ensembleBMA (version 5.1.8)

ensembleBMAgamma0: BMA precipitation modeling

Description

Fits a Bayesian Model Averaging mixture of gammas with a point mass at 0 to ensemble forecasts. Intended for predicting precipitation. Allows specification of a training rule and forecasting dates.

Usage

ensembleBMAgamma0( ensembleData, trainingDays, dates = NULL, 
                   control = controlBMAgamma0(), exchangeable = NULL)

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.

prob0coefs

The fitted coefficients in the model for the point mass at 0 (probability of zero precipitaion) for each member of the ensemble at each date.

biasCoefs

The fitted coefficients in the model for the mean of the gamma components for each member of the ensemble at each date (bias correction).

varCoefs

The fitted coefficients for the model for the variance of gamma components for each date. The coefficients are the same for all members of the ensemble.

weights

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

power

A scalar value giving to the power by which the data was transformed to fit the models for the point mass at 0 and the bias model. The untransformed forecast is used to fit the variance model. This is input as part of control.

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 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 controlBMAgamma0.

exchangeable

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

Details

The output is for all of the dates in ensembleBMA, 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 ensembleBMAgamma0 objects: cdf, quantileForecast, modelParameters, brierScore, crps, CRPS and MAE.

References

J. M. Sloughter, A. E. Raftery, T. Gneiting and C. Fraley, Probabilistic quantitative precipitation forecasting using Bayesian model averaging, Monthly Weather Review 135:3209--3220, 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.

See Also

ensembleData, controlBMAgamma0, fitBMAgamma0, cdf, quantileForecast, modelParameters, brierScore, crps, MAE

Examples

  data(ensBMAtest)

  ensMemNames <- c("gfs","cmcg","eta","gasp","jma","ngps","tcwb","ukmo")
  
  obs <- paste("PCP24","obs", sep = ".")
  ens <- paste("PCP24", ensMemNames, sep = ".")

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

if (FALSE)  # R check
  prcpTestFit <- ensembleBMAgamma0( prcpTestData, trainingDays = 30)

## equivalent to
##    prcpTestFit <- ensembleBMA( prcpTestData, trainingDays = 30,
##                                model = "gamma0")


# for quick run only; use more training days for forecasting
  prcpTestFit <- ensembleBMAgamma0( prcpTestData[3:16,], trainingDays = 6)