ensembleBMA (version 2.1)

quantileForecastBMA: Quantile forecasts at observation locations

Description

Computes quantiles for the probability distribution function (PDF) for an ensemble BMA mixture model at observation locations.

Usage

quantileForecastBMA( fit, ensembleData, quantiles = 0.5, 
                     dates = NULL, popData = NULL, ...)

Arguments

fit

An ensemble BMA model fit.

ensembleData

An ensembleData object including ensemble forecasts and observations. It need not be the object used to form fit, although it must include the same ensemble members. If ensembleData includes dates,

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.

popData

For gamma0 model fits, there is an additional popData argument for providing predictors in the logistic regression for probability of zero precipitation. If popData was supplied to obtain in the modeling for

...

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 BMA 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. 516, Department of Statistics, University of Washington, August 2007.

See Also

ensembleBMA, fitBMA, cdfBMA

Examples

data(slpTest)

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

  slpTestFit <- ensembleBMAnormal(slpTestData)

  slpTestForc <- quantileForecastBMA( 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")

  data(srftGrid)

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

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