ensembleBMA (version 3.0-5)

cdf: 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 dates. Missing values (indicated by NA) are allowed. \ This need not be the data used for the model fit, al

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 originat

...

Included for generic function compatibility.

Value

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

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.

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, May 2008.

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

See Also

ensembleBMA, fitBMA, quantileForecast

Examples

data(slpTest)

  memberLabels <- c("AVN","GEM","ETA","NGM","NOGAPS")

  slpTestData <- ensembleData(forecasts = slpTest[ ,memberLabels],
                         observations = slpTest$obs, dates = slpTest$date)

  slpTestFit <- ensembleBMAnormal(slpTestData,
                   trainingRule = list(length=30, lag = 2))

  slpTestForc <- quantileForecast( slpTestFit, slpTestData)
  range(slpTestForc)

  slpTestCDF <- cdf( slpTestFit, slpTestData, 
                        values = seq(from=1005, to=1025, by = 5))