp,WeakUp,NeutralandDown.SubjVertMo(subjective judgment of vertical motion): a four-level factor with levelsStrongUp,WeakUp,NeutralandDown.QGVertMotion(quasigeostrophic vertical motion): a four-level factor with levelsStrongUp,WeakUp,NeutralandDown.CombVerMo(combined vertical motion): a four-level factor with levelsStrongUp,WeakUp,NeutralandDown.AreaMesoALS(area of meso-alpha): a four-level factor with levelsStrongUp,WeakUp,NeutralandDown.SatContMoist(satellite contribution to moisture): a four-level factor with levelsVeryWet,Wet,NeutralandDry.RaoContMoist(reading at the forecast center for moisture): a four-level factor with levelsVeryWet,Wet,NeutralandDry.CombMoisture(combined moisture): a four-level factor with levelsVeryWet,Wet,NeutralandDry.AreaMoDryAir(area of moisture and adry air): a four-level factor with levelsVeryWet,Wet,NeutralandDry.VISCloudCov(visible cloud cover): a three-level factor with levelsCloudy,PCandClear.IRCloudCover(infrared cloud cover): a three-level factor with levelsCloudy,PCandClear.CombClouds(combined cloud cover): a three-level factor with levelsCloudy,PCandClear.CldShadeOth(cloud shading, other): a three-level factor with levelsCloudy,PCandClear.AMInstabMt(AM instability in the mountains): a three-level factor with levelsNone,WeakandStrong.InsInMt(instability in the mountains): a three-level factor with levelsNone,WeakandStrong.WndHodograph(wind hodograph): a four-level factor with levelsDCVZFavor,StrongWest,WesterlyandOther.OutflowFrMt(outflow from mountains): a three-level factor with levelsNone,WeakandStrong.MorningBound(morning boundaries): a three-level factor with levelsNone,WeakandStrong.Boundaries(boundaries): a three-level factor with levelsNone,WeakandStrong.CldShadeConv(cloud shading, convection): a three-level factor with levelsNone,SomeandMarked.CompPlFcst(composite plains forecast): a three-level factor with levelsIncCapDecIns,LittleChangeandDecCapIncIns.CapChange(capping change): a three-level factor with levelsDecreasing,LittleChangeandIncreasing.LoLevMoistAd(low-level moisture advection): a four-level factor with levelsStrongPos,WeakPos,NeutralandNegative.InsChange(instability change): three-level factor with levelsDecreasing,LittleChangeandIncreasing.MountainFcst(mountains (region 1) forecast): a three-level factor with levelsXNIL,SIGandSVR.Date(date): a six-level factor with levelsMay15_Jun14,Jun15_Jul1,Jul2_Jul15,Jul16_Aug10,Aug11_Aug20andAug20_Sep15.Scenario(scenario): an eleven-level factor with levelsA,B,C,D,E,F,G,H,I,JandK.ScenRelAMCIN(scenario relevant to AM convective inhibition): a two-level factor with levelsABandCThruK.MorningCIN(morning convective inhibition): a four-level factor with levelsNone,PartInhibit,StiflingandTotalInhibit.AMCINInScen(AM convective inhibition in scenario): a three-level factor with levelsLessThanAve,AverageandMoreThanAve.CapInScen(capping withing scenario): a three-level factor with levelsLessThanAve,AverageandMoreThanAve.ScenRelAMIns(scenario relevant to AM instability): a six-level factor with levelsABI,CDEJ,F,G,HandK.LIfr12ZDENSd(LI from 12Z DEN sounding): a four-level factor with levelsLIGt0,N1GtLIGt_4,N5GtLIGt_8andLILt_8.AMDewptCalPl(AM dewpoint calculations, plains): a three-level factor with levelsInstability,NeutralandStability.AMInsWliScen(AM instability within scenario): a three-level factor with levelsLessUnstable,AverageandMoreUnstable.InsSclInScen(instability scaling within scenario): a three-level factor with levelsLessUnstable,AverageandMoreUnstable.ScenRel34(scenario relevant to regions 2/3/4): a five-level factor with levelsACEFK,B,D,GJandHI.LatestCIN(latest convective inhibition): a four-level factor with levelsNone,PartInhibit,StiflingandTotalInhibit.LLIW(LLIW severe weather index): a four-level factor with levelsUnfavorable,Weak,ModerateandStrong.CurPropConv(current propensity to convection): a four-level factor