quantileForecast | R Documentation |

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

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

`fit` |
A model fit to ensemble forecasting data. |

`ensembleData` |
An |

`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 |

`...` |
Included for generic function compatibility. |

This method is generic, and can be applied to any ensemble forecasting
model.

Note the model may have been applied to a power 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.

For the bivariate normal model for wind speed and direction, the
CRPS is computed for the marginal wind speed distribution.

A vector of forecasts corresponding to the desired quantiles.

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, 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*.

`ensembleBMA`

,
`fitBMA`

,
`cdf`

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) ## End(Not run) tempTestForc <- quantileForecast( tempTestFit, tempTestData) ## Not run: # R check 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") srftFit <- ensembleBMAnormal(srftData, date = "2004013100", trainingDays = 25) data(srftGrid) srftGridData <- ensembleData(forecasts = srftGrid[ ,labels], latitude = srftGrid$lat, longitude = srftGrid$lon, forecastHour = 48, initializationTime = "00") srftGridForc <- quantileForecast( srftFit, srftGridData, date = "2004013100") ## End(Not run)

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