plot.ensembleBMA {ensembleBMA}

Plot the Predictive Distribution Function for ensemble forcasting models

Description

Plots the Predictive Distribution Function (PDF) of an ensemble forecasting model.

Usage

## S3 method for class 'ensembleBMAgamma'
plot( x, ensembleData, dates=NULL, ask=TRUE, ...)
## S3 method for class 'ensembleBMAgamma0'
plot( x, ensembleData, dates=NULL, ask=TRUE, ...)
## S3 method for class 'ensembleBMAnormal'
plot( x, ensembleData, dates=NULL, ask=TRUE, ...)
## S3 method for class 'fitBMAgamma'
plot( x, ensembleData, dates=NULL, ...)
## S3 method for class 'fitBMAgamma0'
plot( x, ensembleData, dates=NULL, ...)
## S3 method for class 'fitBMAnormal'
plot( x, ensembleData, dates=NULL, ...)

Arguments

x

A model fit to ensemble forecasting data.

ensembleData

An ensembleData object that includes ensemble forecasts, verification observations and possibly dates. Missing values (indicated by NA) are allowed. \ This need not be the data used for the model fit, although it must include the same ensemble members.

dates

The dates for which the PDF will be computed. These dates must be consistent with fit and ensembleData. The default is to use all of the dates in fit. The dates are ignored if fit originates from fitBMA, which also ignores date information.

ask

A logical value indicating whether or not the user should be prompted for the next plot.

...

Included for generic function compatibility.

Details

This method is generic, and can be applied to any ensemble forecasting model.
The colored curves are the weighted PDFs of the ensemble members, and the bold curve is the overall PDF. The vertical black line represents the median forecast, and the dotted back lines represent the .1 and .9 quartiles. The vertical orange line is the verifying observation (if any).
Exchangeable members are represented in the plots by the weighted group sum rather than by the indivdual weighted PDFs of each member.

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 135:3209–3220, 2007.

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.

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.

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

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")

## Not run: # R check
  tempTestFit <- ensembleBMAnormal( tempTestData, trainingDays = 30)
  plot(tempTestFit, tempTestData)

## End(Not run)