verifRankHist: Verification Rank and Histogram

In ensembleBMA: Probabilistic Forecasting using Ensembles and Bayesian Model Averaging

Verification Rank and Histogram

Description

Computes the rank of verifying observations relative to the corresponding ensemble forecasts and plots its histogram.

Usage

  verifRankHist( forecasts, observations)

Arguments

forecasts	A matrix of ensemble forecasts, in which the rows corresponds to locations and times and the columns correspond to the individual ensemble members.
observations	A vector of observations corresponding to the locations and times of the forecasts.

Details

The verification rank is used to assess calibration of a forecast ensemble. A more uniform verification rank histogram indicates better calibartion.

Value

A vector giving the rank of verifying observations relative to the corresponding ensemble forecasts. The verification rank historgram is plotted.

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(srft)

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

  srftData <- ensembleData( forecasts = srft[ ,labels],
                            dates = srft$date,
                            observations = srft$obs,
                            latitude = srft$lat,
                            longitude = srft$lon,
                            forecastHour = 48,
                            initializationTime = "00")

  use <- ensembleValidDates(srftData) >= "2004013000"

  verifRankHist( ensembleForecasts(srftData[use,]),
                 dataVerifObs(srftData[use,]))

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