cdf {ensembleBMA}

Cummulative Distribution Function for ensemble forcasting models

Description

Computes the cumulative distribution function (CDF) of an ensemble forecasting model at observation locations.

Usage

cdf( fit, ensembleData, values, 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.

values

The vector of desired values at which the CDF of the ensemble forecasting model is to be evaluated.

dates

The dates for which the CDF will be computed. These dates must be consistent with fit and ensembleData. The default is to use all of the dates in fit. The dates are ignored if fit originates from fitBMA, which also ignores date information.

...

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.

Value

A vector of probabilities corresponding to the CDF at the desired values. Useful for determining propability of freezing, precipitation, etc.

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 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

ensembleBMA, fitBMA, quantileForecast

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)

# for quick run only; use more training days for forecasting
  tempTestFit <- ensembleBMAnormal( tempTestData[1:20,], trainingDays = 8)

  tempTestForc <- quantileForecast( tempTestFit, tempTestData)
  range(tempTestForc)

  tempTestCDF <- cdf( tempTestFit, tempTestData, 
                      values = seq(from=277, to=282, by = 1))

  tempTestCDF