ensembleBMA (version 5.1.8)

trainingData: Extract Training Data

Description

Extracts a subset of an ensembleData object corresponding to a given date and number of training days.

Usage

trainingData( ensembleData, trainingDays, date)

Value

An ensembleData object corresponding to the training data for the given date relative to ensembleData.

Arguments

ensembleData

An ensembleData object that includes, ensemble forecasts, observations and dates.

trainingDays

An integer specifying the number of days in the training period.

date

The date for which the training data is desired.

Details

The most recent days are used for training regardless of whether or not they are consecutive.

References

A. E. Raftery, T. Gneiting, F. Balabdaoui and M. Polakowski, Using Bayesian model averaging to calibrate forecast ensembles, Monthly Weather Review 133:1155-1174, 2005.

J. M. Sloughter, A. E. Raftery, T. Gneiting and C. Fraley, Probabilistic quantitative precipitation forecasting using Bayesian model averaging, Monthly Weather Review 135:3309--3320, 2007.

C. Fraley, A. E. Raftery, T. Gneiting and J. M. Sloughter, ensembleBMA: An R Package for Probabilistic Forecasting using Ensembles and Bayesian Model Averaging, Technical Report No. 516R, Department of Statistics, University of Washington, 2007 (revised 2010).

C. Fraley, A. E. Raftery, T. Gneiting, Calibrating Multi-Model Forecast Ensembles with Exchangeable and Missing Members using Bayesian Model Averaging, Monthly Weather Review 138:190--202, 2010.

J. M. Sloughter, T. Gneiting and A. E. Raftery, Probabilistic wind speed forecasting using ensembles and Bayesian model averaging, Journal of the American Statistical Association, 105:25--35, 2010.

See Also

ensembleBMA, fitBMA

Examples

  data(ensBMAtest)

  ensNames <- c("gfs","cmcg","eta","gasp","jma","ngps","tcwb","ukmo")

  obs <- paste("T2","obs", sep = ".")
  ens <- paste("T2", ensNames, sep = ".")


  tempTestData <- ensembleData( forecasts = ensBMAtest[,ens],
                                observations = ensBMAtest[,obs],
                                station = ensBMAtest[,"station"],
                                dates = ensBMAtest[,"vdate"],
                                forecastHour = 48,
                                initializationTime = "00")

  tempTrain <- trainingData( tempTestData, trainingDays = 30,
                             date  = "2008010100")
 
  tempTrainFit <- fitBMAnormal( tempTrain)