R: The HailFinder weather forecast system (synthetic) data set

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 levels `StrongUp`, `WeakUp`, `Neutral` and `Down`.

- `SubjVertMo` (subjective judgment of vertical motion): a four-level factor with levels `StrongUp`, `WeakUp`, `Neutral` and `Down`.

- `QGVertMotion` (quasigeostrophic vertical motion): a four-level factor with levels `StrongUp`, `WeakUp`, `Neutral` and `Down`.

- `CombVerMo` (combined vertical motion): a four-level factor with levels `StrongUp`, `WeakUp`, `Neutral` and `Down`.

- `AreaMesoALS` (area of meso-alpha): a four-level factor with levels `StrongUp`, `WeakUp`, `Neutral` and `Down`.

- `SatContMoist` (satellite contribution to moisture): a four-level factor with levels `VeryWet`, `Wet`, `Neutral` and `Dry`.

- `RaoContMoist` (reading at the forecast center for moisture): a four-level factor with levels `VeryWet`, `Wet`, `Neutral` and `Dry`.

- `CombMoisture` (combined moisture): a four-level factor with levels `VeryWet`, `Wet`, `Neutral` and `Dry`.

- `AreaMoDryAir` (area of moisture and adry air): a four-level factor with levels `VeryWet`, `Wet`, `Neutral` and `Dry`.

- `VISCloudCov` (visible cloud cover): a three-level factor with levels `Cloudy`, `PC` and `Clear`.

- `IRCloudCover` (infrared cloud cover): a three-level factor with levels `Cloudy`, `PC` and `Clear`.

- `CombClouds` (combined cloud cover): a three-level factor with levels `Cloudy`, `PC` and `Clear`.

- `CldShadeOth` (cloud shading, other): a three-level factor with levels `Cloudy`, `PC` and `Clear`.

- `AMInstabMt` (AM instability in the mountains): a three-level factor with levels `None`, `Weak` and `Strong`.

- `InsInMt` (instability in the mountains): a three-level factor with levels `None`, `Weak` and `Strong`.

- `WndHodograph` (wind hodograph): a four-level factor with levels `DCVZFavor`, `StrongWest`, `Westerly` and `Other`.

- `OutflowFrMt` (outflow from mountains): a three-level factor with levels `None`, `Weak` and `Strong`.

- `MorningBound` (morning boundaries): a three-level factor with levels `None`, `Weak` and `Strong`.

- `Boundaries` (boundaries): a three-level factor with levels `None`, `Weak` and `Strong`.

- `CldShadeConv` (cloud shading, convection): a three-level factor with levels `None`, `Some` and `Marked`.

- `CompPlFcst` (composite plains forecast): a three-level factor with levels `IncCapDecIns`, `LittleChange` and `DecCapIncIns`.

- `CapChange` (capping change): a three-level factor with levels `Decreasing`, `LittleChange` and `Increasing`.

- `LoLevMoistAd` (low-level moisture advection): a four-level factor with levels `StrongPos`, `WeakPos`, `Neutral` and `Negative`.

- `InsChange` (instability change): three-level factor with levels `Decreasing`, `LittleChange` and `Increasing`.

- `MountainFcst` (mountains (region 1) forecast): a three-level factor with levels `XNIL`, `SIG` and `SVR`.

- `Date` (date): a six-level factor with levels `May15_Jun14`, `Jun15_Jul1`, `Jul2_Jul15`, `Jul16_Aug10`, `Aug11_Aug20` and `Aug20_Sep15`.

- `Scenario` (scenario): an eleven-level factor with levels `A`, `B`, `C`, `D`, `E`, `F`, `G`, `H`, `I`, `J` and `K`.

- `ScenRelAMCIN` (scenario relevant to AM convective inhibition): a two-level factor with levels `AB` and `CThruK`.

- `MorningCIN` (morning convective inhibition): a four-level factor with levels `None`, `PartInhibit`, `Stifling` and `TotalInhibit`.

- `AMCINInScen` (AM convective inhibition in scenario): a three-level factor with levels `LessThanAve`, `Average` and `MoreThanAve`.

- `CapInScen` (capping withing scenario): a three-level factor with levels `LessThanAve`, `Average` and `MoreThanAve`.

- `ScenRelAMIns` (scenario relevant to AM instability): a six-level factor with levels `ABI`, `CDEJ`, `F`, `G`, `H` and `K`.

- `LIfr12ZDENSd` (LI from 12Z DEN sounding): a four-level factor with levels `LIGt0`, `N1GtLIGt_4`, `N5GtLIGt_8` and `LILt_8`.