with levelsNone,Slight,ModerateandStrong.ScnRelPlFcst(scenario relevant to plains forecast): an eleven-level factor with levelsA,B,C,D,E,F,G,H,I,JandK.PlainsFcst(plains forecast): a three-level factor with levelsXNIL,SIGandSVR.N34StarFcst(regions 2/3/4 forecast): a three-level factor with levelsXNIL,SIGandSVR.R5Fcst(region 5 forecast): a three-level factor with levelsXNIL,SIGandSVR.Dewpoints(dewpoints): a seven-level factor with levelsLowEverywhere,LowAtStation,LowSHighN,LowNHighS,LowMtsHighPl,HighEverywher,Other.LowLLapse(low-level lapse rate): a four-level factor with levelsCloseToDryAd,Steep,ModerateOrLeandStable.MeanRH(mean relative humidity): a three-level factor with levelsVeryMoist,AverageandDry.MidLLapse(mid-level lapse rate): a three-level factor with levelsCloseToDryAd,SteepandModerateOrLe.MvmtFeatures(movement of features): a four-level factor with levelsStrongFront,MarkedUpper,OtherRapidandNoMajor.RHRatio(realtive humidity ratio): a three-level factor with levelsMoistMDryL,DryMMoistLandother.SfcWndShfDis(surface wind shifts and discontinuities): a seven-level factor with levelsDenvCyclone,E_W_N,E_W_S,MovigFtorOt,DryLine,NoneandOther.SynForcng(synoptic forcing): a five-level factor with levelsSigNegative,NegToPos,SigPositive,PosToNegandLittleChange.TempDis(temperature discontinuities): a four-level factor with levelsQStationary,Moving,None,Other.WindAloft(wind aloft): a four-level factor with levelsLV,SWQuad,NWQuad,AllElse.WindFieldMt(wind fields, mountains): a two-level factor with levelsWesterlyandLVorOther.WindFieldPln(wind fields, plains): a six-level factor with levelsLV,DenvCyclone,LongAnticyc,E_NE,SEquadandWidespdDnsl.
Note
The R script to generate data from this network is shipped in the ‘network.scripts’ directory of this package.
Source
Abramson B, Brown J, Edwards W, Murphy A, Winkler RL (1996). "Hailfinder: A Bayesian system for forecasting severe weather". International Journal of Forecasting, 12(1), 57-71.
Elidan G (2001). "Bayesian Network Repository".
http://www.cs.huji.ac.il/labs/compbio/Repository/.
Examples
# load the data and build the correct network from the model string.
data(hailfinder)
res = empty.graph(names(hailfinder))
modelstring(res) = paste("[N07muVerMo][SubjVertMo][QGVertMotion]",
"[SatContMoist][RaoContMoist][VISCloudCov][IRCloudCover][AMInstabMt]",
"[WndHodograph][MorningBound][LoLevMoistAd][Date][MorningCIN]",
"[LIfr12ZDENSd][AMDewptCalPl][LatestCIN][LLIW]",
"[CombVerMo|N07muVerMo:SubjVertMo:QGVertMotion]",
"[CombMoisture|SatContMoist:RaoContMoist]",
"[CombClouds|VISCloudCov:IRCloudCover][Scenario|Date]",
"[CurPropConv|LatestCIN:LLIW][AreaMesoALS|CombVerMo]",
"[ScenRelAMCIN|Scenario][ScenRelAMIns|Scenario][ScenRel34|Scenario]",
"[ScnRelPlFcst|Scenario][Dewpoints|Scenario][LowLLapse|Scenario]",
"[MeanRH|Scenario][MidLLapse|Scenario][MvmtFeatures|Scenario]",
"[RHRatio|Scenario][SfcWndShfDis|Scenario][SynForcng|Scenario]",
"[TempDis|Scenario][WindAloft|Scenario][WindFieldMt|Scenario]",
"[WindFieldPln|Scenario][AreaMoDryAir|AreaMesoALS:CombMoisture]",
"[AMCINInScen|ScenRelAMCIN:MorningCIN]",
"[AMInsWliScen|ScenRelAMIns:LIfr12ZDENSd:AMDewptCalPl]",
"[CldShadeOth|AreaMesoALS:AreaMoDryAir:CombClouds]",
"[InsInMt|CldShadeOth:AMInstabMt][OutflowFrMt|InsInMt:WndHodograph]",
"[CldShadeConv|InsInMt:WndHodograph][MountainFcst|InsInMt]",
"[Boundaries|WndHodograph:OutflowFrMt:MorningBound]",
"[CompPlFcst|AreaMesoALS:CldShadeOth:Boundaries:CldShadeConv]",
"[CapChange|CompPlFcst][InsChange|CompPlFcst:LoLevMoistAd]",
"[CapInScen|CapChange:AMCINInScen]",
"[InsSclInScen|InsChange:AMInsWliScen]",
"[PlainsFcst|CapInScen:InsSclInScen:CurPropConv:ScnRelPlFcst]",
"[N34StarFcst|ScenRel34:PlainsFcst][R5Fcst|MountainFcst:N34StarFcst]",
sep = "")
## Not run:
# there are too many nodes for plot(), use graphviz.plot().
graphviz.plot(res)
## End(Not run)
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