quantileForecast: Quantile forecasts at observation locations

In ensembleBMA: Probabilistic Forecasting using Ensembles and Bayesian Model Averaging

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 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.
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 not include dates, they will be inferred from fit and dates.
...	Included for generic function compatibility.

Details

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.

Value

A vector of forecasts corresponding to the desired quantiles.

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, 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

ensembleBMA, fitBMA, cdf

Examples

  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)

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