- `AMDewptCalPl` (AM dewpoint calculations, plains): a three-level factor with levels `Instability`, `Neutral` and `Stability`.

- `AMInsWliScen` (AM instability within scenario): a three-level factor with levels `LessUnstable`, `Average` and `MoreUnstable`.

- `InsSclInScen` (instability scaling within scenario): a three-level factor with levels `LessUnstable`, `Average` and `MoreUnstable`.

- `ScenRel34` (scenario relevant to regions 2/3/4): a five-level factor with levels `ACEFK`, `B`, `D`, `GJ` and `HI`.

- `LatestCIN` (latest convective inhibition): a four-level factor with levels `None`, `PartInhibit`, `Stifling` and `TotalInhibit`.

- `LLIW` (LLIW severe weather index): a four-level factor with levels `Unfavorable`, `Weak`, `Moderate` and `Strong`.

- `CurPropConv` (current propensity to convection): a four-level factor with levels `None`, `Slight`, `Moderate` and `Strong`.

- `ScnRelPlFcst` (scenario relevant to plains forecast): an eleven-level factor with levels `A`, `B`, `C`, `D`, `E`, `F`, `G`, `H`, `I`, `J` and `K`.

- `PlainsFcst` (plains forecast): a three-level factor with levels `XNIL`, `SIG` and `SVR`.

- `N34StarFcst` (regions 2/3/4 forecast): a three-level factor with levels `XNIL`, `SIG` and `SVR`.

- `R5Fcst` (region 5 forecast): a three-level factor with levels `XNIL`, `SIG` and `SVR`.

- `Dewpoints` (dewpoints): a seven-level factor with levels `LowEverywhere`, `LowAtStation`, `LowSHighN`, `LowNHighS`, `LowMtsHighPl`, `HighEverywher`, `Other`.

- `LowLLapse` (low-level lapse rate): a four-level factor with levels `CloseToDryAd`, `Steep`, `ModerateOrLe` and `Stable`.

- `MeanRH` (mean relative humidity): a three-level factor with levels `VeryMoist`, `Average` and `Dry`.

- `MidLLapse` (mid-level lapse rate): a three-level factor with levels `CloseToDryAd`, `Steep` and `ModerateOrLe`.

- `MvmtFeatures` (movement of features): a four-level factor with levels `StrongFront`, `MarkedUpper`, `OtherRapid` and `NoMajor`.

- `RHRatio` (realtive humidity ratio): a three-level factor with levels `MoistMDryL`, `DryMMoistL` and `other`.

- `SfcWndShfDis` (surface wind shifts and discontinuities): a seven-level factor with levels `DenvCyclone`, `E_W_N`, `E_W_S`, `MovigFtorOt`, `DryLine`, `None` and `Other`.

- `SynForcng` (synoptic forcing): a five-level factor with levels `SigNegative`, `NegToPos`, `SigPositive`, `PosToNeg` and `LittleChange`.

- `TempDis` (temperature discontinuities): a four-level factor with levels `QStationary`, `Moving`, `None`, `Other`.

- `WindAloft` (wind aloft): a four-level factor with levels `LV`, `SWQuad`, `NWQuad`, `AllElse`.

- `WindFieldMt` (wind fields, mountains): a two-level factor with levels `Westerly` and `LVorOther`.

- `WindFieldPln` (wind fields, plains): a six-level factor with levels `LV`, `DenvCyclone`, `LongAnticyc`, `E_NE`, `SEquad` and `WidespdDnsl`.

### Note

The complete BN can be downloaded from https://www.bnlearn.com/bnrepository/.

### 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.

### Examples

    # load the data.
    data(hailfinder)
    # create and plot the network structure.
    modelstring = paste0("[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][N34StarFcst|ScenRel34:PlainsFcst]",
      "[CompPlFcst|AreaMesoALS:CldShadeOth:Boundaries:CldShadeConv][CapChange|CompPlFcst]",
      "[InsChange|CompPlFcst:LoLevMoistAd][CapInScen|CapChange:AMCINInScen]",
      "[InsSclInScen|InsChange:AMInsWliScen][R5Fcst|MountainFcst:N34StarFcst]",
      "[PlainsFcst|CapInScen:InsSclInScen:CurPropConv:ScnRelPlFcst]")
    dag = model2network(modelstring)
    ## Not run: graphviz.plot(dag, shape = "ellipse")

[Package bnlearn version 4.6 Index]