verifPlot: Plot observations along with median, 10th and 90th percentile...

In ensembleBMA: Probabilistic Forecasting using Ensembles and Bayesian Model Averaging

Plot observations along with median, 10th and 90th percentile forecasts.

Description

Computes the median, 10th and 90th percentile forecasts, and plots the corresponding observations.

Usage

  verifPlot( fit, ensembleData, dates = NULL)

Arguments

fit	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 CDF 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.

Value

A matrix giving the median, 10th and 90th percentile forecasts for the ensemble data at the specified dates. If observations are available, they are plotted along with the forecasts in order of increasing 90th percentile forecast.

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.

T. Gneiting, F. Balabdaoui and A. Raftery, Probabilistic forecasts, calibration and sharpness. Journal of the Royal Statistical Society, Series B 69:243–268, 2007.

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 Ensemble 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

ensembleData, pit

Examples

  data(prcpFit)
  data(prcpDJdata)

  forc <- verifPlot( prcpFit, prcpDJdata, date = "20030113")

ensembleBMA documentation built on Sept. 2, 2022, 9:05 a.m.