ensembleBMA (version 5.1.8)

ensembleBMAnormal: BMA mixture of normals modeling

Description

Fits a Bayesian Model Averaging mixture of normals to ensemble forecasts. Allows specification of a training rule and forecasting dates.

Usage

ensembleBMAnormal(ensembleData, trainingDays, dates = NULL, 
                  control = controlBMAnormal(), exchangeable = NULL,
                  minCRPS = FALSE)

Value

A list with the following output components:

training

A list containing information on the training length and lag and the number of instances used for training for each modeling day.

biasCoefs

The fitted bias-correction coefficients for each ensemble member at each date.

sd

The fitted standard deviations for the mixture of normals model at each date.

weights

The fitted BMA weights for the normal components for each ensemble member at each date.

Arguments

ensembleData

An ensembleData object including ensemble forecasts with the corresponding verifying observations and their dates. Missing values (indicated by NA) are allowed.

trainingDays

An integer giving the number of time steps (e.g. days) in the training period. There is no default.

dates

The dates for which BMA forecasting models are desired. By default, this will be all dates in ensembleData for which modeling is allowed given the training rule.

control

A list of control values for the fitting functions. The defaults are given by the function controlBMAnormal.

exchangeable

A numeric or character vector or factor indicating groups of ensemble members that are exchangeable (indistinguishable). The modeling will have equal weights and parameters within each group. The default determines exchangeability from ensembleData.

minCRPS

A logical variable indicating whether or not to add a postprocessing step after the BMA fit to choose the standard deviation so as to minimize the CRPS for the training data. The default is not to do the CRPS minimization, because it can add considerable extra cost to the computation, particularly when there are many ensemble members.

Details

The output is for all of the dates in ensembleData, so there will be missing entries denoted by NA for dates that are too recent to be forecast with the training rule.
The following methods are available for ensembleBMAnormal objects: cdf, quantileForecast, modelParameters, brierScore, crps, CRPS and MAE.

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.

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.

See Also

ensembleData, controlBMAnormal, fitBMAnormal, cdf, quantileForecast, modelParameters, brierScore, crps, MAE

Examples

  data(ensBMAtest)

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

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

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

if (FALSE)  #  R check
  tempTestFit <- ensembleBMAnormal( tempTestData, trainingDays = 30)

## equivalent to
##   tempTestFit <- ensembleBMA( tempTestData, trainingDays = 30,
##                                model = "normal")


# for quick run only; use more training days for forecasting
 tempTestFit <- ensembleBMAnormal( tempTestData[1:20,], trainingDays = 8)