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, dates = NULL, 
                   trainingRule = list(length = 30, lag = 2), 
                   control = controlBMAgamma0(), warmStart = FALSE, 
                   exchangeable = NULL, popData = NULL)

Arguments

ensembleData

An ensembleData object including ensemble forecasts, observations and dates of precipitation.

dates

The dates for which forecasts are desired. By default, this will include all dates consistent with the training rule.

trainingRule

A list giving the length and lag for the training period. The default is to use a 30 time step training period for a forecast 2 time steps ahead of the last time step in the training period.

control

A list of control values for the fitting functions. The defaults are given by the function controlBMAgamma0.

warmStart

A logical variable indicating whether or not estimation of models for a sequence of dates or time steps should be initialized with the weights from the previous date or time step. The default is for the initialization to be independen

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

popData

Optional predictors for the logistic regression for probability of zero precipitation. This option applies to the mixture of gammas model with a point mass at zero that is used for precipitation. In this model, the default predictors a

Value

A list with the following output components:
dateTableThe table of observations corresponding to the dates in ensembleData in chronological order.
trainingRuleThe training rule specified as input.
prob0coefsThe fitted coefficients in the model for the point mass at 0 (probability of zero precipitaion) for each member of the ensemble at each date.
biasCoefsThe fitted coefficients in the model for the mean of the gamma components for each member of the ensemble at each date (bias correction).
varCoefsThe 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.
weightsThe fitted BMA weights for the gamma components for each ensemble member at each date.
transformationThe function corresponding to the transformation of the data used 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.
inverseTransformationThe function corresponding to the inverse of transformation. Used for quantile forecasts and verification. This is input as part of control.

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: cdfBMA, quantileForecastBMA, bmaModelParameters, brierScore, 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. 516, Department of Statistics, University of Washington, August 2007.

Examples

Run this code

data(prcpTest)

  labels <- c("CENT","AVN","CMCG","ETA","GASP","JMA","NGPS","TCWB","UKMO")

  prcpTestData <- ensembleData( forecasts = prcpTest[ ,labels],
                          dates = prcpTest$date, observations = prcpTest$obs)

\dontrun{
  prcpTestFit <- ensembleBMA(prcpTestData, model = "gamma0")
}
  prcpTestFit <- ensembleBMAgamma0(prcpTestData)

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