ensembleBMA (version 3.0-5)

quantileForecast: Quantile forecasts at observation locations

Description

Computes quantiles for the probability distribution function (PDF) for ensemble forecasting models.

Usage

quantileForecast( fit, ensembleData, quantiles = 0.5, dates=NULL, ...)

Arguments

fit

A model fit to ensemble forecasting data.

ensembleData

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

quantiles

The vector of desired quantiles for the PDF of the BMA mixture model.

dates

The dates for which the quantile forecasts will be computed. These dates must be consistent with fit and ensembleData. The default is to use all of the dates in fit. If ensembleData does n

...

Included for generic function compatibility.

Value

• A vector of forecasts corresponding to the desired quantiles.

Details

This method is generic, and can be applied to any ensemble forecasting model. Note the model may have been applied to a transformation of the data, but that information is included in the input fit, and the output is transformed appropriately. This can be used to compute prediction intervals for the PDF.

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.

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, May 2008.

C. Fraley, A. E. Raftery, T. Gneiting, BMA Forecasting with Missing and Exchangeable Ensemble Members, in preparation.

See Also

ensembleBMA, fitBMA, cdf

Examples

data(slpTest)

  labels <- c("AVN","GEM","ETA","NGM","NOGAPS")
  slpTestData <- ensembleData( forecasts = slpTest[ ,labels],
                         observations = slpTest$obs, dates = slpTest$date)

  slpTestFit <- ensembleBMAnormal(slpTestData,
                  trainingRule = list(length = 25, lag = 2))

  slpTestForc <- quantileForecast( slpTestFit, slpTestData)

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)

  srftFit <- ensembleBMAnormal(srftData, date = "2004012900",
               trainingRule = list(length = 25, lag = 2))

  data(srftGrid)

  srftGridData <- ensembleData(forecasts = srftGrid[ ,labels],
                           latitude = srftGrid$lat, longitude = srftGrid$lon)

  srftGridForc <- quantileForecast( srftFit, srftGridData, 
                     date = "2004012900")